From 3f8e6b953ba1b7387f384bcfdd0c564d6d9490c4 Mon Sep 17 00:00:00 2001 From: Azadeh Gholoubi <51101867+azadeh-gh@users.noreply.github.com> Date: Thu, 22 Feb 2024 13:25:02 -0500 Subject: [PATCH] Feature/obs error assignment tool (#147) * added source codes along with input and output example files * Create README.md * Update README.md * Update README.md * Updated some comments * Update run_error_model_estimate.sh * Update README.md * Applied Linting * Update README.md * Update README.md * Update README.md * Fixed pycodestyle violation already in develop * Modified the ploting part of the python code * fixed coding norms * ddressed reviewer comments. added docstring in each function and modified paths --- fix/cloudy_radiance_info.txt | 62 ++ scripts/plot_proftrace.py | 8 +- .../ObsErrorAssignment.py | 535 ++++++++++++++++++ ush/obsErrorAssignmentTool/README.md | 34 ++ .../diag_amsua_n19_ges.2023013018.nc4 | Bin 0 -> 4980544 bytes .../bin005/amsua_n19_obsError_parameters.csv | 3 + .../run_error_model_estimate.sh | 49 ++ 7 files changed, 687 insertions(+), 4 deletions(-) create mode 100644 fix/cloudy_radiance_info.txt create mode 100644 ush/obsErrorAssignmentTool/ObsErrorAssignment.py create mode 100644 ush/obsErrorAssignmentTool/README.md create mode 100644 ush/obsErrorAssignmentTool/config_files/diag_amsua_n19_ges.2023013018.nc4 create mode 100644 ush/obsErrorAssignmentTool/output/bin005/amsua_n19_obsError_parameters.csv create mode 100755 ush/obsErrorAssignmentTool/run_error_model_estimate.sh diff --git a/fix/cloudy_radiance_info.txt b/fix/cloudy_radiance_info.txt new file mode 100644 index 0000000..c5a9664 --- /dev/null +++ b/fix/cloudy_radiance_info.txt @@ -0,0 +1,62 @@ +radiance_mod_instr_input:: +!obsname obsloc ex_obserr ex_biascor cld_effect + gmi sea ex_obserr3 .true. .false. + amsua sea ex_obserr1 .true. .true. + atms sea ex_obserr1 .true. .true. +:: + +obs_amsua:: +! Parameters for the observation error model +! cclr [kg/m2] & ccld [kg/m2]: range of cloud amounts +! over which the main increase in error take place +! ch cclr ccld + 1 0.050 0.600 + 2 0.030 0.450 + 3 0.030 0.400 + 4 0.020 0.450 + 5 0.000 1.000 + 6 0.100 1.500 + 15 0.030 0.200 +:: + +obs_atms:: +! Parameters for the observation error model +! cclr [kg/m2] & ccld [kg/m2]: range of cloud amounts +! over which the main increase in error take place +! ch cclr ccld + 1 0.030 0.350 + 2 0.030 0.380 + 3 0.030 0.400 + 4 0.020 0.450 + 5 0.030 0.500 + 6 0.080 1.000 + 7 0.150 1.000 + 16 0.020 0.350 + 17 0.030 0.500 + 18 0.030 0.500 + 19 0.030 0.500 + 20 0.030 0.500 + 21 0.050 0.500 + 22 0.100 0.500 +:: + +obs_gmi:: +! Parameters for the observation error model +! cclr [kg/m2] & ccld [kg/m2]: range of cloud amounts +! over which the main increase in error take place +! ch cclr ccld + 1 0.050 0.200 + 2 0.050 0.200 + 3 0.050 0.200 + 4 0.050 0.200 + 5 0.050 0.200 + 6 0.050 0.200 + 7 0.050 0.200 + 8 0.050 0.200 + 9 0.050 0.200 + 10 0.050 0.300 + 11 0.050 0.200 + 12 0.050 0.300 + 13 0.050 0.300 +:: + diff --git a/scripts/plot_proftrace.py b/scripts/plot_proftrace.py index a601500..212d40c 100644 --- a/scripts/plot_proftrace.py +++ b/scripts/plot_proftrace.py @@ -297,13 +297,13 @@ def collect_statistics(setdict): # entries in a list # if they are not a list, assert them as a list it = sd['it'] - it = [it] if type(it) != list else it + it = [it] if type(it) is not list else it use = sd['use'] - use = [use] if type(use) != list else use + use = [use] if type(use) is not list else use typ = sd['typ'] - typ = [typ] if type(typ) != list else typ + typ = [typ] if type(typ) is not list else typ styp = sd['styp'] - styp = [styp] if type(styp) != list else styp + styp = [styp] if type(styp) is not list else styp # extract variable from gdas stat = gdas.extract(var) # t, uv, q, etc. # date it obs use diff --git a/ush/obsErrorAssignmentTool/ObsErrorAssignment.py b/ush/obsErrorAssignmentTool/ObsErrorAssignment.py new file mode 100644 index 0000000..4102665 --- /dev/null +++ b/ush/obsErrorAssignmentTool/ObsErrorAssignment.py @@ -0,0 +1,535 @@ +import argparse +import glob + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import xarray as xr +from scipy.stats import gaussian_kde + + +def calculate_statistics(nc4_files, channel, BinValue, qc_num): + """ + Calculate observation statistics necessary for estimating observation error parameters. + + Parameters: + nc4_files (list): List of paths to netCDF4 files. + channel (int): Channel number. + bin_value (float): Size of the bin for data categorization. + qc_num (int): Quality control flag for filtering. + + Returns: + Tuple: QCDataSummaryDF (DataFrame), AllDataSummaryDF (DataFrame), + All_o_f (numpy.ndarray), All_clw (numpy.ndarray). + """ + Bins = np.arange(0, 2.51, BinValue) + BinsLength = len(Bins) - 1 + + # create a dict to hold values for each bin + QCData = {} + AllData = {} + All_o_f = np.array([]) # this keeps o-f raw data (no binning) + All_clw = np.array([]) # this keeps clw raw data (no binning) + + for i in range(BinsLength): + BinLow, BinHigh = str(round(Bins[i], 2)), str(round(Bins[i + 1], 2)) + QCData["%s_%s" % (BinLow, BinHigh)] = np.array([]) + AllData["%s_%s" % (BinLow, BinHigh)] = np.array([]) + + for fileNo, filePath in enumerate(nc4_files): + print(fileNo + 1) + file = xr.open_dataset(filePath) + channelNum = file["Channel_Index"].isin(channel) + O_F = file["Obs_Minus_Forecast_adjusted"][channelNum].values + QC_Flag = file["QC_Flag"][channelNum].values + clw_mean = ( + file["clw_guess"][channelNum].values + file["clw_obs"][channelNum].values + ) / 2 + # first remove O minus F outliers + CheckBool = QC_Flag == qc_num + O_F = O_F[CheckBool] + clw_mean = clw_mean[CheckBool] + # record O-F for use in PDF plot + All_o_f = np.append(All_o_f, O_F) + All_clw = np.append(All_clw, clw_mean) + for i in range(BinsLength): + BinLow, BinHigh = str(round(Bins[i], 2)), str(round(Bins[i + 1], 2)) + CheckBool = (clw_mean <= float(BinHigh)) & (clw_mean >= float(BinLow)) + ThisFile_O_F = O_F[CheckBool] + QCData["%s_%s" % (BinLow, BinHigh)] = np.append( + QCData["%s_%s" % (BinLow, BinHigh)], ThisFile_O_F + ) + + # collect data without QC + BT = file["Observation"][channelNum].values + O_F = file["Obs_Minus_Forecast_adjusted"][channelNum].values + clw_mean = ( + file["clw_guess"][channelNum].values + file["clw_obs"][channelNum].values + ) / 2 + + CheckBool = (BT < 500) & (BT > 50) + O_F = O_F[CheckBool] + clw_mean = clw_mean[CheckBool] + for i in range(BinsLength): + BinLow, BinHigh = str(round(Bins[i], 2)), str(round(Bins[i + 1], 2)) + CheckBool = (clw_mean <= float(BinHigh)) & (clw_mean >= float(BinLow)) + ThisFile_O_F = O_F[CheckBool] + AllData["%s_%s" % (BinLow, BinHigh)] = np.append( + AllData["%s_%s" % (BinLow, BinHigh)], ThisFile_O_F + ) + + AllDataSummary = [] + for bins, bin_values in AllData.items(): + cle_mean = 0.5 * (float(bins.split("_")[0]) + float(bins.split("_")[1])) + if len(bin_values) > 1: + AllDataSummary.append( + [ + bins, + cle_mean, + np.mean(bin_values), + np.std(bin_values), + len(bin_values), + ] + ) + AllDataSummaryDF = pd.DataFrame( + AllDataSummary, + columns=[ + "Bin", + "Mean cloud Amount", + "FG Departure", + "FG Departure std.Dev", + "Count", + ], + ) + AllDataSummaryDF.dropna(inplace=True) + + QCDataSummary = [] + for bins, bin_values in QCData.items(): + cle_mean = 0.5 * (float(bins.split("_")[0]) + float(bins.split("_")[1])) + if len(bin_values) > 1: + QCDataSummary.append( + [ + bins, + cle_mean, + np.mean(bin_values), + np.std(bin_values), + len(bin_values), + ] + ) + QCDataSummaryDF = pd.DataFrame( + QCDataSummary, + columns=[ + "Bin", + "Mean cloud Amount", + "FG Departure", + "FG Departure std.Dev", + "Count", + ], + ) + QCDataSummaryDF.dropna(inplace=True) + + return QCDataSummaryDF, AllDataSummaryDF, All_o_f, All_clw + + +# find the second point +def find_clw_cloudy(QCDataSummaryDF): + """ + Find the clw_cld parameter value based on the maximum slope of the FG Departure std.Dev. + + This function calculates the clw_cld parameter value based on the maximum slope + of the FG Departure std.Dev, which changes as a function of the mean cloud amount. + + Parameters: + QCDataSummaryDF (DataFrame): DataFrame summarizing QC data. + + Returns: + float: The calculated clw_cld parameter value. + + """ + QCDataSummaryDF["x"] = QCDataSummaryDF["Mean cloud Amount"] + QCDataSummaryDF["y"] = QCDataSummaryDF["FG Departure std.Dev"] + x = QCDataSummaryDF["x"].values + y = QCDataSummaryDF["y"].values + MaxOfY = QCDataSummaryDF["y"].max() + + # coordinates of the first point + XOfFirst = x[np.argmin(x)] + YOfFirst = y[np.argmin(x)] + + # find the point on west of max y + XOfYMax = x[np.argmax(y)] + CandidatePoints = QCDataSummaryDF[QCDataSummaryDF["x"] <= XOfYMax] + CandidatePoints = CandidatePoints[CandidatePoints["x"] != XOfFirst] + + maxslope = 0 + for i, row in CandidatePoints.iterrows(): + tempslope = (row["y"] - YOfFirst) / (row["x"] - XOfFirst) + if tempslope > maxslope: + maxslope = tempslope + + clw_cld = (MaxOfY - YOfFirst) / maxslope + return clw_cld + XOfFirst + + +def error_estimation(QCDataSummaryDF): + """ + Estimate observation error parameters based on the provided QCDataSummaryDF DataFrame. + + Parameters: + QCDataSummaryDF (DataFrame): DataFrame summarizing QC data. + + Returns: + Tuple: x (list), y (list), and error parameters (list). + """ + cLW_mean = QCDataSummaryDF["Mean cloud Amount"].values + err = QCDataSummaryDF["FG Departure std.Dev"].values + ErrCld = np.max(err) + ErrClr = err[0] + lastX = cLW_mean[-1] + Clw_clr = cLW_mean[0] + clw_cld = find_clw_cloudy(QCDataSummaryDF) + x = [0, Clw_clr, clw_cld, lastX] + y = [ErrClr, ErrClr, ErrCld, ErrCld] + errorParam = [ErrCld, ErrClr, Clw_clr, clw_cld] + return x, y, errorParam + + +def read_previous_parameters( + sensor_name, channel_num, path_cloudy_radiance_info, path_global_satinfo +): + """ + Read previous parameters from specified files. + + Parameters: + sensor_name (str): Name of the sensor. + channel_num (int): Channel number. + path_cloudy_radiance_info (str): Path to cloudy radiance info file. + path_global_satinfo (str): Path to global satinfo file. + + Returns: + Tuple: x1 (list), y1 (list) values read from the files. + """ + # first from cloudy_radiance file + senseor_generic_name = sensor_name.split("_")[0] + with open(path_cloudy_radiance_info) as TXTFile: + for linenumer, line in enumerate(TXTFile): + if line.startswith("obs_%s" % senseor_generic_name): + for _ in range(4): + TXTFile.readline() + while 1: + ch, cclr, ccld = TXTFile.readline().strip().split() + if int(ch) == channel_num: + break + # now read global_satinfo + satinfoDF = pd.read_csv(path_global_satinfo, delim_whitespace=True) + satinfoDF = satinfoDF.rename(columns={"!sensor/instr/sat": "sensor_name"}) + satinfoDF = satinfoDF.set_index("sensor_name") + satinfoDF = satinfoDF[satinfoDF["chan"] == channel_num] + error = satinfoDF.loc[sensor_name, "error"] + error_cld = satinfoDF.loc[sensor_name, "error_cld"] + x1 = [float(cclr), float(ccld)] + y1 = [float(error), float(error_cld)] + return x1, y1 + + +def plots( + AllDataSummaryDF, + QCDataSummaryDF, + output_path, + sensor, + channel, + x, + y, + x1, + y1, + All_o_f, + AllErrors, +): + """ + Generate plots based on provided data. + + Parameters: + AllDataSummaryDF (DataFrame): DataFrame summarizing all data. + QCDataSummaryDF (DataFrame): DataFrame summarizing QC data. + output_path (str): Path to save the plots. + sensor (str): Sensor name. + channel (int): Channel number. + x (list): List of x values. + y (list): List of y values. + x1 (list): List of x1 values. + y1 (list): List of y1 values. + All_o_f (numpy.ndarray): Array containing all observed minus forecast values. + AllErrors (numpy.ndarray): Array containing all errors. + """ + fig = plt.figure(figsize=(8, 12)) + ax1 = fig.add_subplot(2, 2, 1) + ax1.scatter( + QCDataSummaryDF["Mean cloud Amount"], + QCDataSummaryDF["FG Departure std.Dev"], + marker="o", + color="dodgerblue", + label="QC", + s=4, + ) + ax1.scatter( + AllDataSummaryDF["Mean cloud Amount"], + AllDataSummaryDF["FG Departure std.Dev"], + marker="o", + color="dimgrey", + label="All", + s=4, + ) + ax1.scatter(x1, y1, color="red", label="original", marker="o", s=6) + ax1.plot(x, y, color="green", label="New", linestyle="--") + ax1.scatter(x[1:3], y[1:3], marker="X", color="green", label="New Parameters", s=15) + + ax1.set_xlabel("Mean Cloud Amount", fontsize=14) + ax1.set_ylabel("FG Departure std.Dev", fontsize=14) + ax1.legend(loc="upper left", markerscale=2, scatterpoints=1) + inst, sat = sensor.split("_") + ax1.set_title( + "%s %s Channel %d" % (inst.upper(), sat.upper(), channel), fontsize=18 + ) + + ax2 = plt.subplot(2, 2, 2) + ax2.grid(True, which="both", color="lightgrey", linestyle="--") + ax2.scatter( + QCDataSummaryDF["Mean cloud Amount"], + QCDataSummaryDF["Count"], + marker="o", + color="dodgerblue", + label="QC", + s=4, + ) + ax2.scatter( + AllDataSummaryDF["Mean cloud Amount"], + AllDataSummaryDF["Count"], + marker="o", + color="dimgrey", + label="All", + s=4, + ) + ax2.set_yscale("log") + ax2.set_xlabel("Mean cloud Amount", fontsize=14) + ax2.set_ylabel("Number of observations", fontsize=14) + ax2.legend(loc="upper right", markerscale=2, scatterpoints=1) + + # Plot PDF of omf + ax3 = fig.add_subplot(2, 2, 3) + # Compute the PDF using kernel density estimation + kde = gaussian_kde(All_o_f) + x = np.linspace(All_o_f.min(), All_o_f.max(), 100) + pdf = kde.evaluate(x) + ax3.plot(x, pdf, label="Un-normalized", color="blue") + # plot the pDF of normalized FG + nomalizedFG = All_o_f / AllErrors + kde_norm = gaussian_kde(nomalizedFG) + x_norm = np.linspace(nomalizedFG.min(), nomalizedFG.max(), 100) + pdf_norm = kde_norm.evaluate(x_norm) + ax3.plot(x_norm, pdf_norm, label="normalized", color="red") + ax3.legend(loc="upper right", markerscale=2, scatterpoints=1) + ax3.set_xlabel("FG departure", fontsize=14) + ax3.set_ylabel("PDF", fontsize=14) + ax3.set_yscale("log") + ax3.set_xlim(-10, 10) + + ax4 = fig.add_subplot(2, 2, 4) + ax4.hist(AllErrors, bins=100, density=True, alpha=0.5, label="Un-normalized") + ax4.set_xlabel("Errors", fontsize=14) + ax4.legend(loc="upper right", markerscale=2, scatterpoints=1) + + plt.tight_layout() + plt.savefig(output_path + "%s_ch%d.png" % (sensor, channel)) + plt.close() + + +def get_all_errors(x, y, All_clw): + """ + Get all errors based on provided parameters. + + Parameters: + x (list): List of x values. + y (list): List of y values. + All_clw (numpy.ndarray): Array containing all cloud values. + + Returns: + numpy.ndarray: Array containing all errors. + """ + Clw_clr, clw_cld = x[1], x[2] + ErrClr, ErrCld = y[1], y[2] + AllErrors = np.empty(len(All_clw)) + AllErrors = np.where(All_clw >= clw_cld, ErrCld, AllErrors) + AllErrors = np.where(All_clw <= Clw_clr, ErrClr, AllErrors) + + # now assign the values in the middle + slope = (ErrCld - ErrClr) / (clw_cld - Clw_clr) + MiddleFormula = (All_clw - Clw_clr) * slope + ErrClr + AllErrors = np.where( + ((All_clw > Clw_clr) & (All_clw < clw_cld)), MiddleFormula, AllErrors + ) + return AllErrors + + +def compute_obs_error_parameter( + config_path, + output_path, + global_satinPath, + cloudy_path, + sensor, + Channels, + bin_size, + qc_flag, +): + """ + Compute observation error parameters based on provided data. + + Returns: + DataFrame: DataFrame containing the error parameters of each channel. + Saved as a CSV file. + """ + + print("+=================================================================+") + print( + "| compute_obs_error_parameter |" + ) + print("+-----------------------------------------------------------------+") + print(" ---(n) path to config nc files: " + str(config_path)) + print(" ---(o) path to output files: " + str(output_path)) + print(" ---(g) path to global_satinPath.txt: " + str(global_satinPath)) + print(" ---(l) path to cloudy_path.txt: " + str(cloudy_path)) + print(" ---(s) sensor name: " + str(sensor)) + print(" ---(c) channels: " + str(Channels)) + print(" ---(b) Bin Size: " + str(bin_size)) + print(" ---(q) QC Flag: " + str(qc_flag)) + # convert list of channels into a list of integers + Channels = list(map(int, Channels.split(","))) + AllErrorParam = [] + nc4_files = glob.glob(config_path + "diag_%s_ges*.nc4" % sensor) + print(nc4_files) + for i in Channels: + print("working on channel %d" % i) + QCDataSummaryDF, AllDataSummaryDF, All_o_f, All_clw = calculate_statistics( + nc4_files, i, bin_size, qc_flag + ) + x, y, errorParam = error_estimation(QCDataSummaryDF) + AllErrors = get_all_errors(x, y, All_clw) + x1, y1 = read_previous_parameters(sensor, i, cloudy_path, global_satinPath) + errorParam.append(i) + AllErrorParam.append(errorParam) + plots( + AllDataSummaryDF, + QCDataSummaryDF, + output_path, + sensor, + i, + x, + y, + x1, + y1, + All_o_f, + AllErrors, + ) + AllErrorParamDF = pd.DataFrame( + AllErrorParam, + columns=["Error_cld", "Error_clr", "clw_clr", "clw_cld", "channel"], + ) + AllErrorParamDF.to_csv( + output_path + "%s_obsError_parameters.csv" % sensor, index=False + ) + + +if __name__ == "__main__": + # Argument parsing and main function call + # Parser setup and argument parsing here + parser = argparse.ArgumentParser(description="obs error parameters") + + # Add arguments + parser.add_argument( + "-n", + dest="config_path", + help=r"REQUIRED: path to config nc files", + required=True, + metavar="DIR", + type=str, + ) + parser.add_argument( + "-o", + dest="output_path", + help=r"optional: path to output files", + required=True, + metavar="DIR", + type=str, + ) + parser.add_argument( + "-g", + dest="global_satinPath", + help=r"REQUIRED: path to global_satinPath.txt", + required=True, + metavar="DIR", + type=str, + ) + parser.add_argument( + "-l", + dest="cloudy_path", + help=r"REQUIRED: path to cloudy_path.txt", + required=True, + metavar="DIR", + type=str, + ) + parser.add_argument( + "-s", + dest="sensor", + help=r"REQUIRED: sensor name", + required=True, + metavar="string", + type=str, + ) + parser.add_argument( + "-c", + dest="Channels", + help=r'REQUIRED:list of channels.enclose the list in quotation,' + r' each separated by a comma like "1,2,3,5"', + required=True, + metavar="string", + type=str, + ) + parser.add_argument( + "-b", + dest="bin_size", + help=r"optional: size of bin for plotting", + required=False, + default=0.05, + metavar="float", + type=float, + ) + parser.add_argument( + "-q", + dest="qc_flag", + help=r"optional: qc flag for filtering", + required=False, + default=0, + metavar="integer", + type=int, + ) + + args = vars(parser.parse_args()) + + config_path = args["config_path"] + output_path = args["output_path"] + global_satinPath = args["global_satinPath"] + cloudy_path = args["cloudy_path"] + sensor = args["sensor"] + Channels = args["Channels"] + bin_size = args["bin_size"] + qc_flag = args["qc_flag"] + + compute_obs_error_parameter( + config_path, + output_path, + global_satinPath, + cloudy_path, + sensor, + Channels, + bin_size, + qc_flag, + ) diff --git a/ush/obsErrorAssignmentTool/README.md b/ush/obsErrorAssignmentTool/README.md new file mode 100644 index 0000000..e9b5222 --- /dev/null +++ b/ush/obsErrorAssignmentTool/README.md @@ -0,0 +1,34 @@ +### February 2024 +### Azadeh Gholoubi +# Python Tool for Observation Error Estimation + +## Overview +This tool automates the estimation of observation error parameters. It consists of a shell script, `run_error_model_estimate.sh`, which invokes the `ObsErrorAssignment.py` Python script to perform the following tasks: + +1. Compute mean cloud amount and first guess standard deviation. +2. Generate four plots: + - Standard deviation of FG departures in each channel as a function of mean cloud amount: (clw_guess + clw_obs)/2 + - Number of observations in each defined bin of mean cloud amount + - Histogram of un-normalised and also normalised FG departures in each channel + - Histogram of Errors +3. Estimate error parameters (`error_cld`, `error_clr`, `clw_clr`, and `clw_cld`) and create CSV files for each channel. + +## Usage +Before running the script, the user needs to specify certain parameters in `run_error_model_estimate.sh`: + +- `config_path`: Directory for input config .nc files +- `output_path`: Path to output files +- `sensor`: Sensor name +- `Channels`: Channel numbers (enclosed in quotes, separated by commas, e.g., "1,2,3,5") +- `bin_size`: Size of bin for plotting (default=0.05) +- `bindir`: Output folder name +- `qc_flag`: QC flag for filtering (default=0) +- `PyGSI`: Path to the PyGSI branch on Hera or Orion to load the proper environment + +To view all the required inputs for this tool, use the following command: + +```bash +python ObsErrorAssignment.py -h + + + diff --git a/ush/obsErrorAssignmentTool/config_files/diag_amsua_n19_ges.2023013018.nc4 b/ush/obsErrorAssignmentTool/config_files/diag_amsua_n19_ges.2023013018.nc4 new file mode 100644 index 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