Note
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UserScript: Calculate the Difficulty Index
model_applications/medium_range/ UserScript_fcstGEFS _Difficulty_Index.conf
Scientific Objective
This use case calls the UserScript wrapper to run a user provided script that calculates the difficulty index for windspeed. This use case allows for the user to change a variety of variables needed to run the difficulty index (i.e. threshold start and units) so that user can run the script at different thresholds without needing to alter the code. This script run by the use case uses METcalcpy to provide the difficulty index calculation and METplotpy to provide the plotting capability.
The difficulty index was developed by the Naval Research Lab (NRL). The overall aim of the difficulty index is to graphically represent the expected difficulty of a decision based on a set of forecasts (ensemble) of, e.g., significant wave height as a function of space and time. There are two basic factors that can make a decision difficult. The first factor is the proximity of the ensemble mean forecast to a decision threshold, e.g. 12 ft seas. If the ensemble mean is either much lower or much higher than the threshold, the decision is easier; if it is closer to the threshold, the decision is harder. The second factor is the forecast precision, or ensemble spread. The greater the spread around the ensemble mean, the more likely it is that there will be ensemble members both above and below the decision threshold, making the decision harder. (A third factor that we will not address here is undiagnosed systematic error, which adds uncertainty in a similar way to ensemble spread.) The challenge is combining these factors into a continuous function that allows the user to assess relative risk.
Datasets
This use case calculates the difficulty index for windspeed using NCEP GEFS ensemble data. The data is composed of 30 ensemble members that have been compiled and compressed into one .npz file.
Variables required to calculate the difficulty index: Levels required: 10-m #. v- component of wind #. u- component of wind #. Windspeed #. Latitude #. Longitude
Forecast dataset: NCEP GEFS 30 member Ensemble - Initialization date: 20191208 - Initialization hours: 12 UTC - Lead times: 60 - Format: Grib2 - Resolution: 0.5 degree
METplus Components
This use case runs the UserScript wrapper tool to run a user provided script, in this case, wind_difficulty_index.py.
METplus Workflow
This use case loops by process which means that each tool is run for all times before moving to the next tool. The tool order is as follows:
UserScript
This example loops by initialization time (with begin, end, and increment as specified in the METplus UserScript_fcstGEFS_Difficulty_Index.conf file).
1 initialization time will be run over 1 lead time:
METplus Configuration
METplus first loads all of the configuration files found in parm/metplus_config, then it loads any configuration files passed to METplus via the command line with the -c option, i.e. -c parm/use_cases/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index.conf
[config]
# Documentation for this use case can be found at
# https://metplus.readthedocs.io/en/latest/generated/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index.html
# For additional information, please see the METplus Users Guide.
# https://metplus.readthedocs.io/en/latest/Users_Guide
###
# Processes to run
# https://metplus.readthedocs.io/en/latest/Users_Guide/systemconfiguration.html#process-list
###
PROCESS_LIST = UserScript
###
# Time Info
# LOOP_BY options are INIT, VALID, RETRO, and REALTIME
# If set to INIT or RETRO:
# INIT_TIME_FMT, INIT_BEG, INIT_END, and INIT_INCREMENT must also be set
# If set to VALID or REALTIME:
# VALID_TIME_FMT, VALID_BEG, VALID_END, and VALID_INCREMENT must also be set
# LEAD_SEQ is the list of forecast leads to process
# https://metplus.readthedocs.io/en/latest/Users_Guide/systemconfiguration.html#timing-control
###
LOOP_BY = INIT
INIT_TIME_FMT = %Y%m%d%H
INIT_BEG = 2020120812
INIT_END = 2020120812
INIT_INCREMENT = 12H
LEAD_SEQ =
USER_SCRIPT_CUSTOM_LOOP_LIST = nc
USER_SCRIPT_RUNTIME_FREQ = RUN_ONCE_FOR_EACH
###
# File I/O
# https://metplus.readthedocs.io/en/latest/Users_Guide/systemconfiguration.html#directory-and-filename-template-info
###
USER_SCRIPT_INPUT_DIR = {INPUT_BASE}/model_applications/medium_range/diff_index
USER_SCRIPT_INPUT_TEMPLATE = {USER_SCRIPT_INPUT_DIR}/wndspd_GEFS_NorthPac_5dy_30mem_{init?fmt=%Y%m%d%H}.npz
USER_SCRIPT_OUTPUT_DIR = {OUTPUT_BASE}/model_applications/medium_range/diff_index
###
# UserScript Settings
# https://metplus.readthedocs.io/en/latest/Users_Guide/wrappers.html#userscript
###
USER_SCRIPT_COMMAND = {PARM_BASE}/use_cases/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index/wind_difficulty_index.py
[user_env_vars]
# Difficulty index specific variables
DIFF_INDEX_INPUT_FILENAME = {USER_SCRIPT_INPUT_TEMPLATE}
DIFF_INDEX_THRESH_START = 10.0
DIFF_INDEX_THRESH_END = 40.0
DIFF_INDEX_THRESH_STEP = 2.0
DIFF_INDEX_SAVE_THRESH_START = 20.0
DIFF_INDEX_SAVE_THRESH_STOP = 38.0
DIFF_INDEX_SAVE_THRESH_STEP = 2.0
DIFF_INDEX_UNITS = kn
DIFF_INDEX_FIG_FMT = png
DIFF_INDEX_FIG_BASENAME = {USER_SCRIPT_OUTPUT_DIR}/wndspd_GEFS_NorthPac_5dy_30mem_difficulty_index
MET Configuration
There are no MET tools used in this use case.
Python Embedding
This use case uses a Python embedding script to read input data
parm/use_cases/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index/wind_difficulty_index.py
#!/usr/bin/env python3
"""
Load fieldijn from npz file created with save_ensemble_data.py
helper function, compute ensemble mean and spread, compute
difficulty index for a set of thresholds, plot and save the results.
Author: Bill Campbell, NRL and Lindsay Blank, NCAR
Taken from original test_difficulty_index.py but replacing with METcalcpy and METplotpy.
"""
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from metcalcpy.calc_difficulty_index import forecast_difficulty as di
from metcalcpy.calc_difficulty_index import EPS
from metcalcpy.piecewise_linear import PiecewiseLinear as plin
import metplotpy.plots.difficulty_index.mycolormaps as mcmap
from metplotpy.plots.difficulty_index.plot_difficulty_index import plot_field
def load_data(filename):
"""Load ensemble data from file"""
loaded = np.load(filename)
lats, lons = (loaded['lats'], loaded['lons'])
fieldijn = np.ma.masked_invalid(
np.ma.masked_array(
data=loaded['data']))
return lats, lons, fieldijn
def compute_stats(field):
"""Compute mean and std dev"""
mu = np.mean(field, axis=-1)
sigma = np.std(field, axis=-1, ddof=1)
return mu, sigma
def compute_wind_envelope():
"""
Computes piecewise linear envelope for winds in knots.
Returns
-------
Piecewise linear object
"""
# Envelope for version 6.1, the default
xunits = 'kn'
A6_1_name = "A6_1"
A6_1_left = 0.0
A6_1_right = 0.0
A6_1_xlist = [5.0, 28.0, 34.0, 50.0]
A6_1_ylist = [0.0, 1.5, 1.5, 0.0]
Aplin =\
plin(A6_1_xlist, A6_1_ylist, xunits=xunits,
right=A6_1_right, left=A6_1_left, name=A6_1_name)
return Aplin
def compute_difficulty_index(field, mu, sigma, thresholds, Aplin):
"""
Compute difficulty index for an ensemble forecast given
a set of thresholds, returning a dictionary of fields.
"""
dij = {}
for threshold in thresholds:
dij[threshold] =\
di(sigma, mu, threshold, field, Aplin=Aplin, sigma_over_mu_ref=EPS)
return dij
def plot_difficulty_index(dij, lats, lons, thresholds, units):
"""
Plot the difficulty index for a set of thresholds,
returning a dictionary of figures
"""
plt.close('all')
myparams = {'figure.figsize': (8, 5),
'figure.max_open_warning': 40}
plt.rcParams.update(myparams)
figs = {}
cmap = mcmap.stoplight()
for threshold in thresholds:
if np.max(dij[threshold]) <= 1.0:
vmax = 1.0
else:
vmax = 1.5
figs[threshold] =\
plot_field(dij[threshold],
lats, lons, vmin=0.0, vmax=vmax, cmap=cmap,
xlab='Longitude \u00b0E', ylab='Latitude',
clab='thresh={} {}'.format(threshold, units),
title='Forecast Decision Difficulty Index')
return figs
def save_difficulty_figures(figs, save_thresh, units):
"""
Save subset of difficulty index figures.
"""
fig_fmt = os.environ.get('DIFF_INDEX_FIG_FMT')
fig_basename = os.environ.get('DIFF_INDEX_FIG_BASENAME')
# create output directory if it does not already exist
output_dir = os.path.dirname(fig_basename)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for thresh in save_thresh:
thresh_str = '{:.2f}'.format(thresh).replace('.', '_')
fig_name = (fig_basename + thresh_str +
'_' + units + '.' + fig_fmt)
print('Saving {}...\n'.format(fig_name))
figs[thresh].savefig(fig_name, format=fig_fmt)
def plot_statistics(mu, sigma, lats, lons, units='feet'):
"""Plot ensemble mean and spread, returning figure handles"""
cmap = mcmap.spectral()
mu_fig =\
plot_field(mu, lats, lons, cmap=cmap, clab=units,
vmin=0.0, vmax=np.nanmax(mu),
xlab='Longitude \u00b0E',
ylab='Latitude',
title='Forecast Ensemble Mean')
sigma_fig =\
plot_field(sigma, lats, lons, cmap=cmap, clab=units,
vmin=0.0, vmax=np.nanmax(sigma),
xlab='Longitude \u00b0E',
ylab='Latitude',
title='Forecast Ensemble Std')
return mu_fig, sigma_fig
def save_stats_figures(mu_fig, sigma_fig):
"""
Save ensemble mean and spread figures.
"""
fig_fmt = os.environ.get('DIFF_INDEX_FIG_FMT')
fig_basename = os.environ.get('DIFF_INDEX_FIG_BASENAME')
mu_name = fig_basename + 'mean.' + fig_fmt
print('Saving {}...\n'.format(mu_name))
mu_fig.savefig(mu_name, format=fig_fmt)
sigma_name = fig_basename + 'std.' + fig_fmt
print('Saving {}...\n'.format(sigma_name))
sigma_fig.savefig(sigma_name, format=fig_fmt)
def main():
"""
Load fieldijn from npz file created with NCEP_test.py
helper function, compute ensemble mean and spread, compute
difficulty index for a set of thresholds, plot and save the results.
"""
filename = os.environ.get('DIFF_INDEX_INPUT_FILENAME')
lats, lons, fieldijn = load_data(filename)
# Convert m/s to knots
units = os.environ.get('DIFF_INDEX_UNITS')
mps2kn = 1.94384
fieldijn = mps2kn * fieldijn
# Ensemble mean, std dev
muij, sigmaij = compute_stats(fieldijn)
# Windspeed envelope
Aplin = compute_wind_envelope()
# Difficulty index for a set of thresholds
#thresholds = np.arange(os.environ.get('DIFF_INDEX_THRESH_START'), os.environ.get('DIFF_INDEX_THRESH_END'), os.environ.get('DIFF_INDEX_THRESH_STEP'))
start = float(os.environ.get('DIFF_INDEX_THRESH_START'))
stop = float(os.environ.get('DIFF_INDEX_THRESH_END'))
step = float(os.environ.get('DIFF_INDEX_THRESH_STEP'))
thresholds = np.arange(start, stop, step)
dij = compute_difficulty_index(fieldijn, muij, sigmaij, thresholds, Aplin=Aplin)
# Plot and save difficulty index figures
figs = plot_difficulty_index(dij, lats, lons, thresholds, units)
save_start = float(os.environ.get('DIFF_INDEX_SAVE_THRESH_START'))
save_stop = float(os.environ.get('DIFF_INDEX_SAVE_THRESH_STOP'))
save_step = float(os.environ.get('DIFF_INDEX_SAVE_THRESH_STEP'))
save_thresh = np.arange(save_start, save_stop, save_step)
save_difficulty_figures(figs, save_thresh, units)
# Plot and save ensemble mean, std_dev
mu_fig, sigma_fig =\
plot_statistics(muij, sigmaij, lats, lons, units=units)
save_stats_figures(mu_fig, sigma_fig)
if __name__ == '__main__':
main()
Running METplus
This use case can be run two ways:
1) Passing in UserScript_fcstGEFS_Difficulty_Index.conf, then a user-specific system configuration file:
run_metplus.py \
-c /path/to/METplus/parm/use_cases/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index.conf \
-c /path/to/user_system.conf
Modifying the configurations in parm/metplus_config, then passing in UserScript_fcstGEFS_Difficulty_Index.conf:
run_metplus.py \ -c /path/to/METplus/parm/use_cases/model_applications/medium_range/UserScript_fcstGEFS_Difficulty_Index.conf
The former method is recommended. Whether you add them to a user-specific configuration file or modify the metplus_config files, the following variables must be set correctly:
INPUT_BASE - Path to directory where sample data tarballs are unpacked (See Datasets section to obtain tarballs). This is not required to run METplus, but it is required to run the examples in parm/use_cases
OUTPUT_BASE - Path where METplus output will be written. This must be in a location where you have write permissions
MET_INSTALL_DIR - Path to location where MET is installed locally
and for the [exe] section, you will need to define the location of NON-MET executables. If the executable is in the user’s path, METplus will find it from the name. If the executable is not in the path, specify the full path to the executable here (i.e. RM = /bin/rm) The following executables are required for performing series analysis use cases:
Example User Configuration File:
[dir]
INPUT_BASE = /path/to/sample/input/data
OUTPUT_BASE = /path/to/output/dir
MET_INSTALL_DIR = /path/to/met-X.Y
[exe]
RM = /path/to/rm
CUT = /path/to/cut
TR = /path/to/tr
NCAP2 = /path/to/ncap2
CONVERT = /path/to/convert
NCDUMP = /path/to/ncdump
Expected Output
A successful run will output the following both to the screen and to the logfile:
INFO: METplus has successfully finished running.
Refer to the value set for OUTPUT_BASE to find where the output data was generated. Output for this use case will be found in a directory relative to OUTPUT_BASE. There should be a list of files that have the following format:
wndspd_GEFS_NorthPac_5dy_30mem_difficulty_indexTHRESH_00_kn.png
Where THRESH isa number between DIFF_INDEX_SAVE_THRESH_START and DIFF_INDEX_SAVE_THRESH_STOP which are defined in UserScript_fcstGEFS_Difficulty_Index.conf.
Keywords
Note
UserScriptUseCase
MediumRangeAppUseCase
NRLOrgUseCase
Navigate to the METplus Quick Search for Use Cases page to discover other similar use cases.
sphinx_gallery_thumbnail_path = ‘_static/medium_range-UserScript_fcstGEFS_Difficulty_Index.png’
Total running time of the script: ( 0 minutes 0.000 seconds)