pcpib = ffib.variables['pcp'][:]
lons = ffib.variables['longitude'][:]
lats = ffib.variables['latitude'][:]
cdaysib = ffib.getncattr(
'contributing_days'
) #contributing days is a global attribute rather than a variable
fftr = Dataset(infiletr, 'r')
pcptr = fftr.variables['pcp'][:]
cdaystr = fftr.getncattr(
'contributing_days'
) #contributing days is a global attribute rather than a variable
print cdaystr
print np.max(pcpib)
print np.max(pcptr)
perc_cont = np.zeros_like(pcptr)
perc_cont[:, :] = np.nan
perc_cont = (pcpib / pcptr) * 100.
print np.nanmax(perc_cont)
label = " " #""TC contribution to precipitation (%) " + str(y1) + "-" + str(y2)
bounds = np.linspace(0, 100, 11)
pl.map_composite_data(perc_cont, lats, lons, outfile, "SIO2", 'BuPu',
label, bounds, 'perc')
np.savetxt(datadirib + outfile_data, perc_cont, '%.4f')
print 'here'
pcp_sum_dict[res] = pcp_tot
cdays_sum_dict[res] = cdays
lons_overall_dict[res] = lons
lats_overall_dict[res] = lats
#print pcp_sum_dict
pcp=np.zeros((xr,yr))
for x in range(xr):
for y in range(yr):
pcp[x,y] = pcp_tot[x,y]/cdays #divide the precip total by the number of days
print np.max(pcp)
label = res #""TRMM precipitation (mm/day) " + str(y1) + "-" + str(y2)
bounds = np.linspace(0, 16, 9)
pl.map_composite_data(pcp, lats, lons, outfile, "SIO2", 'Blues', label, bounds)
if lt == 6:
continue
else:
infile = datadir + str(lt) + "/ukmo_nwp.comp_pcp_total."+str(lt)+"-days.2009-2010.n320.nc"
print infile
outfile = "ukmo_nwp.comp_pcp_total."+str(lt)+"-days.2009-2010.n320_mmday.png"
ff = Dataset(infile, 'r')
lons = ff.variables['longitude'][:]
lats = ff.variables['latitude'][:]
cdays = ff.getncattr('contributing_days') # contributing days is a global attribute rather than a variable
pcp_tot = ff.variables['unknown'][:]
print pcp_tot
pcp_sum_dict['n320'] += pcp_tot
cdays_sum_dict['n320'] += cdays
infile = datadir + "analysis.comp_pcp_tc.201903_idai.nc"
# infile = datadir + "2019/analysis.comp_pcp_tc.201903.nc"
print infile
outfile = "analysis.gpm_imerg.comp_pcp_tc.idai.png"
ff = Dataset(infile, 'r')
pcp = ff.variables['pcp'][:]
lons = ff.variables['lon'][:]
#lons = lons + 180
lats = ff.variables['lat'][:]
cdays = ff.getncattr(
'contributing_days'
) # contributing days is a global attribute rather than a variable
print cdays
pcp_new = np.zeros((1800, 3600))
for x in range(3600):
for y in range(1800):
pcp_new[y, x] = pcp[
x, y] # /cdays #divide the precip total by the number of days
print pcp_new
print np.max(pcp_new)
label = " " # ""TC-related precipitation (mm) " + str(y1) + "-" + str(y2)
bounds = np.linspace(0, 1000, 11)
pl.map_composite_data(pcp_new, lats, lons, outfile, "SIO2", 'Blues', label,
bounds)
#
print np.nanmax(trmmpcp) print np.nanmax(dens_sum_an) trmm_obs_precip_per_tc_mmper6h = trmmpcp / dens_sum_an trmm_obs_precip_per_tc_mmph = trmm_obs_precip_per_tc_mmper6h / 6 print np.nanmax(trmm_obs_precip_per_tc_mmper6h) print np.nanmax(trmm_obs_precip_per_tc_mmph) bounds1 = np.linspace(0, 50, 11) label1=' ' outfile = "trmm_pcp_per_tc.analysis_tracks.mmph_2006-2016.png" pl.map_composite_data(trmm_obs_precip_per_tc_mmph, lats, lons, outfile, "SIO3", 'Purples', label1, bounds1,"contour") #get the forecast track density for the whole period, for each lead time (saved as yearly files) fcst_dens_sum = np.zeros((7,72,144)) #lead time, lat, lon for lt in [0,1,2,3,4,5,6]: for y1, y2 in zip(year1s, year2s): densfile = ukmodensdir + str(lt) + "/" + str(y1)+str(y2) + "/ukmo_nwp.density.lt"+str(lt)+"_days."+str(y1)+str(y2)+".nc" ffdens = Dataset(densfile,'r') dens = ffdens.variables['density'][:] fcst_dens_sum[lt,:,:] += dens[:,:] print fcst_dens_sum
#avgtotpcp = totalpcp / 10
ff20182019 = Dataset(file20182019, 'r')
pcp20182019 = ff20182019.variables['pcp'][:]
print np.shape(pcp20182019)
pcp20182019_new = np.zeros((481, 640))
for x in range(640):
for y in range(481):
pcp20182019_new[y, x] = pcp20182019[x, y]
cdays20182019 = ff20182019.getncattr('contributing_days')
pcp20182019_mm = pcp20182019_new / cdays20182019
#bias = avgtotpcp - pcp20182019_new
pcp20182019_ma = np.ma.masked_where(pcp20182019_new == 0, pcp20182019_new)
totalpcp_ma = np.ma.masked_where(totalpcp == 0, totalpcp)
bias = pcp20182019_ma - (totalpcp_ma / 10)
perc = (bias / (totalpcp_ma / 10)) * 100
print bias
label3 = " " #""UKMO - TRMM precipitation bias (mm/day) " + res + " " + str(lt) + " days lead time"
bounds3 = np.linspace(-100, 100, 21)
pl.map_composite_data(perc, lats, lons, outfile_bias, "SIO2", 'RdBu', label3,
bounds3, 'bias')
bias = ecpcp_mmday - gpmpcp_mmday
print "bias max: ", np.nanmax(bias)
print "bias min: ", np.nanmin(bias)
#print ecpcp
#print gpmpcp_mmday
#print bias
label1 = " " #""UKMO precipitation (mm/day) "+res+" "+str(lt)+" days lead time"
label2 = " " #""TRMM precipitation (mm/day) "+res
label3 = " " #""UKMO - TRMM precipitation bias (mm/day) " + res + " " + str(lt) + " days lead time"
bounds1 = np.linspace(0, 60, 7)
bounds3 = np.linspace(-30, 30, 7)
pl.map_composite_data(ecpcp_mmday, lats, lons, outfile_ec, "SIO2",
'Purples', label1, bounds1)
pl.map_composite_data(gpmpcp_mmday, lats, lons, outfile_gpm, "SIO2",
'Purples', label2, bounds1)
pl.map_composite_data(bias, lats, lons, outfile_bias, "SIO2", 'RdBu',
label3, bounds3, 'bias')
#AND THEN FOR SUM, RATHER THAN MM/DAY:
ecfilesum = ecdir + "/idai_precip_sum_mm_ensmean_lt_" + str(lt) + "days.nc"
ffecsum = Dataset(ecfilesum, 'r')
ecpcpsum = ffecsum.variables['tc_precip'][:]
print np.nanmax(ecpcpsum)
biassum = ecpcpsum - gpmpcp
cdaystrmm = fftrmm.getncattr('contributing_days')
trmmpcp_mm = trmmpcp / cdaystrmm
bias = ukmopcp_mm - trmmpcp_mm
print ukmopcp_mm
print trmmpcp_mm
print bias
label1 = " " #""UKMO precipitation (mm/day) "+res+" "+str(lt)+" days lead time"
label2 = " " #""TRMM precipitation (mm/day) "+res
label3 = " " #""UKMO - TRMM precipitation bias (mm/day) " + res + " " + str(lt) + " days lead time"
bounds1 = np.linspace(0, 2, 9)
bounds3 = np.linspace(-1.5, 1.5, 7)
pl.map_composite_data(ukmopcp_mm, lats, lons, outfile_ukmo, "SIO2",
'Blues', label1, bounds1)
pl.map_composite_data(trmmpcp_mm, lats, lons, outfile_trmm, "SIO2",
'Blues', label2, bounds1)
pl.map_composite_data(bias, lats, lons, outfile_bias, "SIO2", 'RdBu',
label3, bounds3, 'bias')
#here, add the composites for the total across the entire period, once remapping is finished
#here, add the composites for the total across the entire period, once remapping is finished
for lt in [0, 1, 2, 3, 4, 5, 6]:
ukmofile = ukmodir + str(lt) + "/ukmo_nwp.comp_pcp_tc." + str(
lt) + "_days.072006-122016.n320.nc"
trmmfile = trmmdir + "trmm.comp_pcp_tc.072006-122016_n320.nc"
outfile_ukmo = "ukmo_nwp.comp_pcp_tc.072006-122016.n320." + str(
lt) + "-days.png"
outfile_trmm = "trmm.comp_pcp_tc.072006-1220160.n320.png"
trmm_data=np.genfromtxt(trmmfile) bias = ukmo_data - trmm_data print ukmo_data print trmm_data print bias label = " "#""UKMO - TRMM precipitation bias (mm/day) " + res + " " + str(lt) + " days lead time" bounds = np.linspace(-25, 25, 11) #pl.map_composite_data(bias, lats, lons, outfile_bias, "SIO2", 'PuOr', label, bounds,'bias') for season in ['DJFM','NDJFMA','MJJASO']: ukmofile = ukmodir + str(lt) + "/ukmo_nwp.perc_cont_tc_rainfall."+str(lt)+"-days.072006-122016"+season+".n320.txt" trmmfile = trmmdir + "trmm_ibtracs.perc_cont_tc_rainfall.072006-122016."+season+"_n320.txt" outfile_bias = "ukmo_bias.perc_cont_tc_rainfall.072006-122016."+str(lt)+"-days."+season+".n320.png" lonlatfile = trmmdir+"trmm.comp_pcp_tc.072006-122016_n320.nc" fflatlon = Dataset(lonlatfile, 'r') lons = fflatlon.variables['longitude'][:] lats = fflatlon.variables['latitude'][:] ukmo_data = np.genfromtxt(ukmofile) trmm_data = np.genfromtxt(trmmfile) bias = ukmo_data - trmm_data label = " " # ""UKMO - TRMM precipitation bias (mm/day) " + res + " " + str(lt) + " days lead time" bounds = np.linspace(-25, 25, 11) pl.map_composite_data(bias, lats, lons, outfile_bias, "SIO2", 'PuOr', label, bounds, 'bias')
#pl.map_composite_data(dens, lats,lons, outfile, "SIO3", 'Greens', label,bounds,"contour")
lons_overall = lons
lats_overall = lats
dens_sum_nwp += dens
infile_ib = datadir_ib + "analysis.density." + str(y1) + str(
y2) + ".nc"
outfile_ib = "analysis_track_density." + str(y1) + str(y2) + ".png"
ffib = Dataset(infile_ib, 'r')
dens_ib = ffib.variables['density'][:]
label = ' '
bounds = np.linspace(0, 20, 11)
pl.map_composite_data(dens_ib, lats, lons, outfile_ib, "SIO3",
'Greens', label, bounds, "contour")
dens_sum_ib += dens_ib
outfile_bias = "ukmo_nwp.track_density_bias.vs_analysis.lt" + str(
lt) + "_days." + str(y1) + str(y2) + ".png"
bias = dens - dens_ib
#bounds_bias = np.linspace(-5, 5, 11)
bounds_bias = np.array([-5, -4, -3, -2, -1, 1, 2, 3, 4, 5])
pl.map_composite_data(bias, lats, lons, outfile_bias, "SIO3", 'BrBG',
label, bounds_bias, "contour", 'bias')
#map the track density across all the years/TC seasons
#print dens_sum
#print lons_overall