Flow-Dependent, Cross-Timescale Model Diagnostic POD

.gitignore

@@ -94,4 +94,5 @@ pip-log.txt
 pip-delete-this-directory.txt
 
 # Jupyter Notebook
-.ipynb_checkpoints
+.ipynb_checkpoints
+.DS_Store

data/fieldlist_NCAR.jsonc

@@ -90,7 +90,6 @@
       "ndim": 3
     },
     "TREFHT" : {
-      // correct name? CMIP6 equivalent should be tas, temp at 2m ref height
       "standard_name": "air_temperature",
       "units": "K",
       "ndim": 3
@@ -157,6 +156,22 @@
       "units": "W m-2",
       "ndim": 3
     },
+    // Variables for flow_dep_diag module:
+    "Z250": {
+      "standard_name": "geopotential_height_250_mbar",
+      "units": "m",
+      "ndim": 3
+    },
+    "T250": {
+      "standard_name": "temperature_250_mbar",
+      "units": "K",
+      "ndim": 3
+    },
+    "T500": {
+      "standard_name": "temperature_500_mbar",
+      "units": "K",
+      "ndim": 3
+    },
     // Variables for AMOC_3D_Structure module:
     // "uo": {
     //   "standard_name": "sea_water_x_velocity",

diagnostics/convective_transition_diag/convecTransCriticalCollapse_usp.py

@@ -1,12 +1,11 @@
-# This file is part of the convective_transition_diag module of the MDTF code package (see LICENSE.txt)
-
+# This file is part of the convective_transition_diag module of the MDTF code package (see mdtf/MDTF_v2.0/LICENSE.txt)
 # ======================================================================
 # convecTransCriticalCollapse_usp.py
 #
 #   Called by convecTransCriticalCollapse.py
 #    Provides User-Specified Parameters for Fitting and Plotting
 #
-#   This file is part of the Convective Transition Diagnostic Package 
+#   This file is part of the Convective Transition Diagnostic Package
 #    and the MDTF code package. See LICENSE.txt for the license.
 #
 import json
@@ -32,7 +31,7 @@
 
 TAVE_VAR=os.environ["tave_var"]
 QSAT_INT_VAR=os.environ["qsat_int_var"]
-# Use 1:tave, or 2:qsat_int as Bulk Tropospheric Temperature Measure 
+# Use 1:tave, or 2:qsat_int as Bulk Tropospheric Temperature Measure
 BULK_TROPOSPHERIC_TEMPERATURE_MEASURE=int(os.environ["BULK_TROPOSPHERIC_TEMPERATURE_MEASURE"])
 
 # Directory & Filename for saving binned results (netCDF4)
@@ -53,7 +52,7 @@
 # List binned data file (with filename corresponding to casename)
 bin_output_list=sorted(glob.glob(BIN_OUTPUT_DIR+"/"+BIN_OUTPUT_FILENAME+".nc"))
 
-# Directory & Filename for saving figures 
+# Directory & Filename for saving figures
 #  convecTransCriticalCollapse.py generates 2 sets figures for MODEL
 FIG_OUTPUT_DIR=os.environ["WK_DIR"]+"/model/PS"
 # Figure filename for Convective Transition Statistics (CTS)
@@ -87,7 +86,7 @@
 ##### Start: FITTING-REQUIRED PARAMETERS #####
 # Use PRECIP_REF (units: mm/hr) to find a 0-th order approximation of Critical CWV w_c
 #  and PRECIP_FIT_MIN<precip<PRECIP_FIT_MAX (units: mm/hr) for Fitting
-# Different values for MODEL & OBS 
+# Different values for MODEL & OBS
 #  Change values for MODEL if necessary
 PRECIP_REF_MODEL=1
 PRECIP_FIT_MIN_MODEL=1.5
@@ -118,7 +117,7 @@
 marker_size = 40 # size of markers in scatter plots
 xtick_pad = 10 # padding between x tick labels and actual plot
 figsize1 = 14 # figure size set by figsize=(figsize1,figsize2)
-figsize2 = 12 
+figsize2 = 12
 
 ### There are 4+2 figures in level 2 diagnostics ###
 ### Choose the plot parameters for each figure below ###
@@ -137,7 +136,7 @@
 #==========================================
 ##### Figure 1 : Precip vs. CWV-w_c #######
 #==========================================
-xlim1['f1']=-45 
+xlim1['f1']=-45
 xlim2['f1']=15
 
 ylim1['f1']=0
@@ -156,7 +155,7 @@
 #========================================================
 ##### Figure 2 : Probability of precip vs. CWV-w_c ######
 #========================================================
-xlim1['f2']=-45 
+xlim1['f2']=-45
 xlim2['f2']=15
 
 ylim1['f2']=0
@@ -321,7 +320,7 @@
 fig_params={}
 
 fig_params['f0']=[axes_fontsize,legend_fontsize,marker_size,xtick_pad,figsize1,figsize2]
-for i in ['f1','f2','f3','f4','f5','f6']:    
+for i in ['f1','f2','f3','f4','f5','f6']:
     fig_params[i]=[[xlim1[i],xlim2[i]],[ylim1[i],ylim2[i]],xlabel[i],ylabel[i],xticks[i],yticks[i]]
 
 data["plot_params"]=fig_params

diagnostics/example/example_diag.py

@@ -2,75 +2,75 @@
 # ================================================================================
 # This script does a simple diagnostic calculation to illustrate how to adapt code
 # for use in the MDTF diagnostic framework. The main change is to set input/output
-# paths, variable names etc. from shell environment variables the framework 
+# paths, variable names etc. from shell environment variables the framework
 # provides, instead of hard-coding them.
 #
 # Below, this script consists of 2 parts: (1) a template of comprehensive header POD
-# developers must include in their POD's main driver script, (2) actual code, and 
+# developers must include in their POD's main driver script, (2) actual code, and
 # (3) extensive in-line comments.
 # ================================================================================
-# 
+#
 # This file is part of the Example Diagnostic POD of the MDTF code package (see mdtf/MDTF-diagnostics/LICENSE.txt)
-# 
+#
 # Example Diagnostic POD
-# 
+#
 #   Last update: 8/1/2020
-# 
+#
 #   This is a example POD that you can use as a template for your diagnostics.
-#  If this were a real POD, you'd place a one-paragraph synopsis of your 
-#   diagnostic here (like an abstract). 
-# 
+#  If this were a real POD, you'd place a one-paragraph synopsis of your
+#   diagnostic here (like an abstract).
+#
 #   Version & Contact info
-# 
+#
 #   Here you should describe who contributed to the diagnostic, and who should be
 #   contacted for further information:
-# 
+#
 #   - Version/revision information: version 1 (5/06/2020)
 #   - PI (name, affiliation, email)
 #   - Developer/point of contact (name, affiliation, email)
 #   - Other contributors
-# 
+#
 #   Open source copyright agreement
-# 
-#   The MDTF framework is distributed under the LGPLv3 license (see LICENSE.txt). 
+#
+#   The MDTF framework is distributed under the LGPLv3 license (see LICENSE.txt).
 #   Unless you've distirbuted your script elsewhere, you don't need to change this.
-# 
+#
 #   Functionality
-# 
-#   In this section you should summarize the stages of the calculations your 
-#   diagnostic performs, and how they translate to the individual source code files 
-#   provided in your submission. This will, e.g., let maintainers fixing a bug or 
+#
+#   In this section you should summarize the stages of the calculations your
+#   diagnostic performs, and how they translate to the individual source code files
+#   provided in your submission. This will, e.g., let maintainers fixing a bug or
 #   people with questions about how your code works know where to look.
-# 
+#
 #   Required programming language and libraries
-# 
-#   In this section you should summarize the programming languages and third-party 
-#   libraries used by your diagnostic. You also provide this information in the 
-#   ``settings.jsonc`` file, but here you can give helpful comments to human 
+#
+#   In this section you should summarize the programming languages and third-party
+#   libraries used by your diagnostic. You also provide this information in the
+#   ``settings.jsonc`` file, but here you can give helpful comments to human
 #   maintainers (eg, "We need at least version 1.5 of this library because we call
 #   this function.")
-# 
+#
 #   Required model output variables
-# 
-#   In this section you should describe each variable in the input data your 
-#   diagnostic uses. You also need to provide this in the ``settings.jsonc`` file, 
-#   but here you should go into detail on the assumptions your diagnostic makes 
+#
+#   In this section you should describe each variable in the input data your
+#   diagnostic uses. You also need to provide this in the ``settings.jsonc`` file,
+#   but here you should go into detail on the assumptions your diagnostic makes
 #   about the structure of the data.
-# 
+#
 #   References
-# 
-#   Here you should cite the journal articles providing the scientific basis for 
+#
+#   Here you should cite the journal articles providing the scientific basis for
 #   your diagnostic.
-# 
+#
 #      Maloney, E. D, and Co-authors, 2019: Process-oriented evaluation of climate
 #         and wether forcasting models. BAMS, 100(9), 1665-1686,
 #         doi:10.1175/BAMS-D-18-0042.1.
 #
 import os
 import matplotlib
 matplotlib.use('Agg') # non-X windows backend
-# Commands to load third-party libraries. Any code you don't include that's 
-# not part of your language's standard library should be listed in the 
+# Commands to load third-party libraries. Any code you don't include that's
+# not part of your language's standard library should be listed in the
 # settings.jsonc file.
 import xarray as xr                # python library we use to read netcdf files
 import matplotlib.pyplot as plt    # python library we use to make plots
@@ -80,24 +80,22 @@
 #
 # The framework copies model data to a regular directory structure of the form
 # <DATADIR>/<frequency>/<CASENAME>.<variable_name>.<frequency>.nc
-# Here <variable_name> and frequency are requested in the "varlist" part of 
+# Here <variable_name> and frequency are requested in the "varlist" part of
 # settings.json.
 
 # The following command sets input_path to the value of the shell environment
-# variable called TAS_FILE. This variable is set by the framework to let the 
+# variable called TAS_FILE. This variable is set by the framework to let the
 # script know where the locally downloaded copy of the data for this variable
 # (which we called "tas") is.
 input_path = os.environ["TAS_FILE"]
 
 # command to load the netcdf file
 model_dataset = xr.open_dataset(input_path)
-
-
 ### 2) Doing computations: #####################################################
 #
 # Diagnostics in the framework are intended to work with native output from a
 # variety of models. For this reason, variable names should not be hard-coded
-# but instead set from environment variables. 
+# but instead set from environment variables.
 #
 tas_var_name = os.environ["tas_var"]
 # For safety, don't even assume that the time dimension of the input file is
@@ -117,10 +115,10 @@
 
 ### 3) Saving output data: #####################################################
 #
-# Diagnostics should write output data to disk to a) make relevant results 
+# Diagnostics should write output data to disk to a) make relevant results
 # available to the user for further use or b) to pass large amounts of data
 # between stages of a calculation run as different sub-scripts. Data can be in
-# any format (as long as it's documented) and should be written to the 
+# any format (as long as it's documented) and should be written to the
 # directory <WK_DIR>/model/netCDF (created by the framework).
 #
 out_path = "{WK_DIR}/model/netCDF/temp_means.nc".format(**os.environ)
@@ -131,9 +129,9 @@
 
 ### 4) Saving output plots: ####################################################
 #
-# Plots should be saved in EPS or PS format at <WK_DIR>/<model or obs>/PS 
-# (created by the framework). Plots can be given any filename, but should have 
-# the extension ".eps" or ".ps". To make the webpage output, the framework will 
+# Plots should be saved in EPS or PS format at <WK_DIR>/<model or obs>/PS
+# (created by the framework). Plots can be given any filename, but should have
+# the extension ".eps" or ".ps". To make the webpage output, the framework will
 # convert these to bitmaps with the same name but extension ".png".
 
 # Define a python function to make the plot, since we'll be doing it twice and
@@ -160,14 +158,14 @@ def plot_and_save_figure(model_or_obs, title_string, dataset):
 
 ### 5) Loading obs data files & plotting obs figures: ##########################
 #
-# If your diagnostic uses any model-independent supporting data (eg. reference 
+# If your diagnostic uses any model-independent supporting data (eg. reference
 # or observational data) larger than a few kB of text, it should be provided via
 # the observational data distribution instead of being included with the source
-# code. This data can be in any format: the framework doesn't process it. The 
+# code. This data can be in any format: the framework doesn't process it. The
 # environment variable OBS_DATA will be set to a path where the framework has
 # copied a directory containing your supplied data.
 #
-# The following command replaces the substring "{OBS_DATA}" with the value of 
+# The following command replaces the substring "{OBS_DATA}" with the value of
 # the OBS_DATA environment variable.
 input_path = "{OBS_DATA}/example_tas_means.nc".format(**os.environ)
 

diagnostics/flow_dep_diag/ClimAnom_func.py

@@ -0,0 +1,23 @@
+import os
+import xarray as xr
+import numpy as np
+from datetime import datetime
+np.seterr(divide='ignore', invalid='ignore')
+
+### Function to calculate climatology anomalies for each variable in the diagnostic.
+
+time_var = os.environ["time_coord"] #set environment variable for time equal to var for function
+#function to calculate climatology anomalies for eah variable
+def climAnom(var_path, var_name):
+
+    ds = xr.open_dataset(var_path, decode_times = True)
+    ds[time_var] = ds.indexes[time_var].to_datetimeindex() #convert time to datetime so we can use groupby functionality
+    # Drop 1 dimensional coordinates
+    ds = ds.squeeze()
+    da = ds[var_name]
+    da_ensmean = da.copy()
+    da_day_clim = da_ensmean.groupby('{time_coord}.dayofyear'.format(**os.environ)).mean(time_var)
+    da_day_anom = da.groupby('{time_coord}.dayofyear'.format(**os.environ)) - da_day_clim
+    da_day_anom = da_day_anom.drop('dayofyear')
+    da_day_anom.to_netcdf("{WK_DIR}/model/netCDF/".format(**os.environ) + var_name + "_climAnom.nc")
+    return da_day_anom