GridStat: Python Embedding to read and process SST

model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.conf

Scientific Objective

This use case utilizes Python embedding to extract several statistics from the sea surface temperature 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.

Datasets

Forecast: RTOFS sst file via Python Embedding script/file
Observations: GHRSST sst file via Python Embedding script/file
Sea Ice Masking: RTOFS ice cover file via Python Embedding script/file
Climatology: WOA sst file via Python Embedding script/file
Location: All of the input data required for this use case can be found in the met_test sample data tarball. Click here to 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.
Data Source: JPL’s PODAAC and NCEP’s FTPPRD data servers

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 each time a field (fcst, obs, and climo) is needed.

METplus Workflow

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.

[config]

# Documentation for this use case can be found at
# https://metplus.readthedocs.io/en/latest/generated/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.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

MODEL = RTOFS
OBTYPE = GHRSST

CONFIG_DIR = {PARM_BASE}/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst

FCST_VAR1_NAME = {CONFIG_DIR}/read_rtofs_ghrsst_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/GHRSST-OSPO-L4-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst {valid?fmt=%Y%m%d} fcst

OBS_VAR1_NAME = {CONFIG_DIR}/read_rtofs_ghrsst_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/GHRSST-OSPO-L4-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst {valid?fmt=%Y%m%d} obs

GRID_STAT_CLIMO_MEAN_FIELD = {name="{CONFIG_DIR}/read_rtofs_ghrsst_woa.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/{valid?fmt=%Y%m%d}_rtofs_glo_2ds_f024_prog.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/GHRSST-OSPO-L4-GLOB_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst {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

GRID_STAT_DESC = NA

GRID_STAT_NEIGHBORHOOD_WIDTH = 1
GRID_STAT_NEIGHBORHOOD_SHAPE = SQUARE

GRID_STAT_NEIGHBORHOOD_COV_THRESH = >=0.5

GRID_STAT_OUTPUT_PREFIX = SST

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

Note

See the GridStat MET Configuration section of the User’s Guide for more information on the environment variables used in the file below:

////////////////////////////////////////////////////////////////////////////////
//
// 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  	 = [];
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}
}
obs = {
  ${METPLUS_OBS_FILE_TYPE}
  ${METPLUS_OBS_FIELD}
}

////////////////////////////////////////////////////////////////////////////////

//
// 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 = {
   dx = [ 1 ];
   dy = [ 1 ];
}

////////////////////////////////////////////////////////////////////////////////

//
// Distance Map statistics
// May be set separately in each "obs.field" entry
//
//distance_map = {
${METPLUS_DISTANCE_MAP_DICT}

////////////////////////////////////////////////////////////////////////////////

//
// 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}

////////////////////////////////////////////////////////////////////////////////
// Threshold for SEEPS p1 (Probability of being dry)

//seeps_p1_thresh =
${METPLUS_SEEPS_P1_THRESH}

////////////////////////////////////////////////////////////////////////////////

//grid_weight_flag =
${METPLUS_GRID_WEIGHT_FLAG}

tmp_dir = "${MET_TMP_DIR}";

// output_prefix =
${METPLUS_OUTPUT_PREFIX}

////////////////////////////////////////////////////////////////////////////////

${METPLUS_MET_CONFIG_OVERRIDES}

Python Embedding

This use case uses one Python script to read forecast, observation, and climatology data

parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst/read_rtofs_ghrsst_woa.py

#!/usr/bin/env python3
"""
Code adapted from
Todd Spindler
NOAA/NWS/NCEP/EMC
Designed to read in RTOFS,GHRSST,WOA and OSTIA data
and based on user input, read sst 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)
#grab input files from command line input
rtofsfile = os.path.expandvars(sys.argv[1]) 
sstfile = 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 GHRSST V&V at',datetime.now(),'for',vDate, ' file_flag:',file_flag)

pd.date_range(vDate,vDate)
platform='GHRSST'
param='sst'


#####################################################################
# READ GHRSST data ##################################################
#####################################################################

if not os.path.exists(sstfile):
        print('missing GHRSST file for',vDate)

sst_data=xr.open_dataset(sstfile,decode_times=True)
sst_data['time']=sst_data.time-pd.Timedelta('12H')  # shift 12Z offset time to 00Z
sst_data2=sst_data.analysed_sst.astype('single')-273.15 # convert from Kelvin
print('Retrieved GHRSST data from NESDIS for',sst_data2.time.values)

sst_data2['lon']=sst_data2.lon.astype('single')
sst_data2['lat']=sst_data2.lat.astype('single')
#sst_data2.attrs['platform']='ghrsst'
sst_data2.attrs['platform']=platform
sst_data2.attrs['units']='degC'

#####################################################################
# 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()
#indata.close()

outdata=indata

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_t{:02n}_04v2.nc".format(vDate.month)
#climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
#climo_data=climo_data['t_an'].squeeze()[0,]

if vDate.day==15:  # even for Feb, just because
    climofile="woa13_decav_t{:02n}_04v2.nc".format(vDate.month)
    climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
    climo_data=climo_data['t_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_t{:02n}_04v2.nc".format(start.month)
    climofile2="woa13_decav_t{:02n}_04v2.nc".format(stop.month)
    climo_xr1=xr.open_dataset(climoDir+'/'+climofile1,decode_times=False)
    climo_xr2=xr.open_dataset(climoDir+'/'+climofile2,decode_times=False)
    climo_data1=climo_xr1['t_an'].squeeze()[0,]  # surface only
    climo_data2=climo_xr2['t_an'].squeeze()[0,]  # surface only

    climo_xr1.close()
    climo_xr2.close()

    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=model
    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=obs
    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()
    model2_lat1=model2_lat
    obs2_lon1=pyr.utils.wrap_longitudes(obs2_lon)
    #obs2_lat1=obs2_lat.copy()
    obs2_lat1=obs2_lat

    # 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')
    data2.close()
    data.close()
    return data3

sst_data2=sst_data2.squeeze()

#print('regridding climo to obs')
climo_data=climo_data.squeeze()
climo_data=regrid(climo_data,sst_data2)

#print('regridding ice to obs')
ice_data2=regrid(ice_data2,sst_data2)

#print('regridding model to obs')
model2=regrid(outdata,sst_data2)

# combine obs ice mask with ncep
obs2=sst_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

coord_lat = sst_data2.lat.values
coord_lon = sst_data2.lon.values

sst_data2.close()

#Create the MET grids based on the file_flag
if file_flag == 'fcst':
    #model2=xr.DataArray(model2,coords=[sst_data2.lat.values,sst_data2.lon.values], dims=['lat','lon'])
    model2=xr.DataArray(model2,coords=[coord_lat,coord_lon], dims=['lat','lon'])
    model2=expand_grid(model2)
    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': 'sst',
            'standard_name': 'sst',
            'long_name': 'sst',
            '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':
    #obs2=xr.DataArray(obs2,coords=[sst_data2.lat.values,sst_data2.lon.values], dims=['lat','lon'])
    obs2=xr.DataArray(obs2,coords=[coord_lat, coord_lon], dims=['lat','lon'])
    obs2=expand_grid(obs2)
    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': 'sst',
            'standard_name': 'analyzed sst',
            'long_name': 'analyzed sst',
            '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':
    #climo2=xr.DataArray(climo2,coords=[sst_data2.lat.values,sst_data2.lon.values], dims=['lat','lon'])
    climo2=xr.DataArray(climo2,coords=[coord_lat, coord_lon], dims=['lat','lon'])
    climo2=expand_grid(climo2)
    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

Running METplus

This use case can be run two ways:

  1. Passing in GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.conf then a user-specific system configuration file:

    run_metplus.py /path/to/METplus/parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.conf /path/to/user_system.conf
    
  2. Modifying the configurations in parm/metplus_config, then passing in GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.conf:

    run_metplus.py /path/to/METplus/parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsGHRSST_climWOA_sst.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

Example User Configuration File:

[config]
INPUT_BASE = /path/to/sample/input/data
OUTPUT_BASE = /path/to/output/dir
MET_INSTALL_DIR = /path/to/met-X.Y

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 directory 20210503 (relative to OUTPUT_BASE) and will contain the following files:

  • grid_stat_SST_000000L_20210503_000000V.stat

  • grid_stat_SST_000000L_20210503_000000V_cnt.txt

  • grid_stat_SST_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.

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