Note
Go to the end to download the full example code.
GridStat: Python Embedding for sea surface salinity using level 3, 1 day composite obs
model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss.conf
Scientific Objective
This use case utilizes Python embedding to extract several statistics from the sea surface salinity data over the globe, which was already being done in a closed system. By producing the same output via METplus, this use case provides standardization and reproducible results.
Version Added
METplus version 6.0
Datasets
Forecast: NOAA Real-Time Ocean Forecast System (RTOFS) Sea Surface Salinity (sss) file via Python Embedding script/file
Observations: Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (sss) file via Python Embedding script/file
Sea Ice Masking: NOAA Real-Time Ocean Forecast System (RTOFS) ice cover file via Python Embedding script/file
Climatology: World Ocean Atlas (WOA) Sea Surface Salinity (sss) file via Python Embedding script/file
Location: All of the input data required for this use case can be found in a sample data tarball. Each use case category will have one or more sample data tarballs. It is only necessary to download the tarball with the use case’s dataset and not the entire collection of sample data. Click here to access the METplus releases page and download sample data for the appropriate release: https://github.com/dtcenter/METplus/releases This tarball should be unpacked into the directory that you will set the value of INPUT_BASE. See Running METplus section for more information.
External Dependencies
You will need to use a version of Python 3.6+ that has the following packages installed:
scikit-learn
pyresample
If the version of Python used to compile MET did not have these libraries at the time of compilation, you will need to add these packages or create a new Python environment with these packages.
If this is the case, you will need to set the MET_PYTHON_EXE environment variable to the path of the version of Python you want to use. If you want this version of Python to only apply to this use case, set it in the [user_env_vars] section of a METplus configuration file.:
[user_env_vars] MET_PYTHON_EXE = /path/to/python/with/required/packages/bin/python
METplus Components
This use case utilizes the METplus GridStat wrapper to generate a command to run the MET tool GridStat with Python Embedding for the specified user hemispheres
METplus Workflow
Beginning time (VALID_BEG): 20210503
End time (VALID_END): 20210503
Increment between beginning and end times (VALID_INCREMENT): 1M
Sequence of forecast leads to process (LEAD_SEQ): 0
GridStat is the only tool called in this example. This use case will pass in both the observation, forecast, and climatology gridded data being pulled from the files via Python Embedding. All of the desired statistics reside in the CNT line type, so that is the only output requested. It processes the following run time:
Valid: 2021-05-03 0Z
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, i.e. parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss.conf
[config]
# Documentation for this use case can be found at
# https://metplus.readthedocs.io/en/latest/generated/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss.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 = GridStat
###
# 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 = VALID
VALID_TIME_FMT = %Y%m%d
VALID_BEG=20210503
VALID_END=20210503
VALID_INCREMENT = 1M
LEAD_SEQ = 0
###
# File I/O
# https://metplus.readthedocs.io/en/latest/Users_Guide/systemconfiguration.html#directory-and-filename-template-info
###
FCST_GRID_STAT_INPUT_TEMPLATE = PYTHON_NUMPY
OBS_GRID_STAT_INPUT_TEMPLATE = PYTHON_NUMPY
GRID_STAT_CLIMO_MEAN_INPUT_TEMPLATE = PYTHON_NUMPY
GRID_STAT_OUTPUT_DIR = {OUTPUT_BASE}
GRID_STAT_OUTPUT_TEMPLATE = {valid?fmt=%Y%m%d}
###
# Field Info
# https://metplus.readthedocs.io/en/latest/Users_Guide/systemconfiguration.html#field-info
###
GRID_STAT_ONCE_PER_FIELD = False
CONFIG_DIR = {PARM_BASE}/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss
FCST_VAR1_NAME = {CONFIG_DIR}/read_rtofs_smos_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/SMOS-L3-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss {valid?fmt=%Y%m%d} fcst
OBS_VAR1_NAME = {CONFIG_DIR}/read_rtofs_smos_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/SMOS-L3-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss {valid?fmt=%Y%m%d} obs
GRID_STAT_CLIMO_MEAN_FIELD = {name="{CONFIG_DIR}/read_rtofs_smos_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/SMOS-L3-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss {valid?fmt=%Y%m%d} climo"; level="(*,*)";}
###
# GridStat Settings
# https://metplus.readthedocs.io/en/latest/Users_Guide/wrappers.html#gridstat
###
GRID_STAT_REGRID_TO_GRID = NONE
MODEL = RTOFS
OBTYPE = SMOS
GRID_STAT_NEIGHBORHOOD_WIDTH = 1
GRID_STAT_NEIGHBORHOOD_SHAPE = SQUARE
GRID_STAT_NEIGHBORHOOD_COV_THRESH = >=0.5
GRID_STAT_OUTPUT_PREFIX = SSS
GRID_STAT_OUTPUT_FLAG_CNT = BOTH
MET Configuration
METplus sets environment variables based on user settings in the METplus configuration file. See How METplus controls MET config file settings for more details.
YOU SHOULD NOT SET ANY OF THESE ENVIRONMENT VARIABLES YOURSELF! THEY WILL BE OVERWRITTEN BY METPLUS WHEN IT CALLS THE MET TOOLS!
If there is a setting in the MET configuration file that is currently not supported by METplus you’d like to control, please refer to: Overriding Unsupported MET config file settings
GridStatConfig_wrapped
////////////////////////////////////////////////////////////////////////////////
//
// Grid-Stat configuration file.
//
// For additional information, see the MET_BASE/config/README file.
//
////////////////////////////////////////////////////////////////////////////////
//
// Output model name to be written
//
// model =
${METPLUS_MODEL}
//
// Output description to be written
// May be set separately in each "obs.field" entry
//
// desc =
${METPLUS_DESC}
//
// Output observation type to be written
//
// obtype =
${METPLUS_OBTYPE}
////////////////////////////////////////////////////////////////////////////////
//
// Verification grid
//
// regrid = {
${METPLUS_REGRID_DICT}
////////////////////////////////////////////////////////////////////////////////
//censor_thresh =
${METPLUS_CENSOR_THRESH}
//censor_val =
${METPLUS_CENSOR_VAL}
//cat_thresh =
${METPLUS_CAT_THRESH}
cnt_thresh = [ NA ];
cnt_logic = UNION;
wind_thresh = [ NA ];
wind_logic = UNION;
eclv_points = 0.05;
//nc_pairs_var_name =
${METPLUS_NC_PAIRS_VAR_NAME}
nc_pairs_var_suffix = "";
//hss_ec_value =
${METPLUS_HSS_EC_VALUE}
rank_corr_flag = FALSE;
//
// Forecast and observation fields to be verified
//
fcst = {
${METPLUS_FCST_FILE_TYPE}
${METPLUS_FCST_FIELD}
${METPLUS_FCST_CLIMO_MEAN_DICT}
${METPLUS_FCST_CLIMO_STDEV_DICT}
}
obs = {
${METPLUS_OBS_FILE_TYPE}
${METPLUS_OBS_FIELD}
${METPLUS_OBS_CLIMO_MEAN_DICT}
${METPLUS_OBS_CLIMO_STDEV_DICT}
}
////////////////////////////////////////////////////////////////////////////////
//
// Climatology mean data
//
//climo_mean = {
${METPLUS_CLIMO_MEAN_DICT}
//climo_stdev = {
${METPLUS_CLIMO_STDEV_DICT}
//
// May be set separately in each "obs.field" entry
//
//climo_cdf = {
${METPLUS_CLIMO_CDF_DICT}
////////////////////////////////////////////////////////////////////////////////
//
// Verification masking regions
//
// mask = {
${METPLUS_MASK_DICT}
////////////////////////////////////////////////////////////////////////////////
//
// Confidence interval settings
//
ci_alpha = [ 0.05 ];
boot = {
interval = PCTILE;
rep_prop = 1.0;
n_rep = 0;
rng = "mt19937";
seed = "";
}
////////////////////////////////////////////////////////////////////////////////
//
// Data smoothing methods
//
//interp = {
${METPLUS_INTERP_DICT}
////////////////////////////////////////////////////////////////////////////////
//
// Neighborhood methods
//
nbrhd = {
field = BOTH;
// shape =
${METPLUS_NBRHD_SHAPE}
// width =
${METPLUS_NBRHD_WIDTH}
// cov_thresh =
${METPLUS_NBRHD_COV_THRESH}
vld_thresh = 1.0;
}
////////////////////////////////////////////////////////////////////////////////
//
// Fourier decomposition
// May be set separately in each "obs.field" entry
//
//fourier = {
${METPLUS_FOURIER_DICT}
////////////////////////////////////////////////////////////////////////////////
//
// Gradient statistics
// May be set separately in each "obs.field" entry
//
//gradient = {
${METPLUS_GRADIENT_DICT}
////////////////////////////////////////////////////////////////////////////////
//
// Distance Map statistics
// May be set separately in each "obs.field" entry
//
//distance_map = {
${METPLUS_DISTANCE_MAP_DICT}
////////////////////////////////////////////////////////////////////////////////
// Threshold for SEEPS p1 (Probability of being dry)
//seeps_p1_thresh =
${METPLUS_SEEPS_P1_THRESH}
////////////////////////////////////////////////////////////////////////////////
//
// Statistical output types
//
//output_flag = {
${METPLUS_OUTPUT_FLAG_DICT}
//
// NetCDF matched pairs output file
// May be set separately in each "obs.field" entry
//
// nc_pairs_flag = {
${METPLUS_NC_PAIRS_FLAG_DICT}
////////////////////////////////////////////////////////////////////////////////
//ugrid_dataset =
${METPLUS_UGRID_DATASET}
//ugrid_max_distance_km =
${METPLUS_UGRID_MAX_DISTANCE_KM}
//ugrid_coordinates_file =
${METPLUS_UGRID_COORDINATES_FILE}
////////////////////////////////////////////////////////////////////////////////
//grid_weight_flag =
${METPLUS_GRID_WEIGHT_FLAG}
tmp_dir = "${MET_TMP_DIR}";
// output_prefix =
${METPLUS_OUTPUT_PREFIX}
////////////////////////////////////////////////////////////////////////////////
${METPLUS_TIME_OFFSET_WARNING}
${METPLUS_MET_CONFIG_OVERRIDES}
Python Embedding
This use case uses one Python script to read forecast and observation data
parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss/read_rtofs_smos_woa.py
#!/bin/env python
"""
Code adapted from
Todd Spindler
NOAA/NWS/NCEP/EMC
Designed to read in RTOFS,SMOS,WOA and OSTIA data
and based on user input, read sss data
and pass back in memory the forecast, observation, or climatology
data field
"""
import numpy as np
import xarray as xr
import pandas as pd
import pyresample as pyr
from pandas.tseries.offsets import DateOffset
from datetime import datetime, timedelta
from sklearn.metrics import mean_squared_error
import io
from glob import glob
import warnings
import os, sys
if len(sys.argv) < 6:
print("Must specify the following elements: fcst_file obs_file ice_file, climo_file, valid_date, file_flag")
sys.exit(1)
rtofsfile = os.path.expandvars(sys.argv[1])
sssfile = os.path.expandvars(sys.argv[2])
icefile = os.path.expandvars(sys.argv[3])
climoDir = os.path.expandvars(sys.argv[4])
vDate=datetime.strptime(sys.argv[5],'%Y%m%d')
file_flag = sys.argv[6]
print('Starting Satellite SMOS V&V at',datetime.now(),'for',vDate, ' file_flag:',file_flag)
pd.date_range(vDate,vDate)
platform='SMOS'
param='sss'
#####################################################################
# READ SMOS data ##################################################
#####################################################################
if not os.path.exists(sssfile):
print('missing SMOS file for',vDate)
sss_data=xr.open_dataset(sssfile,decode_times=True)
sss_data['time']=sss_data.time-pd.Timedelta('12H') # shift 12Z offset time to 00Z
sss_data2=sss_data['sss'].astype('single')
print('Retrieved SMOS data from NESDIS for',sss_data2.time.values)
sss_data2=sss_data2.rename({'longitude':'lon','latitude':'lat'})
# all coords need to be single precision
sss_data2['lon']=sss_data2.lon.astype('single')
sss_data2['lat']=sss_data2.lat.astype('single')
sss_data2.attrs['platform']=platform
sss_data2.attrs['units']='PSU'
#####################################################################
# READ RTOFS data (model output in Tri-polar coordinates) ###########
#####################################################################
print('reading rtofs ice')
if not os.path.exists(rtofsfile):
print('missing rtofs file',rtofsfile)
sys.exit(1)
indata=xr.open_dataset(rtofsfile,decode_times=True)
indata=indata.mean(dim='MT')
indata = indata[param][:-1,]
indata.coords['time']=vDate
#indata.coords['fcst']=fcst
outdata=indata.copy()
outdata=outdata.rename({'Longitude':'lon','Latitude':'lat',})
# all coords need to be single precision
outdata['lon']=outdata.lon.astype('single')
outdata['lat']=outdata.lat.astype('single')
outdata.attrs['platform']='rtofs '+platform
#####################################################################
# READ CLIMO WOA data - May require 2 files depending on the date ###
#####################################################################
if not os.path.exists(climoDir):
print('missing climo file file for',vDate)
vDate=pd.Timestamp(vDate)
climofile="woa13_decav_s{:02n}_04v2.nc".format(vDate.month)
climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
climo_data=climo_data['s_an'].squeeze()[0,]
if vDate.day==15: # even for Feb, just because
climofile="woa13_decav_s{:02n}_04v2.nc".format(vDate.month)
climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
climo_data=climo_data['s_an'].squeeze()[0,] # surface only
else:
if vDate.day < 15:
start=vDate - DateOffset(months=1,day=15)
stop=pd.Timestamp(vDate.year,vDate.month,15)
else:
start=pd.Timestamp(vDate.year,vDate.month,15)
stop=vDate + DateOffset(months=1,day=15)
left=(vDate-start)/(stop-start)
climofile1="woa13_decav_s{:02n}_04v2.nc".format(start.month)
climofile2="woa13_decav_s{:02n}_04v2.nc".format(stop.month)
climo_data1=xr.open_dataset(climoDir+'/'+climofile1,decode_times=False)
climo_data2=xr.open_dataset(climoDir+'/'+climofile2,decode_times=False)
climo_data1=climo_data1['s_an'].squeeze()[0,] # surface only
climo_data2=climo_data2['s_an'].squeeze()[0,] # surface only
print('climofile1 :', climofile1)
print('climofile2 :', climofile2)
climo_data=climo_data1+((climo_data2-climo_data1)*left)
climofile='weighted average of '+climofile1+' and '+climofile2
# all coords need to be single precision
climo_data['lon']=climo_data.lon.astype('single')
climo_data['lat']=climo_data.lat.astype('single')
climo_data.attrs['platform']='woa'
climo_data.attrs['filename']=climofile
#####################################################################
# READ ICE data for masking #########################################
#####################################################################
if not os.path.exists(icefile):
print('missing OSTIA ice file for',vDate)
ice_data=xr.open_dataset(icefile,decode_times=True)
ice_data=ice_data.rename({'sea_ice_fraction':'ice'})
# all coords need to be single precision
ice_data2=ice_data.ice.astype('single')
ice_data2['lon']=ice_data2.lon.astype('single')
ice_data2['lat']=ice_data2.lat.astype('single')
def regrid(model,obs):
"""
regrid data to obs -- this assumes DataArrays
"""
model2=model.copy()
model2_lon=model2.lon.values
model2_lat=model2.lat.values
model2_data=model2.to_masked_array()
if model2_lon.ndim==1:
model2_lon,model2_lat=np.meshgrid(model2_lon,model2_lat)
obs2=obs.copy()
obs2_lon=obs2.lon.astype('single').values
obs2_lat=obs2.lat.astype('single').values
obs2_data=obs2.astype('single').to_masked_array()
if obs2.lon.ndim==1:
obs2_lon,obs2_lat=np.meshgrid(obs2.lon.values,obs2.lat.values)
model2_lon1=pyr.utils.wrap_longitudes(model2_lon)
model2_lat1=model2_lat.copy()
obs2_lon1=pyr.utils.wrap_longitudes(obs2_lon)
obs2_lat1=obs2_lat.copy()
# pyresample gausssian-weighted kd-tree interp
# define the grids
orig_def = pyr.geometry.GridDefinition(lons=model2_lon1,lats=model2_lat1)
targ_def = pyr.geometry.GridDefinition(lons=obs2_lon1,lats=obs2_lat1)
radius=50000
sigmas=25000
model2_data2=pyr.kd_tree.resample_gauss(orig_def,model2_data,targ_def,
radius_of_influence=radius,
sigmas=sigmas,
fill_value=None)
model=xr.DataArray(model2_data2,coords=[obs.lat.values,obs.lon.values],dims=['lat','lon'])
return model
def expand_grid(data):
"""
concatenate global data for edge wraps
"""
data2=data.copy()
data2['lon']=data2.lon+360
data3=xr.concat((data,data2),dim='lon')
return data3
sss_data2=sss_data2.squeeze()
print('regridding climo to obs')
climo_data=climo_data.squeeze()
climo_data=regrid(climo_data,sss_data2)
print('regridding ice to obs')
ice_data2=regrid(ice_data2,sss_data2)
print('regridding model to obs')
model2=regrid(outdata,sss_data2)
# combine obs ice mask with ncep
obs2=sss_data2.to_masked_array()
ice2=ice_data2.to_masked_array()
climo2=climo_data.to_masked_array()
model2=model2.to_masked_array()
#reconcile with obs
obs2.mask=np.ma.mask_or(obs2.mask,ice2>0.0)
obs2.mask=np.ma.mask_or(obs2.mask,climo2.mask)
obs2.mask=np.ma.mask_or(obs2.mask,model2.mask)
climo2.mask=obs2.mask
model2.mask=obs2.mask
obs2=xr.DataArray(obs2,coords=[sss_data2.lat.values,sss_data2.lon.values], dims=['lat','lon'])
model2=xr.DataArray(model2,coords=[sss_data2.lat.values,sss_data2.lon.values], dims=['lat','lon'])
climo2=xr.DataArray(climo2,coords=[sss_data2.lat.values,sss_data2.lon.values], dims=['lat','lon'])
model2=expand_grid(model2)
climo2=expand_grid(climo2)
obs2=expand_grid(obs2)
#Create the MET grids based on the file_flag
if file_flag == 'fcst':
met_data = model2[:,:]
#trim the lat/lon grids so they match the data fields
lat_met = model2.lat
lon_met = model2.lon
print(" RTOFS Data shape: "+repr(met_data.shape))
v_str = vDate.strftime("%Y%m%d")
v_str = v_str + '_000000'
lat_ll = float(lat_met.min())
lon_ll = float(lon_met.min())
n_lat = lat_met.shape[0]
n_lon = lon_met.shape[0]
delta_lat = (float(lat_met.max()) - float(lat_met.min()))/float(n_lat)
delta_lon = (float(lon_met.max()) - float(lon_met.min()))/float(n_lon)
print(f"variables:"
f"lat_ll: {lat_ll} lon_ll: {lon_ll} n_lat: {n_lat} n_lon: {n_lon} delta_lat: {delta_lat} delta_lon: {delta_lon}")
met_data.attrs = {
'valid': v_str,
'init': v_str,
'lead': "00",
'accum': "00",
'name': 'sss',
'standard_name': 'sss',
'long_name': 'sss',
'level': "SURFACE",
'units': "degC",
'grid': {
'type': "LatLon",
'name': "RTOFS Grid",
'lat_ll': lat_ll,
'lon_ll': lon_ll,
'delta_lat': delta_lat,
'delta_lon': delta_lon,
'Nlat': n_lat,
'Nlon': n_lon,
}
}
attrs = met_data.attrs
if file_flag == 'obs':
met_data = obs2[:,:]
#trim the lat/lon grids so they match the data fields
lat_met = obs2.lat
lon_met = obs2.lon
v_str = vDate.strftime("%Y%m%d")
v_str = v_str + '_000000'
lat_ll = float(lat_met.min())
lon_ll = float(lon_met.min())
n_lat = lat_met.shape[0]
n_lon = lon_met.shape[0]
delta_lat = (float(lat_met.max()) - float(lat_met.min()))/float(n_lat)
delta_lon = (float(lon_met.max()) - float(lon_met.min()))/float(n_lon)
print(f"variables:"
f"lat_ll: {lat_ll} lon_ll: {lon_ll} n_lat: {n_lat} n_lon: {n_lon} delta_lat: {delta_lat} delta_lon: {delta_lon}")
met_data.attrs = {
'valid': v_str,
'init': v_str,
'lead': "00",
'accum': "00",
'name': 'sss',
'standard_name': 'analyzed sss',
'long_name': 'analyzed sss',
'level': "SURFACE",
'units': "degC",
'grid': {
'type': "LatLon",
'name': "Lat Lon",
'lat_ll': lat_ll,
'lon_ll': lon_ll,
'delta_lat': delta_lat,
'delta_lon': delta_lon,
'Nlat': n_lat,
'Nlon': n_lon,
}
}
attrs = met_data.attrs
if file_flag == 'climo':
met_data = climo2[:,:]
#modify the lat and lon grids since they need to match the data dimensions, and code cuts the last row/column of data
lat_met = climo2.lat
lon_met = climo2.lon
v_str = vDate.strftime("%Y%m%d")
v_str = v_str + '_000000'
lat_ll = float(lat_met.min())
lon_ll = float(lon_met.min())
n_lat = lat_met.shape[0]
n_lon = lon_met.shape[0]
delta_lat = (float(lat_met.max()) - float(lat_met.min()))/float(n_lat)
delta_lon = (float(lon_met.max()) - float(lon_met.min()))/float(n_lon)
print(f"variables:"
f"lat_ll: {lat_ll} lon_ll: {lon_ll} n_lat: {n_lat} n_lon: {n_lon} delta_lat: {delta_lat} delta_lon: {delta_lon}")
met_data.attrs = {
'valid': v_str,
'init': v_str,
'lead': "00",
'accum': "00",
'name': 'sea_water_temperature',
'standard_name': 'sea_water_temperature',
'long_name': 'sea_water_temperature',
'level': "SURFACE",
'units': "degC",
'grid': {
'type': "LatLon",
'name': "crs Grid",
'lat_ll': lat_ll,
'lon_ll': lon_ll,
'delta_lat': delta_lat,
'delta_lon': delta_lon,
'Nlat': n_lat,
'Nlon': n_lon,
}
}
attrs = met_data.attrs
For more information on the basic requirements to utilize Python Embedding in METplus, please refer to the MET User’s Guide section on Python embedding.
User Scripting
User Scripting is not used in this use case.
Running METplus
Pass the use case configuration file to the run_metplus.py script along with any user-specific system configuration files if desired:
run_metplus.py /path/to/METplus/parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsSMOS_climWOA_sss.conf /path/to/user_system.conf
See Running METplus for more information.
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 thisIce use case will be found in 20210503 (relative to OUTPUT_BASE) and will contain the following files:
grid_stat_SSS_000000L_20210503_000000V.stat
grid_stat_SSS_000000L_20210503_000000V_cnt.txt
grid_stat_SSS_000000L_20210503_000000V_pairs.nc
Keywords
Note
GridStatToolUseCase
PythonEmbeddingFileUseCase
MarineAndCryosphereAppUseCase
ClimatologyUseCase
Navigate to the METplus Quick Search for Use Cases page to discover other similar use cases.