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"# Compute land-atmosphere coupling indices \n",
"This notebook takes in a series of CESM simulations, computes the land-atmosphere coupling index (CI; \n",
"terrestrial leg only currently), and plots those seasonal means.
\n",
"- Note: Built to use monthly output; ideally, CI should be based on daily data. \n",
"- Optional: Comparison against FLUXNET obs\n",
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\n",
"Notebook created by mdfowler@ucar.edu; Last update: 11 Dec 2024 "
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"\n",
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"var HTML_MIME_TYPE = 'text/html';\n",
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" var div = document.createElement(\"div\");\n",
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"\n",
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" // create a DOM node to render to\n",
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"\n",
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" register_renderer(events, OutputArea);\n",
" }\n",
" } catch(err) {\n",
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],
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" const reloading = true;\n",
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" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
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" }\n",
" const element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (let i = 0; i < js_modules.length; i++) {\n",
" const url = js_modules[i];\n",
" const escaped = encodeURI(url)\n",
" if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n",
" if (!window.requirejs) {\n",
" on_load();\n",
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" }\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (const name in js_exports) {\n",
" const url = js_exports[name];\n",
" const escaped = encodeURI(url)\n",
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" on_load();\n",
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" element.async = false;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" element.textContent = `\n",
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},
"metadata": {},
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],
"source": [
"import os\n",
"import glob\n",
"import numpy as np\n",
"import xarray as xr\n",
"from datetime import timedelta\n",
"import pandas as pd\n",
"import sys\n",
"\n",
"# Plotting utils\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import cartopy.crs as ccrs\n",
"import uxarray as uxr"
]
},
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"source": [
"## 1. Modify this section for each run"
]
},
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"source": [
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"## Settings for case locations + names\n",
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"## Where observations are stored\n",
"# '/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data/lnd/analysis_datasets/ungridded/timeseries/FLUXNET2015/'\n",
"obs_data_dir = \"\"\n",
"obsDir = \"\"\n",
"\n",
"CESM_output_dir = os.path.join(\n",
" os.path.sep,\n",
" \"glade\",\n",
" \"campaign\",\n",
" \"cesm\",\n",
" \"development\",\n",
" \"cross-wg\",\n",
" \"diagnostic_framework\",\n",
" \"CESM_output_for_testing\",\n",
")\n",
"\n",
"ts_dir = None\n",
"\n",
"## Full casenames that are present in CESM_output_dir or ts_dir and in individual filenames\n",
"# caseNames = [\n",
"# 'b.e23_alpha16b.BLT1850.ne30_t232.054',\n",
"# 'b.e30_beta02.BLT1850.ne30_t232.104',\n",
"# ]\n",
"case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
"\n",
"# clmFile_h = '.h0.'\n",
"\n",
"start_date = \"0001-01-01\"\n",
"end_date = \"0101-01-01\"\n",
"\n",
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"## Optional settings for notebook\n",
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"\n",
"## If comparison against FLUXNET desired\n",
"# fluxnet_comparison = True"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"injected-parameters"
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"source": [
"# Parameters\n",
"case_name = \"b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.192\"\n",
"base_case_name = \"b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188\"\n",
"CESM_output_dir = \"/glade/derecho/scratch/hannay/archive\"\n",
"base_case_output_dir = \"/glade/derecho/scratch/gmarques/archive\"\n",
"start_date = \"0002-01-01\"\n",
"end_date = \"0021-12-01\"\n",
"base_start_date = \"0002-01-01\"\n",
"base_end_date = \"0021-12-01\"\n",
"obs_data_dir = (\n",
" \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CUPiD_obs_data\"\n",
")\n",
"ts_dir = None\n",
"lc_kwargs = {\"threads_per_worker\": 1}\n",
"serial = True\n",
"clmFile_h = \"h0\"\n",
"fluxnet_comparison = True\n",
"obsDir = \"lnd/analysis_datasets/ungridded/timeseries/FLUXNET2015/\"\n",
"subset_kwargs = {}\n",
"product = \"/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks//lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb\"\n"
]
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"source": [
"# Set some parameter defaults\n",
"if ts_dir is None:\n",
" ts_dir = CESM_output_dir"
]
},
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"source": [
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"## Settings for computing coupling index\n",
"## - - - - - - - - - - - - - - - - - - - - - -\n",
"\n",
"# Set up directory for aux output like coupling index file\n",
"if \"SCRATCH\" in os.environ:\n",
" cupid_temp = os.path.join(os.path.sep, os.environ[\"SCRATCH\"], \"CUPiD_scratch\")\n",
" os.makedirs(cupid_temp, exist_ok=True)\n",
"else:\n",
" cupid_temp = \".\"\n",
"\n",
"startYrs = [start_date.split(\"-\")[0]]\n",
"endYrs = [f\"{int(end_date.split('-')[0])-1:04d}\"]\n",
"\n",
"caseNames = [\n",
" case_name,\n",
" # base_case_name,\n",
"]\n",
"\n",
"shortNames = [case.split(\".\")[-1] for case in caseNames]"
]
},
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"source": [
"## 2. Read in model data and compute terrestrial coupling index"
]
},
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"source": [
"\"\"\"\n",
"Inputs: xname -- controlling variable \n",
" yname -- responding variable\n",
" ds -- dataset containing xname and yname data \n",
" \n",
"This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
" https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
"\"\"\"\n",
"\n",
"\n",
"def compute_couplingIndex_cesm(xname, yname, xDS, yDS):\n",
" xday = xDS[xname].groupby(\"time.season\")\n",
" yday = yDS[yname].groupby(\"time.season\")\n",
"\n",
" # Get the covariance of the two (numerator in coupling index)\n",
" covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
" \"time.season\"\n",
" ).sum() / xday.count()\n",
"\n",
" # Now compute the actual coupling index\n",
" couplingIndex = covarTerm / xday.std()\n",
"\n",
" return couplingIndex"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Soil moisture file not found!\n",
"Land-based SHFLX file not found!\n"
]
},
{
"ename": "NameError",
"evalue": "name 'shflx_DS' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 60\u001b[39m\n\u001b[32m 58\u001b[39m \u001b[38;5;66;03m# If years start at 0000, offset by 1700 years for analysis\u001b[39;00m\n\u001b[32m 59\u001b[39m yrOffset = \u001b[32m1850\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m60\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mshflx_DS\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mtime.year\u001b[39m\u001b[33m\"\u001b[39m].values[\u001b[32m0\u001b[39m] < \u001b[32m1500\u001b[39m:\n\u001b[32m 61\u001b[39m shflx_DS[\u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m] = shflx_DS.time + timedelta(days=yrOffset * \u001b[32m365\u001b[39m)\n\u001b[32m 62\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m soilWater_DS[\u001b[33m\"\u001b[39m\u001b[33mtime.year\u001b[39m\u001b[33m\"\u001b[39m].values[\u001b[32m0\u001b[39m] < \u001b[32m1500\u001b[39m:\n",
"\u001b[31mNameError\u001b[39m: name 'shflx_DS' is not defined"
]
}
],
"source": [
"for iCase in range(len(caseNames)):\n",
" ## Check first if coupling index has already been created:\n",
" TCI_filePath = os.path.join(\n",
" cupid_temp, f\"{caseNames[iCase]}_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
" )\n",
"\n",
" if os.path.exists(TCI_filePath): # Use previously computed TCI\n",
" print(\"Using previously computed coupling index saved in file \", TCI_filePath)\n",
" else: # Compute TCI\n",
"\n",
" # Get list of necessary time series files\n",
" soilWater_file = np.sort(\n",
" glob.glob(\n",
" ts_dir\n",
" + \"/\"\n",
" + caseNames[iCase]\n",
" + \"/lnd/proc/tseries/\"\n",
" + caseNames[iCase]\n",
" + clmFile_h\n",
" + \"SOILWATER_10CM.\"\n",
" + startYrs[iCase]\n",
" + \"??-\"\n",
" + endYrs[iCase]\n",
" + \"??.nc\"\n",
" )\n",
" )\n",
" if len(soilWater_file) == 0:\n",
" print(\"Soil moisture file not found!\")\n",
" elif len(soilWater_file) > 1:\n",
" print(\n",
" \"More than one file matches requested time period and case for soil moisture.\"\n",
" )\n",
" elif len(soilWater_file) == 1:\n",
" soilWater_DS = xr.open_dataset(soilWater_file[0], decode_times=True)\n",
"\n",
" sh_file = np.sort(\n",
" glob.glob(\n",
" ts_dir\n",
" + \"/\"\n",
" + caseNames[iCase]\n",
" + \"/lnd/proc/tseries/\"\n",
" + caseNames[iCase]\n",
" + clmFile_h\n",
" + \"FSH_TO_COUPLER.\"\n",
" + startYrs[iCase]\n",
" + \"??-\"\n",
" + endYrs[iCase]\n",
" + \"??.nc\"\n",
" )\n",
" )\n",
" if len(sh_file) == 0:\n",
" print(\"Land-based SHFLX file not found!\")\n",
" elif len(sh_file) > 1:\n",
" print(\"More than one file matches requested time period and case for SH.\")\n",
" elif len(sh_file) == 1:\n",
" shflx_DS = xr.open_dataset(sh_file[0])\n",
"\n",
" # If years start at 0000, offset by 1700 years for analysis\n",
" yrOffset = 1850\n",
" if shflx_DS[\"time.year\"].values[0] < 1500:\n",
" shflx_DS[\"time\"] = shflx_DS.time + timedelta(days=yrOffset * 365)\n",
" if soilWater_DS[\"time.year\"].values[0] < 1500:\n",
" soilWater_DS[\"time\"] = soilWater_DS.time + timedelta(days=yrOffset * 365)\n",
" # Convert times to datetime for easier use\n",
" shflx_DS[\"time\"] = shflx_DS.indexes[\"time\"].to_datetimeindex()\n",
" soilWater_DS[\"time\"] = soilWater_DS.indexes[\"time\"].to_datetimeindex()\n",
"\n",
" # Add case ID (short name) to the DS\n",
" shflx_DS = shflx_DS.assign_coords({\"case\": shortNames[iCase]})\n",
" soilWater_DS = soilWater_DS.assign_coords({\"case\": shortNames[iCase]})\n",
"\n",
" ## Compute coupling index and save to netCDF file\n",
" ## - - - - - - - - - - - - - - - - - - - - - - - - -\n",
" xname = \"SOILWATER_10CM\" # Controlling variable\n",
" yname = \"FSH_TO_COUPLER\" # Responding variable\n",
"\n",
" xDS = soilWater_DS\n",
" yDS = shflx_DS\n",
"\n",
" couplingInd = compute_couplingIndex_cesm(xname, yname, xDS, yDS)\n",
" couplingInd.to_netcdf(TCI_filePath)\n",
" print(\"File created: \", TCI_filePath)"
]
},
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"source": [
"### 2.1 Read in FLUXNET data if requested"
]
},
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"# --------------------------------------------------------\n",
"# Function to read requested variables from FLUXNET file.\n",
"# - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
"#\n",
"# Inputs: fileName --> Full path to FLUXNET data file\n",
"# varNames --> An array of variable names to be\n",
"# retrieved from said data file.\n",
"# NOTE: If you wish to retrieve *all*\n",
"# variables, pass the string 'ALL'.\n",
"#\n",
"# Outputs: fluxnetID --> ID string used to identify station\n",
"# fluxnetDS --> An x-array dataset containing the\n",
"# requested variables.\n",
"# Missing values will be set to NaN.\n",
"#\n",
"# --------------------------------------------------------\n",
"\n",
"\n",
"def readFLUXNET_var(fileName, varNames):\n",
" # Get ID of station\n",
" startID = fileName.find(\"FLX_\")\n",
" fluxnetID = fileName[startID + 4 : startID + 10]\n",
"\n",
" # If this is taking a long time or you just want to know where in the stations you are, uncomment print statement\n",
" # print('Reading in site - ', fluxnetID)\n",
"\n",
" # Read in CSV file containing data\n",
" dataDF = pd.read_csv(fileName)\n",
"\n",
" # Return ALL variables from dataDF if requested\n",
" if varNames == \"ALL\":\n",
" fluxnetDF = dataDF\n",
"\n",
" # Set any value that's missing to NaN (not -9999)\n",
" fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
"\n",
" # If time has been requested, reformat to pandas date index\n",
" fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
" fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
" )\n",
" fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
"\n",
" # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
" # NOTE: since current implementation doesn't use the pre-formatted CoMeT, might not need this step now\n",
" fluxnetDS = fluxnetDF.to_xarray()\n",
"\n",
" # Reduce returned DF to contain only variables of interest\n",
" else:\n",
"\n",
" # Check that requested variables are available in specific file\n",
" errCount = 0 # Initialize flag for error\n",
" colNames = dataDF.columns.values # Available variables in file\n",
"\n",
" for iVar in range(len(varNames)): # Check each variable individually\n",
" if (varNames[iVar] in colNames) == False:\n",
" # Turn on print statement for more verbose output\n",
" # print('** ERROR: %13s not contained in file for %8s **' %(varNames[iVar], fluxnetID))\n",
"\n",
" # If any variable is not conatined in file, return a NaN\n",
" fluxnetDS = -999\n",
" errCount = errCount + 1\n",
"\n",
" # If all the variables *are* available, isolate those in DF and return that\n",
" if errCount == 0:\n",
" fluxnetDF = dataDF[varNames]\n",
"\n",
" # Set any value that's missing to NaN (not -999)\n",
" fluxnetDF = fluxnetDF.replace(-9999, np.nan)\n",
"\n",
" # If time has been requested, reformat to pandas make index\n",
" if (\"TIMESTAMP\" in varNames) == True:\n",
" fluxnetDF[\"TIMESTAMP\"] = pd.to_datetime(\n",
" fluxnetDF[\"TIMESTAMP\"].values, format=\"%Y%m%d\"\n",
" )\n",
" fluxnetDF = fluxnetDF.set_index([\"TIMESTAMP\"])\n",
"\n",
" # Convert dataframe to Xarray Dataset (required to use coupling metrics toolbox)\n",
" fluxnetDS = fluxnetDF.to_xarray()\n",
"\n",
" return (fluxnetID, fluxnetDS)\n",
"\n",
"\n",
"\"\"\"\n",
"Inputs: xname -- controlling variable \n",
" yname -- responding variable\n",
" ds -- dataset containing xname and yname data \n",
" \n",
"This is pulled almost directly from Ahmed Tawfik's CI code here: \n",
" https://github.com/abtawfik/coupling-metrics/blob/new_version_1/src/comet/metrics/coupling_indices.py \n",
"\"\"\"\n",
"\n",
"\n",
"def compute_couplingIndex_FLUXNET(xname, yname, ds):\n",
" xday = ds[xname].groupby(\"TIMESTAMP.season\")\n",
" yday = ds[yname].groupby(\"TIMESTAMP.season\")\n",
"\n",
" # Get the covariance of the two (numerator in coupling index)\n",
" covarTerm = ((xday - xday.mean()) * (yday - yday.mean())).groupby(\n",
" \"TIMESTAMP.season\"\n",
" ).sum() / xday.count()\n",
"\n",
" # Now compute the actual coupling index\n",
" couplingIndex = covarTerm / xday.std()\n",
"\n",
" return couplingIndex"
]
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"source": [
"if fluxnet_comparison == True:\n",
" ## Metadata files\n",
" siteInfoFile = os.path.join(obs_data_dir, obsDir, \"SiteList.csv\")\n",
" siteInfoDF = pd.read_csv(siteInfoFile)\n",
"\n",
" metadataFile = os.path.join(\n",
" obs_data_dir,\n",
" obsDir,\n",
" \"FLX_AA-Flx_BIF_ALL_20200501/FLX_AA-Flx_BIF_DD_20200501.csv\",\n",
" )\n",
" metadataDF = pd.read_csv(metadataFile)\n",
"\n",
" ## List of all station files\n",
" dataFiles = glob.glob(os.path.join(obs_data_dir, obsDir, \"FLX_*/*SUBSET_DD*\"))\n",
"\n",
" # Set up a few empty arrays to save data into\n",
" terraCI_fluxnetConverted = np.full(\n",
" [len(dataFiles), 4], np.nan\n",
" ) # CI using kg/m2 soil water content [nStations, seasons]\n",
"\n",
" # Also save out some data on each station\n",
" startTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
" endTime_fluxnet = np.zeros(len(dataFiles), dtype=\"datetime64[s]\")\n",
" lat_fluxnet = np.full([len(dataFiles)], np.nan)\n",
" lon_fluxnet = np.full([len(dataFiles)], np.nan)\n",
" SWCdepth = np.full([len(dataFiles)], np.nan)\n",
"\n",
" stationID = []\n",
" stationID_converted = []\n",
"\n",
" allStationID = []\n",
"\n",
" # Variables I want returned:\n",
" varNames = [\"TIMESTAMP\", \"H_F_MDS\", \"SWC_F_MDS_1\", \"SWC_F_MDS_1_QC\"]\n",
"\n",
" # Loop over each station (data file)\n",
" for iStation in range(len(dataFiles)):\n",
"\n",
" # Read in data\n",
" # ----------------------------------------------------------\n",
" fluxnetID, fluxnetDS = readFLUXNET_var(dataFiles[iStation], varNames)\n",
"\n",
" # Save lat and lon for this station\n",
" # ----------------------------------------------------------\n",
" indStation = int(np.where(fluxnetID == siteInfoDF[\"SITE_ID\"])[0][0])\n",
" lat_fluxnet[iStation] = siteInfoDF[\"LOCATION_LAT\"].values[indStation]\n",
" lon_fluxnet[iStation] = siteInfoDF[\"LOCATION_LONG\"].values[indStation]\n",
" allStationID.append(fluxnetID)\n",
"\n",
" # Check that there was data saved for this particular site:\n",
" # ----------------------------------------------------------\n",
" if type(fluxnetDS) == int:\n",
" print(\"No data for station: %8s\" % fluxnetID)\n",
"\n",
" elif (np.all(np.isnan(fluxnetDS[\"H_F_MDS\"])) == True) | (\n",
" np.all(np.isnan(fluxnetDS[\"SWC_F_MDS_1\"])) == True\n",
" ):\n",
" print(\"No data for station: %8s\" % fluxnetID)\n",
"\n",
" # If data is present:\n",
" # ----------------------------------------------------------\n",
" else:\n",
" # Only consider where data is actually present for selected vars\n",
" iReal = np.where(\n",
" (np.isfinite(fluxnetDS[\"SWC_F_MDS_1\"]) == True)\n",
" & (np.isfinite(fluxnetDS[\"H_F_MDS\"]) == True)\n",
" )[0]\n",
" fluxnetDS = fluxnetDS.isel(TIMESTAMP=iReal)\n",
"\n",
" stationID.append(fluxnetID)\n",
"\n",
" # Convert units from volumetric (%) to mass (kg/m2)\n",
" # -------------------------------------------------\n",
" # Step 1: Convert from % to fraction\n",
" fracSM = (fluxnetDS[\"SWC_F_MDS_1\"].values) / 100.0\n",
"\n",
" # Step 2: Need to use depth of obs in conversion\n",
" metaData_station = metadataDF[metadataDF.SITE_ID == fluxnetID]\n",
" iSWC = np.where(metaData_station.DATAVALUE == \"SWC_F_MDS_1\")[0]\n",
" # Some locations (5) have two depths\n",
" if len(iSWC) > 1:\n",
" for iDepth in range(len(iSWC)):\n",
" SWC_DF = metaData_station[iSWC[iDepth] : iSWC[iDepth] + 4]\n",
"\n",
" depth = np.asarray(\n",
" SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
" ).astype(float)\n",
" depthDay = np.asarray(\n",
" SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_DATE\"].DATAVALUE.values[0]\n",
" ).astype(int)\n",
" depthDay = int(\n",
" str(depthDay)[:8]\n",
" ) # Some weird ones have time attached; don't want that\n",
" depthDay = pd.to_datetime(depthDay, format=\"%Y%m%d\")\n",
"\n",
" # Keep deepest level as the depth for station\n",
" if iDepth == 0:\n",
" SWCdepth[iStation] = depth\n",
" convertSM = fracSM * 1000.0 * np.abs(depth)\n",
" else:\n",
" # Use date as break point for getting kg/m2 SWC\n",
" # Eq: SWC_kgm2 = SWC_vol [m3/m3] * 1000 [kg/m3] * depth [m]\n",
" dateArr = pd.DatetimeIndex(fluxnetDS.TIMESTAMP.values)\n",
" iTime = int(np.where(dateArr == depthDay)[0][0])\n",
" convertSM[iTime::] = (fracSM[iTime::]) * 1000.0 * np.abs(depth)\n",
"\n",
" # Keep deepest level as the depth for station\n",
" if depth < SWCdepth[iStation]:\n",
" SWCdepth[iStation] = depth\n",
"\n",
" stationID_converted.append(fluxnetID)\n",
"\n",
" # If station only has one level recorded, things are a bit easier:\n",
" else:\n",
" SWC_DF = metaData_station[iSWC[0] : iSWC[0] + 4]\n",
" SWCdepth[iStation] = np.asarray(\n",
" SWC_DF[SWC_DF.VARIABLE == \"VAR_INFO_HEIGHT\"].DATAVALUE.values[0]\n",
" ).astype(float)\n",
" convertSM = fracSM * 1000.0 * np.abs(SWCdepth[iStation])\n",
"\n",
" stationID_converted.append(fluxnetID)\n",
"\n",
" # Save converted soil moisture to dataset\n",
" fluxnetDS[\"SWC_F_MDS_1_convert\"] = ((\"TIMESTAMP\"), convertSM)\n",
"\n",
" # Save first and last time used for computing CI\n",
" # ----------------------------------------------\n",
" startTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[0]\n",
" endTime_fluxnet[iStation] = fluxnetDS[\"TIMESTAMP\"].values[-1]\n",
"\n",
" # Compute terrestrial coupling metric\n",
" # -----------------------------------\n",
" terraLeg = compute_couplingIndex_FLUXNET(\n",
" \"SWC_F_MDS_1_convert\", \"H_F_MDS\", fluxnetDS\n",
" )\n",
"\n",
" # If there's less than one full year of data, don't use station\n",
" # (i.e., as long as all 4 seasons are defined, save values)\n",
" if np.shape(terraLeg)[0] == 4:\n",
" terraCI_fluxnetConverted[iStation, :] = terraLeg\n",
"\n",
" seasons = terraLeg.season\n",
"\n",
" ## Print some useful information\n",
" print(\n",
" \"Number of FLUXNET stations with CI calculated: %i\"\n",
" % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
" )\n",
"\n",
" # How many months go into each calculation of CI for JJA?\n",
" nMonths = np.full([len(dataFiles)], np.nan)\n",
"\n",
" for iSt in range(len(dataFiles)):\n",
" if np.isfinite(terraCI_fluxnetConverted[iSt, 1]):\n",
" dateRange = pd.date_range(\n",
" start=startTime_fluxnet[iSt], end=endTime_fluxnet[iSt], freq=\"ME\"\n",
" )\n",
" nMonths[iSt] = len(\n",
" np.where((dateRange.month >= 6) & (dateRange.month <= 8))[0]\n",
" )\n",
"\n",
" print(\n",
" \"Minimum number of months used for JJA mean CI: %i \" % int(np.nanmin(nMonths))\n",
" )\n",
" print(\n",
" \"Maximum number of months used for JJA mean CI: %i \" % int(np.nanmax(nMonths))\n",
" )"
]
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"*Make some choices on limiting which stations are used*\n",
"- Let's limit usage to depths less than 20 cm (arbitrary, but I don't want us using non-surface soil moisture for this application). This will eliminate 11 stations.\n",
"- It would also be good to put some time limits on this. So let's say the observations need to have at least 9 months of data for JJA means (3-years). Otherwise, set terraCI to np.nan again so we don't use it."
]
},
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"if fluxnet_comparison == True:\n",
"\n",
" # Get stations with SWC from below 20 cm (or equal to zero)\n",
" iLimit = np.where((SWCdepth == 0.0) | (SWCdepth < -0.2))[0]\n",
"\n",
" # Set the terrestrial leg of CI to missing so we don't consider those\n",
" terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
"\n",
" print(\n",
" \"Number of FLUXNET stations to use with reasonable depths of SWC: %i\"\n",
" % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
" )\n",
"\n",
" # Get stations with less than 9 months used for JJA terrestrial CI\n",
" iLimit = np.where(nMonths < 9)[0]\n",
"\n",
" # Set to missing so we don't consider stations with less than three years of data going into the average\n",
" terraCI_fluxnetConverted[iLimit, :] = np.nan\n",
"\n",
" print(\n",
" \"Number of FLUXNET stations to use with 3+ years of JJA data: %i\"\n",
" % len(np.where(np.isfinite(terraCI_fluxnetConverted[:, 1]) == True)[0])\n",
" )"
]
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"source": [
"## 3. Make plots"
]
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"## Load coupling index with uxarray\n",
"gridFile = (\n",
" \"/glade/p/cesmdata/cseg/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc\"\n",
")\n",
"uxgrid = uxr.open_grid(gridFile)\n",
"uxgrid"
]
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"for iCase in range(len(caseNames)):\n",
" TCI_filePath = os.path.join(\n",
" cupid_temp, f\"{caseNames[iCase]}_TerrestrialCouplingIndex_SHvsSM.nc\"\n",
" )\n",
"\n",
" couplingIndex_case = uxr.open_dataset(gridFile, TCI_filePath)\n",
" # Rename the variable:\n",
" couplingIndex_case = couplingIndex_case.rename(\n",
" {\"__xarray_dataarray_variable__\": \"CouplingIndex\"}\n",
" )\n",
"\n",
" # Assign case coord\n",
" couplingIndex_case = couplingIndex_case.assign_coords(\n",
" {\"case\": couplingIndex_case.case.values}\n",
" )\n",
"\n",
" # Return all the cases in a single dataset\n",
" if iCase == 0:\n",
" couplingIndex_DS = couplingIndex_case\n",
" del couplingIndex_case\n",
" else:\n",
" couplingIndex_DS = uxr.concat([couplingIndex_DS, couplingIndex_case], \"case\")\n",
" del couplingIndex_case\n",
"\n",
"print(\"Coupling index is now ready to go\")"
]
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"source": [
"def make_cmap(colors, position=None, bit=False):\n",
" \"\"\"\n",
" make_cmap takes a list of tuples which contain RGB values. The RGB\n",
" values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
" True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
" a cmap with equally spaced colors.\n",
" Arrange your tuples so that the first color is the lowest value for the\n",
" colorbar and the last is the highest.\n",
" position contains values from 0 to 1 to dictate the location of each color.\n",
" \"\"\"\n",
"\n",
" import matplotlib as mpl\n",
" import numpy as np\n",
"\n",
" bit_rgb = np.linspace(0, 1, 256)\n",
" if position == None:\n",
" position = np.linspace(0, 1, len(colors))\n",
" else:\n",
" if len(position) != len(colors):\n",
" sys.exit(\"position length must be the same as colors\")\n",
" elif position[0] != 0 or position[-1] != 1:\n",
" sys.exit(\"position must start with 0 and end with 1\")\n",
"\n",
" if bit:\n",
" for i in range(len(colors)):\n",
" colors[i] = (\n",
" bit_rgb[colors[i][0]],\n",
" bit_rgb[colors[i][1]],\n",
" bit_rgb[colors[i][2]],\n",
" )\n",
"\n",
" cdict = {\"red\": [], \"green\": [], \"blue\": []}\n",
" for pos, color in zip(position, colors):\n",
" cdict[\"red\"].append((pos, color[0], color[0]))\n",
" cdict[\"green\"].append((pos, color[1], color[1]))\n",
" cdict[\"blue\"].append((pos, color[2], color[2]))\n",
"\n",
" cmap = mpl.colors.LinearSegmentedColormap(\"my_colormap\", cdict, 256)\n",
" return cmap"
]
},
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"### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
"colorsList_SMvSHF = [\n",
" (124, 135, 181),\n",
" (107, 109, 161),\n",
" (51, 82, 120),\n",
" (49, 114, 127),\n",
" (97, 181, 89),\n",
" (200, 218, 102),\n",
" (255, 242, 116),\n",
" (238, 164, 58),\n",
"] # This example uses the 8-bit RGB\n",
"my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)"
]
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"def plotTCI_case(seasonstr, caseSel=None):\n",
"\n",
" transform = ccrs.PlateCarree()\n",
" projection = ccrs.PlateCarree()\n",
"\n",
" if caseSel:\n",
" # create a Poly Array from a 1D slice of a face-centered data variable\n",
" collection = (\n",
" couplingIndex_DS[\"CouplingIndex\"]\n",
" .sel(season=seasonstr)\n",
" .isel(case=caseSel)\n",
" .to_polycollection()\n",
" )\n",
"\n",
" collection.set_transform(transform)\n",
" collection.set_antialiased(False)\n",
" collection.set_cmap(my_cmap_SMvSHF)\n",
" collection.set_clim(vmin=-20, vmax=5)\n",
"\n",
" fig, ax = plt.subplots(\n",
" 1,\n",
" 1,\n",
" figsize=(12, 5),\n",
" facecolor=\"w\",\n",
" constrained_layout=True,\n",
" subplot_kw=dict(projection=projection),\n",
" )\n",
"\n",
" ax.coastlines()\n",
" ax.add_collection(collection)\n",
" ax.set_global()\n",
" fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
" ax.set_title(\n",
" seasonstr\n",
" + \" Coupling Index: \"\n",
" + str(couplingIndex_DS.case.isel(case=caseSel).values)\n",
" )\n",
"\n",
" plt.show()\n",
" plt.close()\n",
"\n",
" else:\n",
" # create a Poly Array from a 1D slice of a face-centered data variable\n",
" collection = (\n",
" couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).to_polycollection()\n",
" )\n",
"\n",
" collection.set_transform(transform)\n",
" collection.set_antialiased(False)\n",
" collection.set_cmap(my_cmap_SMvSHF)\n",
" collection.set_clim(vmin=-20, vmax=5)\n",
"\n",
" fig, ax = plt.subplots(\n",
" 1,\n",
" 1,\n",
" figsize=(12, 5),\n",
" facecolor=\"w\",\n",
" constrained_layout=True,\n",
" subplot_kw=dict(projection=projection),\n",
" )\n",
"\n",
" ax.coastlines()\n",
" ax.add_collection(collection)\n",
" ax.set_global()\n",
" fig.colorbar(collection, label=\"Terrestrial Coupling Index ($W m^{-2}$)\")\n",
" ax.set_title(\n",
" seasonstr + \" Coupling Index: \" + str(couplingIndex_DS.case.values)\n",
" )\n",
"\n",
" if fluxnet_comparison == True:\n",
" ## Add FLUXNET obs\n",
" iSeason = np.where(seasons == seasonstr)[0]\n",
" iStations = np.where(\n",
" np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True\n",
" )[0]\n",
" norm_CI = matplotlib.colors.Normalize(vmin=-20, vmax=5)\n",
"\n",
" ax.scatter(\n",
" lon_fluxnet[iStations],\n",
" lat_fluxnet[iStations],\n",
" c=terraCI_fluxnetConverted[iStations, iSeason],\n",
" cmap=my_cmap_SMvSHF,\n",
" norm=norm_CI,\n",
" edgecolor=\"k\",\n",
" s=30,\n",
" marker=\"o\",\n",
" transform=ccrs.PlateCarree(),\n",
" )\n",
"\n",
" plt.show()\n",
" plt.close()\n",
"\n",
" return"
]
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"def plotScatter(seasonstr, caseSel=None):\n",
" node_lats = uxgrid.face_lat.values\n",
" node_lons = uxgrid.face_lon.values\n",
"\n",
" predictions = []\n",
"\n",
" fig, axs = plt.subplots(1, 1, figsize=(8, 8))\n",
"\n",
" iSeason = np.where(seasons == seasonstr)[0]\n",
" iStations = np.where(np.isfinite(terraCI_fluxnetConverted[:, iSeason]) == True)[0]\n",
"\n",
" for iPoint in range(len(iStations)):\n",
" this_lon = lon_fluxnet[iStations[iPoint]] # lon1\n",
" this_lat = lat_fluxnet[iStations[iPoint]] # lat1\n",
" obs_point = np.array((this_lon, this_lat))\n",
"\n",
" # Get subset of relevant points\n",
" i = np.where(\n",
" (node_lats >= (this_lat - 2))\n",
" & (node_lats <= (this_lat + 2))\n",
" & (node_lons >= (this_lon - 2))\n",
" & (node_lons <= (this_lon + 2))\n",
" )[0]\n",
"\n",
" minDistance = 100\n",
" for iSelClose in range(len(i)):\n",
" # Find point in uxarray? Use euclidian distance\n",
" distance = np.linalg.norm(\n",
" obs_point - np.array((node_lons[i[iSelClose]], node_lats[i[iSelClose]]))\n",
" )\n",
"\n",
" if (distance < minDistance) & (\n",
" np.isfinite(\n",
" couplingIndex_DS[\"CouplingIndex\"]\n",
" .sel(season=\"JJA\")\n",
" .values[i[iSelClose]]\n",
" )\n",
" == True\n",
" ):\n",
" minDistance = distance\n",
" selLoc = i[iSelClose]\n",
"\n",
" predictions = np.append(\n",
" predictions,\n",
" couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
" )\n",
" axs.plot(\n",
" terraCI_fluxnetConverted[iPoint, iSeason],\n",
" couplingIndex_DS[\"CouplingIndex\"].sel(season=seasonstr).values[selLoc],\n",
" \"bo\",\n",
" alpha=0.5,\n",
" )\n",
"\n",
" axs.set_xlabel(\"FLUXNET CI Value\", fontsize=12)\n",
" axs.set_ylabel(\"CESM CI Value\", fontsize=12)\n",
" axs.set_title(\n",
" \"Individual station CI vs. nearest gridcell CI: \" + seasonstr, fontsize=14\n",
" )\n",
" axs.set_xlim([-25, 25])\n",
" axs.set_ylim([-25, 25])\n",
" axs.plot(np.arange(-25, 26), np.arange(-25, 26), \"k--\")\n",
"\n",
" rmse = np.sqrt(\n",
" ((predictions - terraCI_fluxnetConverted[iStations, iSeason]) ** 2).mean()\n",
" )\n",
" axs.text(0.05, 0.95, \"RMSE: \" + str(rmse), transform=axs.transAxes)\n",
"\n",
" return axs"
]
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"if len(caseNames) == 1:\n",
" plotTCI_case(\"JJA\", None)\n",
" plotTCI_case(\"DJF\", None)\n",
"\n",
" plotScatter(\"JJA\", None)\n",
" plotScatter(\"DJF\", None)\n",
"else:\n",
" for iCase in range(len(caseNames)):\n",
" plotTCI_case(\"JJA\", iCase)\n",
" plotTCI_case(\"DJF\", iCase)\n",
"\n",
" plotScatter(\"JJA\", iCase)\n",
" plotScatter(\"DJF\", iCase)"
]
}
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"output_path": "/glade/work/hannay/CUPiD/examples/key_metrics/computed_notebooks/lnd/Global_TerrestrialCouplingIndex_VisualCompareObs.ipynb",
"parameters": {
"CESM_output_dir": "/glade/derecho/scratch/hannay/archive",
"base_case_name": "b.e30_beta06.B1850C_LTso.ne30_t232_wgx3.188",
"base_case_output_dir": "/glade/derecho/scratch/gmarques/archive",
"base_end_date": "0021-12-01",
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"clmFile_h": "h0",
"end_date": "0021-12-01",
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