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nhc_dashboard.py
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nhc_dashboard.py
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'''
nhc_dashboard.py - python function that can be used to read gridded sensitivity files
for track, intensity, 2D wind, and precipitation metrics and plot the
NHC dashboard figure. If traveling salesman dropsonde and track files
exist, they will also be plotted on top of the sensitivity fields for
each metric as well. This program can be used to determine the extent
to which a proposed flight track aligns with the sensitive regions.
usage: python nhc_dashboard.py --init yyyymmddhh --storm XXXXXXNNB --fhour hhh
--param paramfile --drops DROPFILE --turns TURNFILE
where:
--init is the initialization date in yyyymmddhh format
--storm is the TC name (XXXXXX is the storm name, NN is the number, B is the basin)
--fhour is the forecast hour to plot the sensitivity grids
--param is the parameter file path (optional, otherwise goes to default values in default.parm)
--drops is the path to the dropsonde locations file
--turns is the path to the aircraft turns file
'''
import os, sys
import argparse
import configparser
import tarfile
import xarray as xr
import numpy as np
import datetime as dt
import matplotlib
from IPython.core.pylabtools import figsize, getfigs
import matplotlib.pyplot as plt
from matplotlib import colors
import cartopy.crs as ccrs
sys.path.append('../esens-util')
from SensPlotRoutines import addDrop, addTurns, addRangeRings, set_projection, background_map
def main():
# Read the initialization time and storm from the command line
exp_parser = argparse.ArgumentParser()
exp_parser.add_argument('--init', action='store', type=str, required=True)
exp_parser.add_argument('--storm', action='store', type=str, required=True)
exp_parser.add_argument('--fhour', action='store', type=int, required=True)
exp_parser.add_argument('--param', action='store', type=str)
exp_parser.add_argument('--drops', action='store', type=str)
exp_parser.add_argument('--turns', action='store', type=str)
args = exp_parser.parse_args()
# Read paramter file and set location paramters to case-specific location
config = configparser.ConfigParser()
config.read(args.param)
config['locations']['work_dir'] = '{0}/{1}.{2}'.format(config['locations']['work_dir'],args.init,args.storm)
config['locations']['output_dir'] = '{0}/{1}.{2}'.format(config['locations']['output_dir'],args.init,args.storm)
config['locations']['figure_dir'] = '{0}/{1}.{2}'.format(config['locations']['figure_dir'],args.init,args.storm)
os.chdir(config['locations']['work_dir'])
# untar the gridded sensitivity information if it is not present in work directory
if not os.path.isdir('{0}/{1}'.format(config['locations']['work_dir'],args.init)):
tarout = '{0}/{1}.tar'.format(config['locations']['outgrid_dir'] + '/../awips',args.init)
if ( os.path.isfile(tarout) and tarfile.is_tarfile(tarout) ):
os.system('tar --skip-old-files -xf {0}'.format(tarout))
datea_dt = dt.datetime.strptime(args.init, '%Y%m%d%H')
datef_dt = datea_dt + dt.timedelta(hours=args.fhour)
datef = datef_dt.strftime("%Y%m%d%H")
fhrt = '%0.3i' % args.fhour
if args.storm[-1] == "l":
bb = 'al'
lat1 = float(config['sens'].get('min_lat','8.0'))
lat2 = float(config['sens'].get('max_lat','65.0'))
lon1 = float(config['sens'].get('min_lon','-140.0'))
lon2 = float(config['sens'].get('max_lon','-20.0'))
elif args.storm[-1] == "e":
bb = 'ep'
lat1 = float(config['sens'].get('min_lat','8.0'))
lat2 = float(config['sens'].get('max_lat','65.0'))
lon1 = float(config['sens'].get('min_lon','-180.0'))
lon2 = float(config['sens'].get('max_lon','-80.0'))
elif args.storm[-1] == "c":
bb = 'cp'
lat1 = float(config['sens'].get('min_lat','8.0'))
lat2 = float(config['sens'].get('max_lat','65.0'))
lon1 = float(config['sens'].get('min_lon','-180.0'))
lon2 = float(config['sens'].get('max_lon','-80.0'))
elif args.storm[-1] == "w":
bb = 'wp'
bbnn = '{0}{1}'.format(bb,args.storm[-3:-1])
# Read lat/lon arrays over the desired domain
ds = xr.open_dataset('{0}/{1}/track/{0}_f{2}_masteer_sens.nc'.format(args.init,bbnn,fhrt))
if ds.lat[0] > ds.lat[1]:
lattmp1 = lat1
lattmp2 = lat2
lat1 = lattmp2
lat2 = lattmp1
lat = ds.lat.sel(lat=slice(lat1, lat2)).values.squeeze()
lon = ds.lon.sel(lon=slice(lon1, lon2)).values.squeeze()
plotDict = {}
for key in config['sens']:
plotDict[key] = config['sens'][key]
plotDict['tcLat'] = ds.attrs['TC_latitude']
plotDict['tcLon'] = ds.attrs['TC_longitude']
plotDict['ring_center_lat']=float(plotDict['tcLat'])
plotDict['ring_center_lon']=float(plotDict['tcLon'])
plotDict['range_rings']='True'
plotDict['min_lat']=float(plotDict['tcLat'])-float(plotDict.get('storm_center_radius', 10.))
plotDict['max_lat']=float(plotDict['tcLat'])+float(plotDict.get('storm_center_radius', 10.))
plotDict['min_lon']=float(plotDict['tcLon'])-float(plotDict.get('storm_center_radius', 10.))
plotDict['max_lon']=float(plotDict['tcLon'])+float(plotDict.get('storm_center_radius', 10.))
plotDict['grid_interval']=3.
plotDict['subplot'] = 'True'
plotDict['subrows'] = 2
plotDict['subcols'] = 2
# Determine if dropsonde file is provided; if not, try to use parameter file value with valid date
if args.drops:
plotDict['dropsonde_file'] = args.drops
elif 'dropsonde_file' in config['sens']:
plotDict['dropsonde_file'] = config['sens']['dropsonde_file'].format(datef)
if 'dropsonde_file' in plotDict:
if not os.path.isfile(plotDict['dropsonde_file']):
print("{0} dropsonde file does not exist. Drops will not be plotted".format(plotDict['dropsonde_file']))
else:
print("No information on the dropsonde file.")
# Determine if a turns file is provided; if not, try to use parameter file value with valid date
if args.turns:
plotDict['turns_file'] = args.turns
elif 'turns_file' in config['sens']:
plotDict['turns_file'] = config['sens']['turns_file'].format(datef)
if 'turns_file' in plotDict:
if not os.path.isfile(plotDict['turns_file']):
print("{0} turns file does not exist. Turns will not be plotted".format(plotDict['turns_file']))
else:
print("No information on the turns file.")
fig = plt.figure(figsize=(11,11))
# Read sensitivity information for the intensity metric
masens = ds.track_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
infile = '{0}/{1}/track/{0}_f{2}_csteer_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
svsens = ds.track_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
svsens = np.array([])
# create track metric panel
plotDict['subnumber'] = 1
plotDict['plotTitle'] = 'track'
plotDict['plotLegend'] = ['Major Steering Wind', 'Steering Vorticity']
plotSummarySens(lat, lon, masens, svsens, np.array([]), plotDict)
# Read sensitivity information for the intensity metric (if this metric exists)
infile = '{0}/{1}/inten/{0}_f{2}_vor850hPa_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
vo850sens = ds.inten_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
vo850sens = np.array([])
infile = '{0}/{1}/inten/{0}_f{2}_csteer_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
svsens = ds.inten_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
svsens = np.array([])
infile = '{0}/{1}/inten/{0}_f{2}_q500-850hPa_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
q58sens = ds.inten_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
q58sens = np.array([])
# create intensity metric panel
plotDict['subnumber'] = 2
plotDict['plotTitle'] = 'intensity'
plotDict['plotLegend'] = ['850 hPa vorticity', 'Steering Vorticity', '500-850 hPa qvap']
plotSummarySens(lat, lon, vo850sens, svsens, q58sens, plotDict)
# Read sensitivity information for the 2D max. wind metric (if this metric exists)
infile = '{0}/{1}/wind/{0}_f{2}_masteer_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
masens = ds.wind_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
masens = np.array([])
infile = '{0}/{1}/wind/{0}_f{2}_csteer_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
svsens = ds.wind_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
svsens = np.array([])
infile = '{0}/{1}/wind/{0}_f{2}_q500-850hPa_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
q58sens = ds.wind_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
q58sens = np.array([])
# create max. wind metric panel
plotDict['subnumber'] = 3
plotDict['plotTitle'] = 'max. wind'
plotDict['plotLegend'] = ['Major Steering Wind', 'Steering Vorticity', '500-850 hPa qvap']
plotSummarySens(lat, lon, masens, svsens, q58sens, plotDict)
# Read sensitivity information for the precipitation metric (if this metric exists)
infile = '{0}/{1}/pcp/{0}_f{2}_csteer_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
svsens = ds.pcp_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
svsens = np.array([])
infile = '{0}/{1}/pcp/{0}_f{2}_q500-850hPa_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
q58sens = ds.pcp_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
q58sens = np.array([])
infile = '{0}/{1}/pcp/{0}_f{2}_ivt_sens.nc'.format(args.init,bbnn,fhrt)
if os.path.isfile(infile):
ds = xr.open_dataset(infile)
ivsens = ds.pcp_sensitivity.sel(lat=slice(lat1, lat2), lon=slice(lon1, lon2)).squeeze()
else:
ivsens = np.array([])
# Create Precipitation Metric Panel
plotDict['subnumber'] = 4
plotDict['plotTitle'] = 'precip.'
plotDict['plotLegend'] = ['IVT', 'Steering Vorticity', '500-850 hPa qvap']
plotSummarySens(lat, lon, ivsens, svsens, q58sens, plotDict)
# Add a title and write out the file
fig.suptitle('{0} ({1}), init: {2}, valid: {3} (Hour: {4})'.format(args.storm[0:-3].capitalize(), \
bbnn.upper(),args.init,datef,fhrt), fontsize=16, y=0.92)
fileout = '{0}/{1}_f{2}_dashboard.png'.format(config['locations']['figure_dir'],args.init,fhrt)
plt.savefig(fileout,format='png',dpi=120,bbox_inches='tight')
plt.close(fig)
def plotSummarySens(lat, lon, f1sens, f2sens, f3sens, plotDict):
'''
Function that plots the individual panels of the NHC dashboard sensitivity plots for a
specific forecast metric. Each forecast metric has a separate set of forecast fields to
compute the sensitivity through, passed in via f*sens arrays. The user has the option to
add customized elements to the plot, including range rings, locations of
rawinsondes/dropsondes, turns, titles, etc. These are all turned
on or off using the configuration file.
Attributes:
lat (float): Latitude of fields
lon (float): Longitude of fields
f1sens (float): Sensitivity of metric to forecast field #1
f2sens (float): Sensitivity of metric to forecast field #2
f3sens (float): Sensitivity of metric to forecast field #3
plotDict (dict.): Dictionary that contains configuration options
'''
minLat = float(plotDict.get('min_lat', np.amin(lat)))
maxLat = float(plotDict.get('max_lat', np.amax(lat)))
minLon = float(plotDict.get('min_lon', np.amin(lon)))
maxLon = float(plotDict.get('max_lon', np.amax(lon)))
sencnt = float(plotDict.get('summary_contour', 0.36))
ax = background_map(plotDict.get('projection', 'PlateCarree'), minLon, maxLon, minLat, maxLat, plotDict)
# Plot each sensitivity area for each field that is available for this metric
hasSens = False
if f1sens.any():
plt1 = plt.contourf(lon,lat,f1sens,[-sencnt, sencnt],extend='both',alpha=0.5,transform=ccrs.PlateCarree(), \
cmap=matplotlib.colors.ListedColormap(("#00FF00","#FFFFFF","#00FF00")))
hasSens = True
if f2sens.any():
plt2 = plt.contourf(lon,lat,f2sens,[-sencnt, sencnt],extend='both',alpha=0.5,transform=ccrs.PlateCarree(), \
cmap=matplotlib.colors.ListedColormap(("#FF00FF","#FFFFFF","#FF00FF")))
hasSens = True
if f3sens.any():
plt3 = plt.contourf(lon,lat,f3sens,[-sencnt, sencnt],extend='both',alpha=0.5,transform=ccrs.PlateCarree(), \
cmap=matplotlib.colors.ListedColormap(("#0000FF","#FFFFFF","#0000FF")))
hasSens = True
# Add text if there are no fields with senitivity output available (i.e., metric is missing)
if not hasSens:
plt.text((minLon+maxLon)*0.5, minLat+(maxLat-minLat)*0.66, 'Not', color='k', fontsize=24, \
horizontalalignment='center', transform=ccrs.PlateCarree())
plt.text((minLon+maxLon)*0.5, minLat+(maxLat-minLat)*0.33, 'Available', color='k', fontsize=24, \
horizontalalignment='center', transform=ccrs.PlateCarree())
# plot the title if that string is present
if 'plotTitle' in plotDict:
plt.title(plotDict['plotTitle'])
# Plot the text figure legend underneath each panel
if 'plotLegend' in plotDict:
plt.text(minLon, minLat-(maxLat-minLat)*0.09, plotDict['plotLegend'][0], color='#00FF00', fontsize=8, \
horizontalalignment='left', transform=ccrs.PlateCarree())
plt.text((minLon+maxLon)*0.5, minLat-(maxLat-minLat)*0.09, plotDict['plotLegend'][1], color='#FF00FF', fontsize=8, \
horizontalalignment='center', transform=ccrs.PlateCarree())
if len(plotDict['plotLegend']) > 2:
plt.text(maxLon, minLat-(maxLat-minLat)*0.09, plotDict['plotLegend'][2], color='#0000FF', fontsize=8, \
horizontalalignment='right', transform=ccrs.PlateCarree())
# Add range rings if that information is present
addRangeRings(plotDict['ring_center_lat'], plotDict['ring_center_lon'], lat, lon, plt, plotDict)
# Plot dropsondes and turns if file exists AND there was sensitivity information for this metric
if hasSens:
addDrop(plotDict.get("dropsonde_file","null"), plt, plotDict)
addTurns(plotDict.get("turns_file","null"), plt, plotDict)
if __name__ == '__main__':
main()