Note
Go to the end to download the full example code.
GridStat: Python Embedding to read and process sea surface heights
model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh.conf
Scientific Objective
This use case utilizes Python embedding to extract several statistics from the sea surface height 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 4.1
Datasets
Forecast: RTOFS ssh file via Python Embedding script/file
Observations: AVISO ssh file via Python Embedding script/file
Sea Ice Masking: RTOFS ice cover file via Python Embedding script/file
Climatology: HYCOM ssh 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.
Data Source: COPERNICUS GLOBAL OCEAN SSH NRT (LEVEL 4), HYCOM + NCODA Global 1/12 deg Reanalysis
METplus Components
This use case only runs the Grid-Stat tool. It utilizes Python Embedding for the forecast, observation, and climatology datasets to be METplus-friendly.
METplus Workflow
Beginning time (VALID_BEG): 20210811
End time (VALID_END): 20210811
Increment between beginning and end times (VALID_INCREMENT): 1M
Sequence of forecast leads to process (LEAD_SEQ): 24
This use case will run 1 time to process the provided input files. In order to properly ingest the forecast, observation, and climatology datasets, Python Embedding is used. The configuration file passes the input forecast file, input observation file, ice cover masking file, directory path containing the climatology file, valid date string of %Y%m%d, and a file flag indicating which data dictionary should be passed back to METplus (forecast, observation, or climatology). All of the desired statistics reside in the CNT and SAL1L2 line types, so those are the only output requested.
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_obsAVISO_climHYCOM_ssh.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_obsAVISO_climHYCOM_ssh.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=20210811
VALID_END=20210811
VALID_INCREMENT = 1M
LEAD_SEQ = 24
###
# 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_CLIMO_MEAN_FIELD = {name="{CONFIG_DIR}/read_rtofs_aviso_hycom.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/{init?fmt=%Y%m%d}_rtofs_glo_2ds_f024_diag.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/nrt_global_allsat_phy_l4_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh {valid?fmt=%Y%m%d} climo"; level="(*,*)";}
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_obsAVISO_climHYCOM_ssh
FCST_VAR1_NAME = {CONFIG_DIR}/read_rtofs_aviso_hycom.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/{init?fmt=%Y%m%d}_rtofs_glo_2ds_f024_diag.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/nrt_global_allsat_phy_l4_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh {valid?fmt=%Y%m%d} fcst
OBS_VAR1_NAME = {CONFIG_DIR}/read_rtofs_aviso_hycom.py {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/{init?fmt=%Y%m%d}_rtofs_glo_2ds_f024_diag.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/nrt_global_allsat_phy_l4_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/OSTIA-UKMO-L4-GLOB-v2.0_{valid?fmt=%Y%m%d}.nc {INPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh {valid?fmt=%Y%m%d} obs
###
# GridStat Settings
# https://metplus.readthedocs.io/en/latest/Users_Guide/wrappers.html#gridstat
###
GRID_STAT_REGRID_TO_GRID = NONE
MODEL = RTOFS
OBTYPE = AVISO
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 = SSH
GRID_STAT_OUTPUT_FLAG_CNT = BOTH
GRID_STAT_OUTPUT_FLAG_SAL1L2 = 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, observation, and climatology data. At runtime, the Python script is passed the input forecast file, input observation file, ice cover masking file, directory path containing the climatology file, valid date string of %Y%m%d, and a file flag indicating which data dictionary should be passed back to METplus (forecast, observation, or climatology). The reasoning for needing all three files each time the script is run is due to the kd-tree calculations and masking which are all interrelated and cannot be currently performed within METplus. If any of the files are missing, an appropriate error message will be provided and the script will exit. If all files are present, then the script proceeds to pull out the requested forecast and observation fields, adjusting coordinate systems as necessary (not all of the inputs have the same coordinate system). For the climatology data, the script’s action is dependant on the valid date: if it’s prior to or after the 15th, the offset is calculated and used to extract a second climatology file’s data that extends over the date in use. If the valid date is exactly the 15th, then a single file can be used. After that, the script processes the ice mask data, creates weights for the model data via kd-tree interpolation, creates a new interpolated model grid that matches the observation dataset (this is also done for the climatology data field), masks the various fields with the ice mask, and removes the bad data below a given latitude. Finally, the file flag is used to determine which of the three modified grids (forecast, observation, or climatology) needs to be returned to METplus. Note that these flags correspond to the respective configuration file field in METplus.
parm/use_cases/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh/read_rtofs_aviso_hycom.py
#!/bin/env python
"""
Code adapted from
Todd Spindler
NOAA/NWS/NCEP/EMC
Designed to read in RTOFS,AVISO,HYCOM and OSTIA data
and based on user input, read ssh 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])
sshfile = 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 AVISO V&V at',datetime.now(),'for',vDate, ' file_flag:',file_flag)
pd.date_range(vDate,vDate)
platform='AVISO'
param='ssh'
#####################################################################
# READ AVISO data ###################################################
#####################################################################
if not os.path.exists(sshfile):
print('missing AVISO file for',vDate)
ssh_data=xr.open_dataset(sshfile,decode_times=True)
print('Retrieved SSH above sea level AVISO data from NESDIS for',ssh_data.time.values)
sla=ssh_data.sla.astype('single')
sla.attrs['platform']=platform
sla.attrs['time']=pd.Timestamp(ssh_data.time.values[0])
sla=sla.rename({'longitude':'lon','latitude':'lat'})
sla.attrs['filename']=sshfile.split('/')[-1]
# all coords need to be single precision
sla['lon']=sla.lon.astype('single')
sla['lat']=sla.lat.astype('single')
sla.attrs['units']='meters'
adt=ssh_data.adt.astype('single')
adt.attrs['platform']='aviso'
adt.attrs['filename']=sshfile
adt.attrs['time']=pd.Timestamp(ssh_data.time.values[0])
adt=adt.rename({'longitude':'lon','latitude':'lat'})
# all coords need to be single precision
adt['lon']=adt.lon.astype('single')
adt['lat']=adt.lat.astype('single')
adt.attrs['units']='meters'
sla=sla.squeeze()
adt=adt.squeeze()
#####################################################################
# 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 HYCOM 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="hycom_GLBv0.08_53X_archMN.1994_{0:02n}_2015_{0:02n}_ssh.nc".format(vDate.month)
climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
if vDate.day==15: # even for Feb, just because
climofile="hycom_GLBv0.08_53X_archMN.1994_{0:02n}_2015_{0:02n}_ssh.nc".format(vDate.month)
climo_data=xr.open_dataset(climoDir+'/'+climofile,decode_times=False)
climo_data=climo_data['surf_el'].copy().squeeze()
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="hycom_GLBv0.08_53X_archMN.1994_{0:02n}_2015_{0:02n}_ssh.nc".format(start.month)
climofile2="hycom_GLBv0.08_53X_archMN.1994_{0:02n}_2015_{0:02n}_ssh.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['surf_el'].copy().squeeze()
climo_data2=climo_data2['surf_el'].copy().squeeze()
climo_data=climo_data1+((climo_data2-climo_data1)*left)
climofile='weighted average of '+climofile1+' and '+climofile2
print('climofile1 :', climofile1)
print('climofile2 :', climofile2)
climo_data.coords['time']=datetime(vDate.year,vDate.month,1) # just a reference to the month
# 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']='hycom'
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 gausshian-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
print('regridding climo to obs')
climo_data=climo_data.squeeze()
climo_data=regrid(climo_data,adt)
print('regridding ice to obs')
ice_data2=regrid(ice_data2,adt)
print('regridding model to obs')
model2=regrid(outdata,adt)
# combine obs ice mask with ncep
obs2=adt.to_masked_array()
obs_anom=sla.copy()
obs_anom2=obs_anom.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
obs_anom2.mask=obs2.mask
obs2=xr.DataArray(obs2,coords=[adt.lat.values,adt.lon.values], dims=['lat','lon'])
obs_anom2=xr.DataArray(obs_anom2,coords=[adt.lat.values,adt.lon.values], dims=['lat','lon'])
model2=xr.DataArray(model2,coords=[adt.lat.values,adt.lon.values], dims=['lat','lon'])
climo2=xr.DataArray(climo2,coords=[adt.lat.values,adt.lon.values], dims=['lat','lon'])
model2=expand_grid(model2)
climo2=expand_grid(climo2)
obs2=expand_grid(obs2)
obs_anom2=expand_grid(obs_anom2)
#Modify the lat/lon min/max values to subset the data
model3=model2.where((model2.lon>=0)&(model2.lon<=360)&
(model2.lat>=-80)&(model2.lat<=90),drop=True)
climo3=climo2.where((climo2.lon>=0)&(climo2.lon<=360)&
(climo2.lat>=-80)&(climo2.lat<=90),drop=True)
obs3=obs2.where((obs2.lon>=0)&(obs2.lon<=360)&
(obs2.lat>=-80)&(obs2.lat<=90),drop=True)
obs_anom3=obs_anom2.where((obs_anom2.lon>=0)&(obs_anom2.lon<=360)&
(obs_anom2.lat>=-80)&(obs_anom2.lat<=90),drop=True)
#Create the MET grids based on the file_flag
if file_flag == 'fcst':
met_data = model3[:,:]
#trim the lat/lon grids so they match the data fields
lat_met = model3.lat
lon_met = model3.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': 'ssh',
'standard_name': 'sea_surface_elevation',
'long_name': 'sea surf. height [92.8H]',
'level': "SURFACE",
'units': "meters",
'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 = obs3[:,:]
#trim the lat/lon grids so they match the data fields
lat_met = obs3.lat
lon_met = obs3.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': 'ssh',
'standard_name': 'sea_surface_height_above_geoid',
'long_name': 'absolute_dynamic_topography',
'level': "SURFACE",
'units': "meters",
'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 = climo3[:,:]
#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 = climo3.lat
lon_met = climo3.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_surface_height',
'standard_name': 'sea_surface_elevation',
'long_name': 'Water Surface Elevation',
'level': "SURFACE",
'units': "meters",
'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
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
User Scripting
This use case does not use additional scripts.
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_obsAVISO_climHYCOM_ssh.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 this use case will be found in {OUTPUT_BASE}/model_applications/marine_and_cryosphere/GridStat_fcstRTOFS_obsAVISO_climHYCOM_ssh Output for this ice use case will be found in 20210503. and will contain the following files:
* grid_stat_SSH_000000L_20210811_000000V.stat
* grid_stat_SSH_000000L_20210811_000000V_sal1l2.txt
* grid_stat_SSH_000000L_20210811_000000V_cnt.txt
* grid_stat_SSH_000000L_20210811_000000V_pairs.nc
The SAL1L2 and CNT line types were requested output with the BOTH configuration, so the .stat file will contain both line type outputs as well as each line type having its own text file. The netCDF file will only contain the raw fields as no NC_PAIRS_FLAG settings were utilized.
Keywords
Note
GridStatToolUseCase
PythonEmbeddingFileUseCase
MarineAndCryosphereAppUseCase
ClimatologyUseCase
Navigate to the METplus Quick Search for Use Cases page to discover other similar use cases.