def _read_diag_conv(fname, endian='big'):
'''
Helper function to read a conventional diagnostic file
'''
try:
diag = _rd.diag_conv(fname, endian=endian)
diag.read_obs()
except:
raise Exception('Error handling %s' % fname)
return diag
fig = plt.figure(figsize=(16, 8), dpi=100)
plt.subplots_adjust(top=0.9, bottom=0.1, right=0.98, left=0.02)
gs = gspec.GridSpec(1, 2)
ax = plt.subplot(gs[0])
plt.title("North Hemisphere - Polar")
m = Basemap(projection='npstere', boundinglat=55, lon_0=180, resolution='l')
m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)
m.fillcontinents(color='grey', alpha=0.7, lake_color='grey', zorder=10)
m.drawcoastlines(linewidth=0.1, color="white")
m.drawparallels(np.asarray([80, 60]), labels=[1, 1, 0, 1])
m.drawmeridians(np.arange(-180, 180, 30), labels=[1, 1, 0, 1])
# read header
diag_conv = read_diag.diag_conv(obsfile, endian='big')
print('total number of obs = ', diag_conv.nobs)
# read data
diag_conv.read_obs()
#instrumentlist = [' v',' u',' t',' q','gps']
instrumentlist = [' v']
for instrument in instrumentlist:
if instrument == ' v':
obsname = 'V-Wind'
elif instrument == ' u':
obsname = 'U-Wind'
elif instrument == ' t':
obsname = 'Temperature'
import read_diag import numpy as np obsfile = 'diag_conv_ges.2015102700' diag_conv = read_diag.diag_conv(obsfile,endian='big') print 'total number of obs = ',diag_conv.nobs diag_conv.read_obs() # print o-f stats for ps obs # all ship ps obs used in assimilation indxps = np.logical_and(diag_conv.obtype == ' ps', diag_conv.used == 1) indxps = np.logical_and(indxps, diag_conv.code == 180) nobsps = indxps.sum() fitsq = ((diag_conv.hx[indxps]-diag_conv.obs[indxps])**2).mean() print nobsps, np.sqrt(fitsq)
pbins = np.zeros(nlevs + 1, np.float)
pbins[0:nlevs] = levs1
pbins[nlevs] = levs2[-1]
for nlev in range(18, nlevs):
levs[nlev] = 0.5 * (levs1[nlev] + levs2[nlev])
rms_wind = np.zeros(len(levs), np.float)
rms_temp = np.zeros(len(levs), np.float)
bias_temp = np.zeros(len(levs), np.float)
count_temp = np.zeros(len(levs), np.int)
count_wind = np.zeros(len(levs), np.int)
for date in dates:
obsfile = os.path.join(datapath,
'%s/diag_conv_ges.%s_%s' % (date, date, runid))
print obsfile
diag_conv = read_diag.diag_conv(obsfile, endian=endian)
diag_conv.read_obs()
if sondesonly:
insitu_wind = np.logical_or(
diag_conv.code == 220, # sondes
diag_conv.code == 232) # drops
insitu_wind = np.logical_or(insitu_wind,
diag_conv.code == 221) # pibals
else:
insitu_wind = np.logical_and(diag_conv.code >= 280,
diag_conv.code <= 282) #sfc
# sones, pibals
insitu_wind = np.logical_or(insitu_wind,\
np.logical_or(diag_conv.code == 220, diag_conv.code == 221))
# aircraft, drops
insitu_wind = np.logical_or(insitu_wind,\
levs2[22] = 0.
levs1[0] = 1200.
pbins = np.zeros(nlevs+1,np.float)
pbins[0:nlevs] = levs1; pbins[nlevs] = levs2[-1]
for nlev in range(18,nlevs):
levs[nlev] = 0.5*(levs1[nlev]+levs2[nlev])
rms_wind = np.zeros(len(levs),np.float)
rms_temp = np.zeros(len(levs),np.float)
bias_temp = np.zeros(len(levs),np.float)
count_temp = np.zeros(len(levs),np.int)
count_wind = np.zeros(len(levs),np.int)
for date in dates:
obsfile = os.path.join(datapath,'%s/diag_conv_ges.%s_%s' % (date,date,runid))
print obsfile
diag_conv = read_diag.diag_conv(obsfile,endian=endian)
diag_conv.read_obs()
if sondesonly:
insitu_wind = np.logical_or(diag_conv.code == 220, # sondes
diag_conv.code == 232) # drops
insitu_wind = np.logical_or(insitu_wind, diag_conv.code == 221) # pibals
else:
insitu_wind = np.logical_and(diag_conv.code >= 280, diag_conv.code <= 282) #sfc
# sones, pibals
insitu_wind = np.logical_or(insitu_wind,\
np.logical_or(diag_conv.code == 220, diag_conv.code == 221))
# aircraft, drops
insitu_wind = np.logical_or(insitu_wind,\
np.logical_and(diag_conv.code >= 230, diag_conv.code <= 235))
if sondesonly:
insitu_temp = np.logical_or(diag_conv.code == 120, # sondes
