diff --git a/catalog/TTU_2019_rcp45_station_data/collection.json b/catalog/TTU_2019_rcp45_station_data/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..66e6647f221c194452f0f4e608c74fb041b8bd43
--- /dev/null
+++ b/catalog/TTU_2019_rcp45_station_data/collection.json
@@ -0,0 +1,2610 @@
+{
+ "type": "Collection",
+ "id": "TTU_2019_rcp45_station_data",
+ "stac_version": "1.0.0",
+ "description": "High-Resolution Precipitation Projections for the South Central U.S.",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.json",
+ "type": "application/json"
+ }
+ ],
+ "stac_extensions": [
+ "https://stac-extensions.github.io/datacube/v2.2.0/schema.json"
+ ],
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+ "description": null,
+ "extent": [
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+ "2100-07-01T00:00:00Z"
+ ],
+ "step": "P1Y0M0DT0H0M0S"
+ },
+ "nv": {
+ "type": "count",
+ "description": null,
+ "extent": [
+ 0,
+ 1
+ ]
+ },
+ "stations": {
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+ "bbox": [
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+ 293.0080871582031,
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+ }
+ },
+ "cube:variables": {
+ "Seasonal_mean_bounds": {
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+ "bounds_time": {
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+ "type": "auxiliary",
+ "description": "station latitude",
+ "unit": "degrees_north"
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+ "stations"
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+ "type": "auxiliary",
+ "description": "station longitude",
+ "unit": "degrees_east"
+ },
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+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
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+ "time",
+ "stations"
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+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
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+ ],
+ "type": "data",
+ "description": "ACCESS1-0: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
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+ "description": "ACCESS1-3: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
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+ "type": "data",
+ "description": "CCSM4: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
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+ "description": "CMCC-CM: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
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+ "dimensions": [
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+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
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+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
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+ "type": "data",
+ "description": "HadGEM2-CC: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
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+ "stations"
+ ],
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+ "description": "IPSL-CM5A-LR: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
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+ "type": "data",
+ "description": "MPI-ESM-LR: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_inmcm4": {
+ "dimensions": [
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+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_multi_model_mean": {
+ "dimensions": [
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+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_above_the_one_in_10_wettest_years_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of years with as much or more cumulative precipitation than the 1-in-10 wettest historical year (January-December) between 1950 and 2009",
+ "unit": "1(yes)/0(no)"
+ },
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+ "dimensions": [
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+ "stations"
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+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_90th_quantile": {
+ "dimensions": [
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+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
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+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_CSIRO-Mk3-6-0": {
+ "dimensions": [
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+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
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+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_2in_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_days_above_3in_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp45_dry_growing_season_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_growing_season_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_dry_years_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_growing_season_dry_days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_growing_season_dry_days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_longest_dry_period_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp45_pr_djf_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_djf_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative winter (December-January-February) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_jja_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative summer (June-July-August) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mam_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative spring (March-April-May) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_mjjas_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative growing season (May-June-July-August-September) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_pr_son_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp45_wet_years_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wet_years_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp45_wettest_3days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_3days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp45_wettest_5days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
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+}
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diff --git a/catalog/TTU_2019_rcp85_station_data/collection.json b/catalog/TTU_2019_rcp85_station_data/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb6782d626a5c831ff2b32c1bd52a84154d06839
--- /dev/null
+++ b/catalog/TTU_2019_rcp85_station_data/collection.json
@@ -0,0 +1,2610 @@
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+ "type": "data",
+ "description": "CCSM4: days per year with more than 2 inches of precipitation in 24 hours",
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+ ],
+ "type": "data",
+ "description": "CMCC-CM: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_2in_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of days per year with more than 2 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_days_above_3in_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of days per year with more than 3 inches of precipitation in 24 hours",
+ "unit": "number of days per year"
+ },
+ "rcp85_dry_growing_season_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_growing_season_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of growing seasons with the same or less cumulative precipitation than the driest historical year (May-September) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_dry_years_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of years with the same or less cumulative precipitation than the driest historical year (January- December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_growing_season_dry_days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_growing_season_dry_days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of total number of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_longest_dry_period_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_longest_dry_period_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of longest consecutive period of dry days during the growing season (May-September)",
+ "unit": "number of days per year"
+ },
+ "rcp85_longest_dry_period_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
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+ "unit": "inches"
+ },
+ "rcp85_pr_son_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_pr_son_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of cumulative fall (September-October-November) precipitation",
+ "unit": "inches"
+ },
+ "rcp85_wet_years_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wet_years_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of years with as much or more cumulative precipitation than the wettest historical year (January-December) between 1950 and 2015",
+ "unit": "1(yes)/0(no)"
+ },
+ "rcp85_wettest_3days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_3days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of amount of precipitation in the wettest 3 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_5days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of amount of precipitation in the wettest 5 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_7days_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of amount of precipitation in the wettest 7 days of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_10th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "10th quantile of CMIP5 GCMs of amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_90th_quantile": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "90th quantile of CMIP5 GCMs of amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_ACCESS1-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-0: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_ACCESS1-3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "ACCESS1-3: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_CCSM4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CCSM4: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_CMCC-CM": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMCC-CM: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_CNRM-CM5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CNRM-CM5: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_CSIRO-Mk3-6-0": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CSIRO-Mk3-6-0: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_HadGEM2-CC": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "HadGEM2-CC: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_IPSL-CM5A-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "IPSL-CM5A-LR: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_MIROC5": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MIROC5: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_MPI-ESM-LR": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MPI-ESM-LR: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_MRI-CGCM3": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "MRI-CGCM3: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_inmcm4": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "inmcm4: amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_multi_model_mean": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "CMIP5 multi-model mean of amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "rcp85_wettest_day_observations": {
+ "dimensions": [
+ "time",
+ "stations"
+ ],
+ "type": "data",
+ "description": "Observations of amount of precipitation in the wettest 1 day of the year",
+ "unit": "inches"
+ },
+ "station_ids": {
+ "dimensions": [
+ "stations"
+ ],
+ "type": "auxiliary",
+ "description": null,
+ "unit": "GHCN_ID"
+ }
+ },
+ "extent": {
+ "spatial": {
+ "bbox": [
+ [
+ -139.6710968017578,
+ 24.55500030517578,
+ -66.99191284179693,
+ 59.51190185546875
+ ]
+ ]
+ },
+ "temporal": {
+ "interval": [
+ [
+ "1950-07-01T00:00:00Z",
+ "2100-07-01T00:00:00Z"
+ ]
+ ]
+ }
+ },
+ "license": "Unlicense",
+ "assets": {
+ "zarr-s3-osn": {
+ "href": "s3://mdmf/gdp/TTU_2019_rcp85_station_data.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "Open Storage Network Pod S3 API access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "anon": true,
+ "client_kwargs": {
+ "endpoint_url": "https://usgs.osn.mghpcc.org/"
+ }
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ },
+ "zarr-s3": {
+ "href": "s3://nhgf-development/workspace/DataConversion/TTU_2019_rcp85_station_data.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "S3 access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "requester_pays": true
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ }
+ }
+}
\ No newline at end of file
diff --git a/catalog/catalog.json b/catalog/catalog.json
index 5f5c61e1a34c4b6cf21e9266892089f3399b2fac..44a34356ccbc97873fd9e65807c1aac88947d44a 100644
--- a/catalog/catalog.json
+++ b/catalog/catalog.json
@@ -138,6 +138,16 @@
"rel": "child",
"href": "./CooperUpperDeschutes/collection.json",
"type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./TTU_2019_rcp45_station_data/collection.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./TTU_2019_rcp85_station_data/collection.json",
+ "type": "application/json"
}
]
}
\ No newline at end of file
diff --git a/workflows/archive/TTU_2019_rcp45_station_data_create_collection_from_zarr.ipynb b/workflows/archive/TTU_2019_rcp45_station_data_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f6c4fd1d85e1b0e3d1609de7f64acf37fa2f5817
--- /dev/null
+++ b/workflows/archive/TTU_2019_rcp45_station_data_create_collection_from_zarr.ipynb
@@ -0,0 +1,855 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# TTU_2019_rcp45_station_data Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'TTU_2019_rcp45_station_data'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\": {\"anon\": True, \"client_kwargs\": {\"endpoint_url\":\"https://usgs.osn.mghpcc.org/\"}}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description = \"Open Storage Network Pod S3 API access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs2 = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\":{\"requester_pays\":True}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description2 = \"S3 access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles2 = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# open and view zarr dataset\n",
+ "fs2 = fsspec.filesystem('s3', anon=True, endpoint_url='https://usgs.osn.mghpcc.org/')\n",
+ "ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n",
+ " backend_kwargs={'consolidated':True}, chunks={})\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "\n",
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected.\n",
+ "\n",
+ "**WARNING: no x, y dims in this dataset, but we will still include lat/lon as dims in our dict so we can use the same code snippets as usual to get a bbox for the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dims_auto_extract = ['X', 'Y', 'T']\n",
+ "# dim_names_dict = {}\n",
+ "# for d in dims_auto_extract:\n",
+ "# dim_names_dict[d] = stac_helpers.extract_dim(ds, d)\n",
+ "dim_names_dict = {'X': 'longitude', 'Y': 'latitude', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out first and last timestamps\n",
+ "temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\n",
+ "print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n",
+ "# create a temporal extent object\n",
+ "temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection = catalog.get_child(collection_id)\n",
+ " print(\"existing collection opened\")\n",
+ " collection.extent=collection_extent\n",
+ " collection.description=collection_description\n",
+ " collection.license=collection_license\n",
+ "else:\n",
+ " collection = pystac.Collection(id=collection_id,\n",
+ " description=collection_description,\n",
+ " extent=collection_extent,\n",
+ " license=collection_license)\n",
+ " print(\"new collection created\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "asset_id = \"zarr-s3-osn\"\n",
+ "asset = pystac.Asset(href=zarr_url,\n",
+ " description=asset_description,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles,\n",
+ " extra_fields = xarray_opendataset_kwargs)\n",
+ "collection.add_asset(asset_id, asset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "asset_id2 = \"zarr-s3\"\n",
+ "asset2 = pystac.Asset(href=zarr_url2,\n",
+ " description=asset_description2,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles2,\n",
+ " extra_fields = xarray_opendataset_kwargs2)\n",
+ "collection.add_asset(asset_id2, asset2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "time step is yearly, so I am manually constructing the step value based on this documentation of iso format: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#time_step = pd.Timedelta(get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**\n",
+ "\n",
+ "These are station data, so there is no regular spatial step"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "These are station data, so there is no regular spatial step"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the dataset\n",
+ "dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
+ " 'nv': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'nv'), 'extent': [ds.nv.min().item(), ds.nv.max().item()]}),\n",
+ " 'stations': pystac.extensions.datacube.Dimension({'type': 'geometry', 'description': stac_helpers.get_long_name(ds, 'stations'), 'bbox': spatial_bounds}),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15db7bae-e387-4377-852b-5f864c67ee69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert list(dims_dict.keys()) == dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop metpy_crs coordinate we have added\n",
+ "if 'metpy_crs' in ds.coords:\n",
+ " ds = ds.drop_vars('metpy_crs')\n",
+ "\n",
+ "# pull list of vars from dataset\n",
+ "vars = list(ds.variables)\n",
+ "\n",
+ "# spec says that the keys of cube:dimensions and cube:variables should be unique together; a key like lat should not be both a dimension and a variable.\n",
+ "# we will drop all values in dims from vars\n",
+ "vars = [v for v in vars if v not in dims]\n",
+ "\n",
+ "# Microsoft Planetary Computer includes coordinates and crs as variables here:\n",
+ "# https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# we will keep those in the var list\n",
+ "\n",
+ "# create dictionary of dataset variables and associated dimensions\n",
+ "vars_dict={}\n",
+ "for v in vars:\n",
+ " unit = stac_helpers.get_unit(ds, v)\n",
+ " var_type = stac_helpers.get_var_type(ds, v)\n",
+ " long_name = stac_helpers.get_long_name(ds, v)\n",
+ " vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n",
+ "else:\n",
+ " catalog.add_child(collection)\n",
+ " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/archive/TTU_2019_rcp85_station_data_create_collection_from_zarr.ipynb b/workflows/archive/TTU_2019_rcp85_station_data_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ac1d9963f3326d928d3ddb7b51a6ed87175e3dcb
--- /dev/null
+++ b/workflows/archive/TTU_2019_rcp85_station_data_create_collection_from_zarr.ipynb
@@ -0,0 +1,875 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# TTU_2019_rcp85_station_data Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'TTU_2019_rcp85_station_data'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf/gdp/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\": {\"anon\": True, \"client_kwargs\": {\"endpoint_url\":\"https://usgs.osn.mghpcc.org/\"}}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description = \"Open Storage Network Pod S3 API access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs2 = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\":{\"requester_pays\":True}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description2 = \"S3 access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles2 = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# open and view zarr dataset\n",
+ "fs2 = fsspec.filesystem('s3', anon=True, endpoint_url='https://usgs.osn.mghpcc.org/')\n",
+ "ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n",
+ " backend_kwargs={'consolidated':True}, chunks={})\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "\n",
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected.\n",
+ "\n",
+ "**WARNING: no x, y dims in this dataset, but we will still include lat/lon as dims in our dict so we can use the same code snippets as usual to get a bbox for the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dims_auto_extract = ['X', 'Y', 'T']\n",
+ "# dim_names_dict = {}\n",
+ "# for d in dims_auto_extract:\n",
+ "# dim_names_dict[d] = stac_helpers.extract_dim(ds, d)\n",
+ "dim_names_dict = {'X': 'longitude', 'Y': 'latitude', 'T': 'time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out first and last timestamps\n",
+ "temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\n",
+ "print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n",
+ "# create a temporal extent object\n",
+ "temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection = catalog.get_child(collection_id)\n",
+ " print(\"existing collection opened\")\n",
+ " collection.extent=collection_extent\n",
+ " collection.description=collection_description\n",
+ " collection.license=collection_license\n",
+ "else:\n",
+ " collection = pystac.Collection(id=collection_id,\n",
+ " description=collection_description,\n",
+ " extent=collection_extent,\n",
+ " license=collection_license)\n",
+ " print(\"new collection created\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "asset_id = \"zarr-s3-osn\"\n",
+ "asset = pystac.Asset(href=zarr_url,\n",
+ " description=asset_description,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles,\n",
+ " extra_fields = xarray_opendataset_kwargs)\n",
+ "collection.add_asset(asset_id, asset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "asset_id2 = \"zarr-s3\"\n",
+ "asset2 = pystac.Asset(href=zarr_url2,\n",
+ " description=asset_description2,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles2,\n",
+ " extra_fields = xarray_opendataset_kwargs2)\n",
+ "collection.add_asset(asset_id2, asset2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "time step is yearly, so I am manually constructing the step value based on this documentation of iso format: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#time_step = pd.Timedelta(get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "time_step = \"P1Y0M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 2\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**\n",
+ "\n",
+ "These are station data, so there is no regular spatial step"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "These are station data, so there is no regular spatial step"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the dataset\n",
+ "dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
+ " 'nv': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'nv'), 'extent': [ds.nv.min().item(), ds.nv.max().item()]}),\n",
+ " 'stations': pystac.extensions.datacube.Dimension({'type': 'geometry', 'description': stac_helpers.get_long_name(ds, 'stations'), 'bbox': spatial_bounds}),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "910cf68e-5d1d-43f9-baad-b8b5709dcbd7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims_dict.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74cac855-7570-42d6-a8a6-5d233bc06ad7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list(dims_dict.keys()) == dims"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3d7d8551-13e2-4ec7-9f9c-b578d9780d66",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert list(dims_dict.keys()) == dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop metpy_crs coordinate we have added\n",
+ "if 'metpy_crs' in ds.coords:\n",
+ " ds = ds.drop_vars('metpy_crs')\n",
+ "\n",
+ "# pull list of vars from dataset\n",
+ "vars = list(ds.variables)\n",
+ "\n",
+ "# spec says that the keys of cube:dimensions and cube:variables should be unique together; a key like lat should not be both a dimension and a variable.\n",
+ "# we will drop all values in dims from vars\n",
+ "vars = [v for v in vars if v not in dims]\n",
+ "\n",
+ "# Microsoft Planetary Computer includes coordinates and crs as variables here:\n",
+ "# https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# we will keep those in the var list\n",
+ "\n",
+ "# create dictionary of dataset variables and associated dimensions\n",
+ "vars_dict={}\n",
+ "for v in vars:\n",
+ " unit = stac_helpers.get_unit(ds, v)\n",
+ " var_type = stac_helpers.get_var_type(ds, v)\n",
+ " long_name = stac_helpers.get_long_name(ds, v)\n",
+ " vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n",
+ "else:\n",
+ " catalog.add_child(collection)\n",
+ " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
index 61d5f552e791cb638e3f34a5652c545e1343f04d..046544f814569f7d2f1b8bfdd888a2fd6513d038 100644
--- a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
@@ -45,46 +45,21 @@
},
{
"cell_type": "markdown",
- "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
- "metadata": {},
- "source": [
- "## Open up NHGF STAC Catalog"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
- "metadata": {},
- "outputs": [],
- "source": [
- "# define folder location where your STAC catalog json file is\n",
- "catalog_path = os.path.join('..', '..', 'catalog')\n",
- "# open catalog\n",
- "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
"metadata": {},
"source": [
- "## Collection Metadata Input"
+ "## Collection ID"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
"metadata": {},
"outputs": [],
"source": [
- "# name for STAC collection\n",
- "collection_id = 'conus404-daily'\n",
- "# description of STAC collection\n",
- "collection_description = 'CONUS404 40 years of daily values for subset of model output variables derived from hourly values on cloud storage'\n",
- "# license for dataset\n",
- "collection_license = 'CC0-1.0'"
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'conus404-daily'"
]
},
{
@@ -163,6 +138,30 @@
"ds"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = 'CONUS404 40 years of daily values for subset of model output variables derived from hourly values on cloud storage'\n",
+ "# you can consider pulling this fram dataset attributes instead of manually typing it:\n",
+ "# collection_description = ds.attrs['title']\n",
+ "\n",
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
{
"cell_type": "markdown",
"id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
@@ -407,10 +406,23 @@
},
{
"cell_type": "markdown",
- "id": "cfb71202-03df-45b5-ac2f-0dc2ee1ab780",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
"metadata": {},
+ "outputs": [],
"source": [
- "## Create pystac collection"
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
]
},
{
@@ -556,7 +568,9 @@
"id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
"metadata": {},
"source": [
- "**Time**"
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
]
},
{
@@ -567,6 +581,8 @@
"outputs": [],
"source": [
"time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
"print(f'time step: {time_step}')"
]
},
@@ -699,26 +715,6 @@
"# ds.isel(y=slice(ix-1,ix+3)).y"
]
},
- {
- "cell_type": "markdown",
- "id": "dc9c149c-c2ba-4c5f-b48b-7b1b88a0309c",
- "metadata": {},
- "source": [
- "#### extract x, y dimension lower and upper bounds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "773d2e70-9bc5-4f37-96d0-5ff980ae0c2d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# get x, y bounds for extent of those dimensions (required)\n",
- "xy_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
- "print(xy_bounds)"
- ]
- },
{
"cell_type": "markdown",
"id": "00a5e041-081d-428d-ac2e-75d16de205e6",
@@ -729,7 +725,15 @@
"\n",
"Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
"\n",
- "If you have a dimension like \"bnds\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76)."
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\n",
+ " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]})\n",
+ " }\n",
+ "```"
]
},
{
@@ -759,8 +763,8 @@
"# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
"# note that the extent of each dimension should be pulled from the dataset\n",
"dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
- " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [xy_bounds[0], xy_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
- " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [xy_bounds[1], xy_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
" 'bottom_top_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top_stag')}),\n",
" 'bottom_top': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top')}),\n",
" 'soil_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layers_stag')}),\n",
@@ -768,8 +772,18 @@
" 'y_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, 'y_stag')}),\n",
" 'snow_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snow_layers_stag')}),\n",
" 'snso_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snso_layers_stag')}),\n",
- " }\n",
- "display(dims_dict)"
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert list(dims_dict.keys()) == dims"
]
},
{
diff --git a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
index 5c91bcca4d8eb22975b2a17520b6147152844055..ecf96c9df2b4e17a5f566477c7e70ab78672b49c 100644
--- a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
@@ -49,45 +49,20 @@
},
{
"cell_type": "markdown",
- "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "id": "f8c93dbc-174c-4387-be7a-00eccf004509",
"metadata": {},
"source": [
- "## Open up NHGF STAC Catalog"
+ "## Item ID"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define folder location where your STAC catalog json file is\n",
- "catalog_path = os.path.join('..', '..', 'catalog_items')\n",
- "# open catalog\n",
- "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "id": "65b8979c-a462-4f68-8912-e82158d8811e",
"metadata": {},
- "source": [
- "## Item Metadata Input"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
- "metadata": {
- "tags": []
- },
"outputs": [],
"source": [
- "# name for STAC collection\n",
+ "# name for STAC item - should match name of zarr dataset\n",
"item_id = 'conus404-daily'"
]
},
@@ -109,7 +84,7 @@
"outputs": [],
"source": [
"# url to zarr store that you want to create a collection for\n",
- "zarr_url = 's3://hytest/conus404/conus404_daily.zarr/'\n",
+ "zarr_url = f's3://mdmf/gdp/{item_id}.zarr/'\n",
"\n",
"# define keyword arguments needed for opening the dataset with xarray\n",
"# ref: https://github.com/stac-extensions/xarray-assets\n",
@@ -416,10 +391,25 @@
},
{
"cell_type": "markdown",
- "id": "cfb71202-03df-45b5-ac2f-0dc2ee1ab780",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
"metadata": {},
"source": [
- "## Create pystac item"
+ "## Open up STAC Catalog and create an item"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog_items')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
]
},
{
@@ -565,7 +555,9 @@
"id": "18b8950f-030f-4f78-b9ac-799dc9263cb6",
"metadata": {},
"source": [
- "**Time**"
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
]
},
{
@@ -576,6 +568,8 @@
"outputs": [],
"source": [
"time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P1Y0M0DT0H0M0S\"\n",
"print(f'time step: {time_step}')"
]
},
@@ -708,26 +702,6 @@
"# ds.isel(y=slice(ix-1,ix+3)).y"
]
},
- {
- "cell_type": "markdown",
- "id": "1cf871b8-7eee-4c47-81b4-a42583dd60b0",
- "metadata": {},
- "source": [
- "#### extract x, y dimension lower and upper bounds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "00a18a29-fb9a-4b56-8009-493122997b16",
- "metadata": {},
- "outputs": [],
- "source": [
- "# get x, y bounds for extent of those dimensions (required)\n",
- "xy_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
- "print(xy_bounds)"
- ]
- },
{
"cell_type": "markdown",
"id": "00a5e041-081d-428d-ac2e-75d16de205e6",
@@ -738,7 +712,15 @@
"\n",
"Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
"\n",
- "If you have a dimension like \"bnds\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76)."
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\n",
+ " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]})\n",
+ " }\n",
+ "```"
]
},
{
@@ -768,8 +750,8 @@
"# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
"# note that the extent of each dimension should be pulled from the dataset\n",
"dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step': time_step}),\n",
- " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [xy_bounds[0], xy_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
- " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [xy_bounds[1], xy_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step, 'reference_system': projjson}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step, 'reference_system': projjson}),\n",
" 'bottom_top_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top_stag')}),\n",
" 'bottom_top': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'bottom_top')}),\n",
" 'soil_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'soil_layers_stag')}),\n",
@@ -777,8 +759,18 @@
" 'y_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, 'y_stag')}),\n",
" 'snow_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snow_layers_stag')}),\n",
" 'snso_layers_stag': pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'z', 'description': stac_helpers.get_long_name(ds, 'snso_layers_stag')}),\n",
- " }\n",
- "display(dims_dict)"
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "816ed76c-c287-4116-a507-6a5c2734e24b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert list(dims_dict.keys()) == dims"
]
},
{