24 pjg gardlenstest1

DRCDP_institution_id.json

@@ -1,5 +1,9 @@
 {
     "institution_id":{
+        "NCAR":{
+            "contact":"Samantha Hartke; [email protected]",
+            "name":"National Center for Atmospheric Research, Boulder, CO, USA"
+        },
         "TTU":{
             "ROR":"0405mnx93",
             "URL":"https://www.ttu.edu/",

DRCDP_source_id.json

@@ -1,5 +1,25 @@
 {
     "source_id":{
+        "GARD-LENS-1-0":{
+            "calendar":"standard",
+            "contact":"Samantha Hartke; [email protected]",
+            "further_info_url":"https://opensky.ucar.edu/islandora/object/articles%3A42362",
+            "grid":"5 x 5 km latitude x longitude",
+            "grid_label":"gn",
+            "institution_id":"NCAR",
+            "license":"Creative Commons Attribution 4.0 International",
+            "license_id":"CC BY 4.0",
+            "license_url":"https://creativecommons.org/licenses/by/4.0/",
+            "nominal_resolution":"5 km",
+            "product":"downscaled-statistical",
+            "reference":" Hartke, Samantha. H., Andrew J. Newman, Ethan Gutmann, Rachel McCrary, Nicholas D. Lybarger, and Flavio Lehner (2024) GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties. Sci Data 11, 1374, 15 pp. https://doi.org/10.1038/s41597-024-04205-z",
+            "region":"north_america",
+            "region_id":"NAM",
+            "source":"GARD-LENS 1.0: Statistically-downscaled climate model projections based on CMIP6",
+            "source_name":"GARD-LENS",
+            "source_version":"1.0",
+            "title":"GARD-LENS 1.0 dataset prepared for DRCDP"
+        },
         "LOCA2-0":{
             "calendar":"gregorian",
             "contact":"Daniel Feldman; [email protected]",
@@ -81,4 +101,4 @@
             "title":"STAR-ESDM 1.0 dataset prepared for DRCDP"
         }
     }
-}
+}

DataPreparationExamples/GARD-LENS/GARDLENS_CMIP6_input.json

@@ -0,0 +1,10 @@
+{
+    "activity_id": "DRCDP",
+    "source_id": "GARD-LENS-1-0",
+    "driving_activity_id": "CMIP",
+    "driving_experiment_id": "historical",
+    "driving_mip_era": "CMIP6",
+    "driving_source_id": "ACCESS-CM2",
+    "driving_variant_label": "r1i1p1f1",
+    "outpath":"/global/cfs/projectdirs/m3522/project_downscale/"
+}

DataPreparationExamples/GARD-LENS/GARDLENS_CMIP6_runCMOR.py

@@ -0,0 +1,83 @@
+import cmor
+import xcdat as xc
+import xarray as xr
+import cftime
+import numpy as np
+import sys, os, glob
+from datetime import datetime
+
+# %% Get current script path, append src dir
+current_dir = os.path.dirname(os.path.abspath(__file__))
+new_path = os.path.join(current_dir, "..", "..", "src")
+sys.path.append(new_path)
+from DRCDPLib import writeUserJson
+
+### RUNNING PARALLEL VIA jobs.txt
+multi  = False 
+if multi == True:
+ vari = sys.argv[1]
+ domain = sys.argv[2]
+ modi = sys.argv[3]
+ rn = sys.argv[4]
+ outputVarName = vari
+ if vari == 'pr': outputUnits = 'kg m-2 s-1'
+ if vari == 'tasmax': outputUnits = 'K'
+ if vari == 'tasmin': outputUnits = 'K'
+
+# TEST
+vari = 'pcp'
+domain = 'CONUS'
+mod = 'ecearth3'
+rn = 'r150i1p1f1'
+outputVarName = 'pr' 
+outputUnits = 'kg m-2 s-1'
+
+# CMOR TABLES
+cmorTable = '../../Tables/DRCDP_APday.json'
+inputJson = 'GARDLENS_CMIP6_input.json'
+
+inFile = '/global/cfs/projectdirs/m2637/GARDLENS/' + vari + '/GARDLENS_' + mod + '_' + rn + '_' + vari + '_1970_2100_' + domain + '.nc'
+
+start_time = datetime.now()
+#fc = xc.open_mfdataset(inFile,decode_times=True,use_cftime=True, preprocess=extract_date)
+fc = xc.open_dataset(inFile,decode_times=False,use_cftime=False)
+fc = fc.isel(time=slice(0,100))  # test 100 time steps
+d = fc[vari]
+
+lat = fc.lat.values  
+lon = fc.lon.values  
+time = fc.time.values 
+tunits = fc.time.units 
+
+fc = fc.bounds.add_bounds("X") 
+fc = fc.bounds.add_bounds("Y")
+fc = fc.bounds.add_bounds("T")
+
+##### CMOR setup
+cmor.setup(inpath='./',netcdf_file_action=cmor.CMOR_REPLACE_4,logfile= vari + '_' + mod + '-' + rn + '-'+'cmorLog.txt')
+cmor.dataset_json(writeUserJson(inputJson, cmorTable))
+cmor.load_table(cmorTable)
+
+# SET CMIP MODEL SPECIFIC ATTRIBUTES 
+#cmor.set_cur_dataset_attribute("source_id","LOCA2--" + mod)
+cmor.set_cur_dataset_attribute("driving_source_id",mod)
+cmor.set_cur_dataset_attribute("driving_variant_label",rn)
+cmor.set_cur_dataset_attribute("driving_experiment_id",'historical')
+
+# Create CMOR axes
+cmorLat = cmor.axis("latitude", coord_vals=lat[:], cell_bounds=fc.lat_bnds.values, units="degrees_north")
+cmorLon = cmor.axis("longitude", coord_vals=lon[:], cell_bounds=fc.lon_bnds.values, units="degrees_east")
+cmorTime = cmor.axis("time", coord_vals=time[:], cell_bounds=fc.time_bnds.values[:], units= tunits)
+cmoraxes = [cmorTime,cmorLat, cmorLon]
+
+# Setup units and create variable to write using cmor - see https://cmor.llnl.gov/mydoc_cmor3_api/#cmor_set_variable_attribute
+varid   = cmor.variable(outputVarName,outputUnits,cmoraxes,missing_value=1.e20)
+values  = np.array(d[:],np.float32)
+
+cmor.set_deflate(varid,1,1,1) ; # shuffle=1,deflate=1,deflate_level=1 - Deflate options compress file data
+cmor.write(varid,values,len(time)) ; # Write variable with time axis
+cmor.close()
+fc.close()
+end_time = datetime.now()
+print('done cmorizing ',mod, rn, vari,' process time: {}'.format(end_time-start_time))
+

DataPreparationExamples/GARD-LENS/jobs.txt

@@ -0,0 +1,22 @@
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr AWI-CM-1-1-MR 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr IPSL-CM6A-LR 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr ACCESS-CM2 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr INM-CM5-0 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr KACE-1-0-G 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr MPI-ESM1-2-LR 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr NorESM2-LM 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr CanESM5 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr GFDL-ESM4 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr ACCESS-ESM1-5 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr GFDL-CM4 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr INM-CM4-8 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr EC-Earth3 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr BCC-CSM2-MR 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr MPI-ESM1-2-HR 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr TaiESM1 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr EC-Earth3-Veg 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr FGOALS-g3 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr MIROC6 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr NorESM2-MM 
+python GARDLENS_CMIP6_runCMOR.py ssp585 pr MRI-ESM2-0 
+

Tables/DRCDP_CV.json

@@ -14,6 +14,10 @@
             "gn":"data reported on the analysis-native grid"
         },
         "institution_id":{
+            "NCAR":{
+                "contact":"Samantha Hartke; [email protected]",
+                "name":"National Center for Atmospheric Research, Boulder, CO, USA"
+            },
             "TTU":{
                 "ROR":"0405mnx93",
                 "URL":"https://www.ttu.edu/",
@@ -102,6 +106,46 @@
             "variable_id"
         ],
         "source_id":{
+            "GARD-LENS-1-0":{
+                "calendar":"standard",
+                "contact":"Samantha Hartke; [email protected]",
+                "further_info_url":"https://opensky.ucar.edu/islandora/object/articles%3A42362",
+                "grid":"5 x 5 km latitude x longitude",
+                "grid_label":"gn",
+                "institution_id":"NCAR",
+                "license":"Creative Commons Attribution 4.0 International",
+                "license_id":"CC BY 4.0",
+                "license_url":"https://creativecommons.org/licenses/by/4.0/",
+                "nominal_resolution":"5 km",
+                "product":"downscaled-statistical",
+                "reference":"Hartke, S.H., Newman, A.J., Gutmann, E. et al. GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties. Sci Data 11, 1374 (2024). https://doi.org/10.1038/s41597-024-04205-z",
+                "region":"north_america",
+                "region_id":"NAM",
+                "source":"GARD-LENS-1-0: Statistically-downscaled climate model projections based on CMIP6",
+                "source_name":"GARD-LENS",
+                "source_version":"1.0",
+                "title":"GARD-LENS-1-0 dataset prepared for DRCDP"
+            },
+            "GARD-LENS1-0":{
+                "calendar":"standard",
+                "contact":"Samantha Hartke; [email protected]",
+                "further_info_url":"https://opensky.ucar.edu/islandora/object/articles%3A42362",
+                "grid":"5 x 5 km latitude x longitude",
+                "grid_label":"gn",
+                "institution_id":"NCAR",
+                "license":"Creative Commons Attribution 4.0 International",
+                "license_id":"CC BY 4.0",
+                "license_url":"https://creativecommons.org/licenses/by/4.0/",
+                "nominal_resolution":"5 km",
+                "product":"downscaled-statistical",
+                "reference":"Hartke, S.H., Newman, A.J., Gutmann, E. et al. GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties. Sci Data 11, 1374 (2024). https://doi.org/10.1038/s41597-024-04205-z",
+                "region":"north_america",
+                "region_id":"NAM",
+                "source":"GARD-LENS-1-0: Statistically-downscaled climate model projections based on CMIP6",
+                "source_name":"GARD-LENS",
+                "source_version":"1.0",
+                "title":"GARD-LENS-1-0 dataset prepared for DRCDP"
+            },
             "LOCA2-0":{
                 "calendar":"gregorian",
                 "contact":"Daniel Feldman; [email protected]",

src/createCVs.ipynb

@@ -349,7 +349,11 @@
     "] = \"Texas Tech University, 2520 Broadway Avenue, Lubbock, TX 79409, USA\"\n",
     "institution_id[\"institution_id\"][\"TTU\"][\"ROR\"] = \"0405mnx93\"\n",
     "institution_id[\"institution_id\"][\"TTU\"][\"URL\"] = \"https://www.ttu.edu/\"\n",
-    "institution_id"
+    "institution_id\n",
+    "\n",
+    "institution_id[\"institution_id\"][\"NCAR\"] = {}\n",
+    "institution_id[\"institution_id\"][\"NCAR\"][\"name\"] = \"National Center for Atmospheric Research, Boulder, CO, USA\"\n",
+    "institution_id[\"institution_id\"][\"NCAR\"][\"\"] = \"[email protected]\""
    ]
   },
   {
@@ -877,6 +881,27 @@
     "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\n",
     "    \"title\"\n",
     "] = \"STAR-ESDM 1.0 dataset prepared for DRCDP\"\n",
+    "\n",
+    "source_id[\"source_id\"][\"source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"region\"] = \"north_america\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"region_id\"] = \"NAM\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\n",
+    "    \"source\"\n",
+    "] = \"STAR-ESDM 1.0: Statistically-downscaled climate model projections based on CMIP6\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"source_name\"] = \"STAR-ESDM\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"source_version\"] = \"1.0\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\n",
+    "    \"title\"\n",
+    "] = \"STAR-ESDM 1.0 dataset prepared for DRCDP1-0\"][\"region\"] = \"north_america\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"region_id\"] = \"NAM\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\n",
+    "    \"source\"\n",
+    "] = \"STAR-ESDM 1.0: Statistically-downscaled climate model projections based on CMIP6\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"source_name\"] = \"STAR-ESDM\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\"source_version\"] = \"1.0\"\n",
+    "source_id[\"source_id\"][\"STAR-ESDM1-0\"][\n",
+    "    \"title\"\n",
+    "] = \"STAR-ESDM 1.0 dataset prepared for DRCDP\"\n",
+    "\n",
     "source_id"
    ]
   },
@@ -1056,7 +1081,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "NERSC Python",
    "language": "python",
    "name": "python3"
   },
@@ -1070,7 +1095,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

src/createCVs.py

@@ -0,0 +1,62 @@
+import json
+
+
+# read data from local (ensuring a pull has occurred to sync with main)
+f = open('../DRCDP_institution_id.json','r')
+institution_id = json.load(f)
+
+with open('../DRCDP_institution_id.json','r') as f: institution_id = json.load(f)
+with open('../DRCDP_source_id.json','r') as f: source_id = json.load(f)
+
+# add new institution_id
+key = 'NCAR'
+institution_id['institution_id'][key] = {}
+institution_id['institution_id'][key][ 'contact' ] = 'Samantha Hartke; [email protected]'
+institution_id['institution_id'][key][ 'name' ] = 'National Center for Atmospheric Research, Boulder, CO, USA'
+#institution_id['institution_id'][key]['ROR'] = '00d9ah105'
+#institution_id['institution_id'][key]['URL'] = 'https://www.climatologylab.org'
+
+# add new source_id
+key = 'GARD-LENS-1-0'
+source_id['source_id'][key] = {}
+source_id['source_id'][key]['calendar'] = 'standard'
+source_id['source_id'][key]['contact'] = 'Samantha Hartke; [email protected]'
+source_id['source_id'][key]['further_info_url'] = 'https://opensky.ucar.edu/islandora/object/articles%3A42362'
+source_id['source_id'][key]['grid'] = '5 x 5 km latitude x longitude'
+source_id['source_id'][key]['grid_label'] = 'gn'
+source_id['source_id'][key]['institution_id'] = 'NCAR'
+source_id['source_id'][key][ 'license' ] = 'Creative Commons Attribution 4.0 International'
+source_id['source_id'][key]['license_id'] = 'CC BY 4.0'
+source_id['source_id'][key][ 'license_url' ] = 'https://creativecommons.org/licenses/by/4.0/'
+source_id['source_id'][key]['nominal_resolution'] = '5 km'
+source_id['source_id'][key]['product'] = 'downscaled-statistical'
+source_id['source_id'][key]['reference'] = ' '.join(
+    ['Hartke, S.H., Newman, A.J., Gutmann, E. et al. GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties. Sci Data 11, 1374 (2024). https://doi.org/10.1038/s41597-024-04205-z' ]
+)
+source_id['source_id'][key]['region'] = 'north_america'
+source_id['source_id'][key]['region_id'] = 'NAM'
+source_id['source_id'][key][ 'source' ] = 'GARD-LENS-1-0: Statistically-downscaled climate model projections based on CMIP6'
+source_id['source_id'][key]['source_name'] = 'GARD-LENS'
+source_id['source_id'][key]['source_version'] = '1.0'
+source_id['source_id'][key]['title'] = 'GARD-LENS-1-0 dataset prepared for DRCDP'
+
+# overwrite existing files
+# write files
+outFile = ''.join(['../DRCDP_institution_id.json'])
+with open(outFile, 'w') as f:
+    json.dump(institution_id, f, ensure_ascii=True, sort_keys=True, indent=4, separators=(',', ':'))
+outFile = ''.join(['../DRCDP_source_id.json'])
+with open(outFile, 'w') as f:
+    json.dump(source_id, f, ensure_ascii=True, sort_keys=True, indent=4, separators=(',', ':'))
+
+# overwrite DRCDP_CV.json with new entries
+f = open('../Tables/DRCDP_CV.json','r')
+CV = json.load(f)
+CV['CV']['institution_id'] = institution_id['institution_id']
+CV['CV']['source_id'] = source_id['source_id']
+
+# overwrite DRCDP_CV.json
+outFile = ''.join(['../Tables/DRCDP_CV.json'])
+with open(outFile, 'w') as f:
+    json.dump(CV, f, ensure_ascii=True, sort_keys=True, indent=4, separators=(',', ':'))
+

src/pullTable.ipynb

@@ -383,7 +383,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "NERSC Python",
    "language": "python",
    "name": "python3"
   },
@@ -397,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,