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compute_env_allfields_parallel.py
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compute_env_allfields_parallel.py
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#!/usr/bin/env python
from netCDF4 import Dataset
import numpy as np
from datetime import *
import time, os, sys
import scipy.ndimage as ndimage
import pickle as pickle
import multiprocessing
from scipy import spatial
from mpl_toolkits.basemap import *
import scipy.ndimage.filters
from scipy.interpolate import griddata, RectBivariateSpline
import matplotlib.colors as colors
import matplotlib.pyplot as plt
### THIS CODE EVOLVED FROM CODE WITHIN /glade/u/home/sobash/NSC_scripts
### TO UPSCALE 3-KM CAM DATA TO AN 80-KM GRID
def readNCLcm(name):
'''Read in NCL colormap for use in matplotlib'''
import os
rgb, appending = [], False
rgb_dir_ys = '/glade/apps/opt/ncl/6.2.0/intel/12.1.5/lib/ncarg/colormaps'
rgb_dir_ch = '/glade/u/apps/ch/opt/ncl/6.4.0/intel/16.0.3/lib/ncarg/colormaps'
if os.path.isdir(rgb_dir_ys): fh = open('%s/%s.rgb'%(rgb_dir_ys,name), 'r')
else: fh = open('%s/%s.rgb'%(rgb_dir_ch,name), 'r')
for line in fh.read().splitlines():
if appending: rgb.append(map(float,line.split()))
if ''.join(line.split()) in ['#rgb',';RGB']: appending = True
maxrgb = max([ x for y in rgb for x in y ])
if maxrgb > 1: rgb = [ [ x/255.0 for x in a ] for a in rgb ]
return rgb
def upscale(field, type='mean', maxsize=27):
if model in ['NSC3km', 'NSC3km-12sec', 'GEFS', 'RT2020']: kernel = np.ones((26,26)) #should this be 27?
elif model in ['NSC1km']: kernel = np.ones((81,81))
weights = kernel / float(kernel.size)
if type == 'mean':
#field = scipy.ndimage.filters.convolve(field, weights=weights, mode='constant', cval=0.0)
field = scipy.ndimage.filters.uniform_filter(field, size=maxsize, mode='constant', cval=0.0)
elif type == 'max':
field = scipy.ndimage.filters.maximum_filter(field, size=maxsize)
field_interp = field.flatten()[nngridpts[1]].reshape((65,93))
return field_interp
def get_closest_gridbox():
### find closest 3-km or 1-km grid point to each 80-km grid point
gpfname = '/glade/work/sobash/NSC_objects/nngridpts_80km_%s'%model
if os.path.exists(gpfname):
nngridpts = pickle.load(open(gpfname, 'rb'), encoding='bytes')
else:
# INTERPOLATE NARR TO 80KM GRID
#fig, axes, m = pickle.load(open('/glade/u/home/wrfrt/rt_ensemble_2018wwe/python_scripts/rt2015_CONUS.pk', 'r'))
awips = Basemap(projection='lcc', llcrnrlon=-133.459, llcrnrlat=12.19, urcrnrlon=-49.38641, urcrnrlat=57.2894, lat_1=25.0, lat_2=25.0, lon_0=-95, resolution=None, area_thresh=10000.)
grid81 = awips.makegrid(93, 65, returnxy=True)
x81, y81 = awips(grid81[0], grid81[1])
#x81 = (x[1:,1:] + x[:-1,:-1])/2.0
#y81 = (y[1:,1:] + y[:-1,:-1])/2.0
if model == 'NSC1km': f = Dataset('/glade/p/mmm/parc/sobash/NSC/1KM_WRF_POST/2011062500/diags_d01_2011-06-25_00_00_00.nc', 'r')
if model == 'NSC3km-12sec': f = Dataset('/glade/p/mmm/parc/sobash/NSC/3KM_WRF_POST_12sec_ts/2011062500/diags_d01_2011-06-25_00_00_00.nc', 'r')
if model == 'GEFS': f = Dataset('/glade/scratch/sobash/ncar_ens/gefs_ics/2017042500/wrf_rundir/ens_1/diags_d02.2017-04-25_00:00:00.nc', 'r')
if model == 'RT2020': f = Dataset('/glade/p/mmm/parc/sobash/NSC/3KM_WRF_POST_12sec_ts/2011062500/diags_d01_2011-06-25_00_00_00.nc', 'r')
lats = f.variables['XLAT'][0,:]
lons = f.variables['XLONG'][0,:]
f.close()
xy = awips(lons.ravel(), lats.ravel())
tree = spatial.KDTree(list(zip(xy[0].ravel(),xy[1].ravel())))
nngridpts = tree.query(list(zip(x81.ravel(),y81.ravel())))
pickle.dump(nngridpts, open('nngridpts_80km_%s'%model, 'wb'))
return nngridpts
def upscale_forecast(fhr):
print(time.ctime(time.time()), 'reading', yyyymmddhh, fhr)
this_upscaled_fields = upscaled_fields.copy()
### read in NSSL dataset for this day
try:
wrfvalidstr = (tdate + timedelta(hours=fhr)).strftime('%Y-%m-%d_%H_%M_%S')
wrfvalidstr2 = (tdate + timedelta(hours=fhr)).strftime('%Y-%m-%d_%H:%M:%S')
if model == 'NSC3km-12sec': wrffile = "/glade/p/mmm/parc/sobash/NSC/3KM_WRF_POST_12sec_ts/%s/diags_d01_%s.nc"%(yyyymmddhh,wrfvalidstr)
if model == 'NSC1km': wrffile = "/glade/p/mmm/parc/sobash/NSC/1KM_WRF_POST/%s/diags_d01_%s.nc"%(yyyymmddhh,wrfvalidstr)
if model == 'GEFS': wrffile = "/glade/scratch/sobash/ncar_ens/gefs_ics/%s/wrf_rundir/ens_%d/diags_d02.%s.nc"%(yyyymmddhh,mem,wrfvalidstr2)
if model == 'RT2020': wrffile = "/glade/scratch/wrfrt/realtime_ensemble/rt2020/%s/wrf_rundir/ens_%d/diags_d01_%s.nc"%(yyyymmddhh,mem,wrfvalidstr2)
fh = Dataset(wrffile)
# populate dictionary of upscaled fields
for f in this_upscaled_fields.keys():
#print(time.ctime(time.time()), f)
if f == 'SHR06':
this_field1 = fh.variables['USHR6'][0,:]
this_field2 = fh.variables['VSHR6'][0,:]
this_field = np.sqrt(this_field1**2 + this_field2**2)
elif f == 'SHR01':
this_field1 = fh.variables['USHR1'][0,:]
this_field2 = fh.variables['VSHR1'][0,:]
this_field = np.sqrt(this_field1**2 + this_field2**2)
elif f == 'LR75':
t500 = fh.variables['T_PL'][0,5,:]
t700 = fh.variables['T_PL'][0,3,:]
ht500 = fh.variables['GHT_PL'][0,5,:]
ht700 = fh.variables['GHT_PL'][0,3,:]
this_field = -(t700-t500)/(ht700-ht500)
elif f == 'CAPESHEAR':
ushr = fh.variables['USHR6'][0,:]
vshr = fh.variables['VSHR6'][0,:]
shr06 = np.sqrt(ushr**2 + vshr**2)
mlcape = fh.variables['MLCAPE'][0,:]
this_field = mlcape * shr06
elif f == 'STP':
lcl = fh.variables['MLLCL'][0,:]
sbcape = fh.variables['SBCAPE'][0,:]
srh01 = fh.variables['SRH01'][0,:]
sbcinh = fh.variables['SBCINH'][0,:]
ushr = fh.variables['USHR6'][0,:]
vshr = fh.variables['VSHR6'][0,:]
shr06 = np.sqrt(ushr**2 + vshr**2)
lclterm = ((2000.0-lcl)/1000.0)
lclterm = np.where(lcl<1000, 1.0, lclterm)
lclterm = np.where(lcl>2000, 0.0, lclterm)
shrterm = (shr06/20.0)
shrterm = np.where(shr06 > 30, 1.5, shrterm)
shrterm = np.where(shr06 < 12.5, 0.0, shrterm)
cinterm = ((200+sbcinh)/150.0)
cinterm = np.where(cinterm>-50, 1.0, cinterm)
cinterm = np.where(cinterm<-200, 0.0, cinterm)
this_field = (sbcape/1500.0) * lclterm * (srh01/150.0) * shrterm * cinterm
elif f in ['TD2']:
if model not in ['GEFS']: this_field = fh.variables['TD2'][0,:]
else:
import metpy.calc
from metpy.units import units
p = fh.variables['PSFC'][0,:]
r = fh.variables['Q2'][0,:]
this_field = metpy.calc.dewpoint(metpy.calc.vapor_pressure(p*units.Pa,r*units('kg/kg')))*units('K')
elif f in ['T925', 'T850', 'T700', 'T500']:
level = int(f[1:])
idx = press_levels.index(level)
this_field = fh.variables['T_PL'][0,idx,:]
elif f in ['TD925', 'TD850', 'TD700', 'TD500']:
level = int(f[2:])
idx = press_levels.index(level)
this_field = fh.variables['TD_PL'][0,idx,:]
elif f in ['U925', 'U850', 'U700', 'U500']:
level = int(f[1:])
idx = press_levels.index(level)
this_field = fh.variables['U_PL'][0,idx,:]
elif f in ['V925', 'V850', 'V700', 'V500']:
level = int(f[1:])
idx = press_levels.index(level)
this_field = fh.variables['V_PL'][0,idx,:]
elif f in ['UP_HELI_MAX80', 'UP_HELI_MAX120']:
this_field = fh.variables['UP_HELI_MAX'][0,:]
elif f in ['UP_HELI_MAX01-120']:
this_field = fh.variables['UP_HELI_MAX01'][0,:]
elif f in ['HAILCAST_DIAM_MAX']:
this_field = fh.variables['UP_HELI_MAX'][0,:]
this_field[:] = 0.0
else:
this_field = fh.variables[f][0,:]
# use maximum for certain fields, mean for others
if f in ['UP_HELI_MAX', 'UP_HELI_MAX03', 'UP_HELI_MAX01', 'W_UP_MAX', 'W_DN_MAX', 'WSPD10MAX', 'HAILCAST_DIAM_MAX']:
if model in ['NSC3km-12sec', 'RT2020']: field_interp = upscale(this_field, type='max', maxsize=27)
else: field_interp = upscale(this_field, type='max', maxsize=27*3)
elif f in ['UP_HELI_MAX80']:
if model in ['NSC3km-12sec', 'RT2020']: field_interp = upscale(this_field, type='max', maxsize=53)
else: field_interp = upscale(this_field, type='max', maxsize=53*3)
elif f in ['UP_HELI_MAX120', 'UP_HELI_MAX01-120']:
if model in ['NSC3km-12sec', 'RT2020']: field_interp = upscale(this_field, type='max', maxsize=81)
else: field_interp = upscale(this_field, type='max', maxsize=81*3)
else:
if model in ['NSC3km-12sec', 'RT2020']: field_interp = upscale(this_field, type='mean', maxsize=26) #should be 27?
else: field_interp = upscale(this_field, type='mean', maxsize=81)
this_upscaled_fields[f] = field_interp
fh.close()
except Exception as e:
print(e)
return (fhr, this_upscaled_fields)
return {fhr: this_upscaled_fields}
sdate = datetime.strptime(sys.argv[1], '%Y%m%d%H')
edate = sdate + timedelta(hours=24)
tdate = sdate
yyyy = tdate.strftime('%Y')
yyyymmddhh = tdate.strftime('%Y%m%d%H')
yymmdd = tdate.strftime('%y%m%d')
model = 'NSC3km-12sec'
#model = 'RT2020'
model = 'NSC1km'
#model = sys.argv[2]
#mem = int(sys.argv[3])
mem = 1
upscaled_fields = { 'UP_HELI_MAX':[], 'UP_HELI_MAX03':[], 'UP_HELI_MAX01':[], 'W_UP_MAX':[], 'W_DN_MAX':[], 'WSPD10MAX':[], 'STP':[], 'LR75':[], 'CAPESHEAR':[],
'MUCAPE':[], 'SBCAPE':[], 'SBCINH':[], 'MLCINH':[], 'MLLCL':[], 'SHR06': [], 'SHR01':[], 'SRH01':[], 'SRH03':[], 'T2':[], 'TD2':[], 'PSFC':[], 'PREC_ACC_NC':[], \
'HAILCAST_DIAM_MAX':[], \
'T925':[], 'T850':[], 'T700':[], 'T500':[], 'TD925':[], 'TD850':[], 'TD700':[], 'TD500':[], 'U925':[], 'U850':[], 'U700':[], 'U500':[], 'V925':[], 'V850':[], 'V700':[], 'V500':[], \
'UP_HELI_MAX80':[], 'UP_HELI_MAX120':[], 'UP_HELI_MAX01-120':[] }
press_levels = [1000,925,850,700,600,500,400,300,250,200,150,100]
upscaled_fields = { 'UP_HELI_MAX01-120':[] }
# get closest grid boxes
print('finding closest grid points')
nngridpts = get_closest_gridbox()
print('running upscaling in parallel')
nfhr = 37
nprocs = 6
chunksize = int(math.ceil(nfhr / float(nprocs)))
pool = multiprocessing.Pool(processes=nprocs)
data = pool.map(upscale_forecast, range(nfhr), chunksize)
pool.close()
# merge dictionaries
combined = {}
for d in data: combined.update(d)
for f in upscaled_fields.keys():
for fhr in range(nfhr):
upscaled_fields[f].append(combined[fhr][f])
# this script produces files that are slightly larger than the original files - ~2KB - not sure why..
if model == 'GEFS': np.savez_compressed('/glade/work/sobash/NSC/%s_%s_mem%d_upscaled'%(sdate.strftime('%Y%m%d%H'),model,mem), a=upscaled_fields)
else: np.savez_compressed('/glade/work/sobash/NSC/%s_%s_upscaled_UH01'%(sdate.strftime('%Y%m%d%H'),model), a=upscaled_fields)
#plot_field = np.array(upscaled_fields['MUCAPE'])
#print plot_field.shape
#plot_field = np.amax(plot_field, axis=0)
# plotting
plotting = False
if plotting:
levels = np.arange(250,5000,250)
#levels = np.arange(0,100,2.5)
test = readNCLcm('MPL_Reds')[10:]
cmap = colors.ListedColormap(test)
norm = colors.BoundaryNorm(levels, ncolors=cmap.N, clip=True)
##awips.pcolormesh(x81, y81, np.ma.masked_less(u_interp, 100.0), cmap=cmap, norm=norm)
awips.pcolormesh(x81, y81, plot_field, cmap=cmap, norm=norm)
#awips.pcolormesh(x81, y81, env['b'], cmap=cmap, norm=norm)
awips.drawstates()
awips.drawcountries()
awips.drawcoastlines()
plt.savefig('test.png')