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make_osr_climo.py
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make_osr_climo.py
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#!/usr/bin/env python
import numpy as np
from datetime import *
import time as t
import os, sys
from get_osr_gridded_by_day_hr import *
import pickle
import scipy.ndimage.filters
import xarray as xr
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import *
from matplotlib.colors import BoundaryNorm
def readSevereClimo(fname, day_of_year, hr):
from scipy.interpolate import RectBivariateSpline
data = np.load(fname)
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)
x, y = awips(data['lons'], data['lats'])
spline = RectBivariateSpline(x[0,:], y[:,0], data['severe'][day_of_year-1,hr,:].T, kx=3, ky=3)
interp_data = spline.ev(grid81[2].ravel(), grid81[3].ravel())
return np.reshape(interp_data, (65,93))
def computeClimo():
gmt2cst = timedelta(hours=6)
m = 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 = m.makegrid(93, 65, returnxy=True)
mask = pickle.load(open('/glade/u/home/sobash/2013RT/usamask.pk', 'rb'))
mask = np.logical_not(mask)
mask = mask.reshape((65,93))
osr81_sum_by_year = []
for year in range(1986,2016):
#for year in range(2001,2016):
#times in database are in CST, so if we want 00z-00z, subtract 6 hrs
obs_start, obs_end = datetime(year,1,1,0,0,0) - gmt2cst, datetime(year,12,31,23,59,0) - gmt2cst
if year < 2010:
#osr81, osr81_count = get_osr_gridded(obs_start, obs_end, 93, 65, ['wind'])
#osr81, osr81_count = get_osr_gridded_by_day(obs_start, obs_end, 93, 65, ['wind'])
#osr81, osr81_count = get_osr_gridded_by_day_hr(obs_start, obs_end, 93, 65, ['wind','hailone','torn'])
osr81, osr81_count = get_osr_gridded_by_day_hr(obs_start, obs_end, 93, 65, ['sighail'])
else:
#osr81, osr81_count = get_osr_gridded(obs_start, obs_end, 93, 65, ['wind'])
#osr81, osr81_count = get_osr_gridded_by_day(obs_start, obs_end, 93, 65, ['wind'])
#osr81, osr81_count = get_osr_gridded_by_day_hr(obs_start, obs_end, 93, 65, ['wind','hailone','torn'])
osr81, osr81_count = get_osr_gridded_by_day_hr(obs_start, obs_end, 93, 65, ['sighail'])
osr81[:,:,mask] = 0.0
osr81_count[:,:,mask] = 0.0
osr81_sum_by_year.append(osr81)
print(year, osr81.sum(), osr81_count.sum())
osr81_sum_by_year = np.array(osr81_sum_by_year)
data = []
for sig in [40, 120]:
#determine if report occurred within 2-hr and X-km of central grid pt
if sig == 40: osr81_sum_by_year = scipy.ndimage.filters.maximum_filter(osr81_sum_by_year, footprint=np.ones((1,1,5,1,1)), mode='wrap')
if sig == 120: osr81_sum_by_year = scipy.ndimage.filters.maximum_filter(osr81_sum_by_year, footprint=np.ones((1,1,5,3,3)), mode='wrap')
frequency = osr81_sum_by_year.mean(axis=0)
frequency = scipy.ndimage.filters.gaussian_filter(frequency, sigma=[15,1.5,1.5,1.5], mode='wrap')
print(frequency.shape)
#for i in range(0,101,10): print(i, np.percentile(frequency, i))
data.append( frequency )
ds = xr.Dataset(data_vars={
'climo': ( ['window', 'day', 'hr', 'y', 'x'], np.array(data).astype('float32') ),
},
coords={'window': [40, 120],
'day': range(1,367),
'hr': range(0,24),
'lon': (('y', 'x'), grid81[0].astype('float32')),
'lat': (('y', 'x'), grid81[1].astype('float32')),
},
attrs={ 'output time':datetime.utcnow().strftime('%Y-%m-%d %H:%m:%s UTC') },
)
ds.to_netcdf('climo_severe_2hr_sighail.nc')
#np.savez('climo_severe_120km_2hr_torn.npz', lats=grid81[1], lons=grid81[0], severe=frequency.astype('float32'))
#np.savez('climo_severe_40km_2hr_torn.npz', lats=grid81[1], lons=grid81[0], severe=frequency.astype('float32'))
def plot_climo():
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# setup color table
levels = np.arange(0,0.05,0.005)
#levels = np.arange(0,0.1,0.01)
cmap = plt.get_cmap('Reds')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
fig = plt.figure(figsize=(9,9))
# old basemap plotting code
m = 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='l', area_thresh=10000.)
grid81 = m.makegrid(93, 65, returnxy=True)
xorig, yorig = m(grid81[0], grid81[1])
x = (xorig[1:,1:] + xorig[:-1,:-1])/2.0
y = (yorig[1:,1:] + yorig[:-1,:-1])/2.0
m.drawcoastlines()
m.drawstates()
m.drawcountries()
#a = m.pcolormesh(x, y, readSevereClimo('../severe.npz', 121)[1:,1:], cmap=cmap, norm=norm)
#a = plt.pcolormesh(grid81[0], grid81[1],readSevereClimo('climo_torn_15yr.npz', 121)[1:,1:], cmap=cmap, norm=norm, transform=ccrs.LambertConformal())
# cartopy code
#m = 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 = m.makegrid(93, 65, returnxy=True)
#ax = plt.axes(projection=ccrs.LambertConformal(central_latitude=38.33643, central_longitude=-97.53348, standard_parallels=(32,46)))
#ax.set_extent([-122, -70, 24, 50], ccrs.PlateCarree())
#states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none', name='admin_1_states_provinces_lakes_shp')
#ax.add_feature(states, linewidth=0.25, color='gray')
#ax.coastlines('50m', linewidth=0.25, color='gray')
#a = plt.pcolormesh(lons, lats, np.ma.masked_less(climo_to_plot[1:,1:], 0.0025), cmap=cmap, norm=norm, transform=ccrs.PlateCarree(), zorder=1000)
lons = (grid81[0][1:,1:] + grid81[0][:-1,:-1])/2.0
lats = (grid81[1][1:,1:] + grid81[1][:-1,:-1])/2.0
a = m.pcolormesh(x, y, np.ma.masked_less(climo_to_plot[1:,1:], 0.0025), cmap=cmap, norm=norm)
cbar = plt.colorbar(a, shrink=0.95, pad=0, orientation='horizontal')
plt.savefig('test%02d.png'%f, dpi=200, bbox_inches='tight')
computeClimo()
#data = np.load('climo_severe_40km_2hr.npz')
#climo = data['severe']
#for f in range(0,24):
# climo_to_plot = climo[:,f,:].mean(axis=0)
# #climo_to_plot = readSevereClimo('climo_severe_120km_2hr.npz', 181, f)
# print(climo_to_plot.max(), climo_to_plot.min(), climo_to_plot.shape)
# plot_climo()