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pcc_imerg_rado.py
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pcc_imerg_rado.py
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"""
Einlesen und darstellen von GPM IMERG und Radolan Dateien
"""
import h5py
import numpy as np
import matplotlib.pyplot as plt
import wradlib
import glob
from scipy import stats, linspace
import wradlib as wrl
from osgeo import osr
from pcc import get_time_of_gpm
from pcc import cut_the_swath
## Landgrenzenfunktion
## -------------------
from pcc import boxpol_pos
bonn_pos = boxpol_pos()
bx, by = bonn_pos['gkx_ppi'], bonn_pos['gky_ppi']
bonnlat, bonnlon = bonn_pos['lat_ppi'], bonn_pos['lon_ppi']
from pcc import plot_borders
from pcc import plot_radar
from pcc import get_miub_cmap
my_cmap = get_miub_cmap()
from pcc import get_my_cmap
my_cmap2 = get_my_cmap()
ZP = '20141007'#'20140609'
#year, m, d, ht, mt, st = ZP[0:4], ZP[4:6], ZP[6:8], ZP[8:10], ZP[10:12], ZP[12:14]
year, m, d = ZP[0:4], ZP[4:6], ZP[6:8]
ye = ZP[2:4]
R_all = []
G_all_HQ = []
G_all_IR = []
G_all_MI = []
## Read GPM Data
## -------------
pfad2 = ('/home/velibor/shkgpm/data/imerg/'+str(year)+str(m)+str(d)+'/*.HDF5')
pfad_gpm = sorted(glob.glob(pfad2))
#for jjj in range(len(pfad_gpm)):
for jjj in range(4,6):
pfad_gpm_g = pfad_gpm[jjj]
imerg_zeit = pfad_gpm_g[62:86]
print 'Imerg_Zeit: ',imerg_zeit
gpmimerg = h5py.File(pfad_gpm_g, 'r')
gpm_lat=np.array(gpmimerg['Grid']['lat'])
gpm_lon=np.array(gpmimerg['Grid']['lon'])
gpm_time =np.array(gpmimerg['Grid']['HQobservationTime'], dtype=float)
gpm_time[gpm_time==-9999]= 0
#gpm_pp=np.array(gpmimerg['Grid']['HQprecipitation'])
gpm_pp=np.array(gpmimerg['Grid']['precipitationCal'])
gpm_pp_ir=np.array(gpmimerg['Grid']['IRprecipitation'])
#gpm_pp_ir=np.array(gpmimerg['Grid']['precipitationUncal'])
gpm_pp_mi=np.array(gpmimerg['Grid']['HQprecipitation'])
gpm_pp[gpm_pp==-9999.9]= np.nan
gpm_pp_ir[gpm_pp_ir==-9999.9]= np.nan
gpm_pp_mi[gpm_pp_mi==-9999.9]= np.nan
gpm_pp = np.transpose(gpm_pp)
gpm_pp_ir = np.transpose(gpm_pp_ir)
gpm_pp_mi = np.transpose(gpm_pp_mi)
# Cut -------------------------------------------------------------------------
#lim1 [xmin, xmax, ymin,ymax]
#limit= np.array([-140, -90, -10, 30])#pazifik
#limit= np.array([65, 100, 5, 40]) #indien
#limit= np.array([5, 9, 49, 53]) #bonn
#limit= np.array([-140, -80, -5, 30])#pazifik
#limit= np.array([2,18,44,58]) #deutschland
limit= np.array([2.0735617005681379,
15.704155593113517,
46.952580411190304,
54.896591448461479]) #RADOLAN
#limit= np.array([-180,180,-90, 90]) #Welt
lon_ii = np.array(np.where((limit[0] < gpm_lon) & (limit[1] > gpm_lon)))
lat_ii = np.array(np.where((limit[2] < gpm_lat) & (limit[3] > gpm_lat)))
gpm_lon = gpm_lon[lon_ii[0,0]:lon_ii[0,-1]]
gpm_lat = gpm_lat[lat_ii[0,0]:lat_ii[0,-1]]
gpm_pp = gpm_pp[lat_ii[0,0]:lat_ii[0,-1], lon_ii[0,0]:lon_ii[0,-1]]
gpm_pp_ir = gpm_pp_ir[lat_ii[0,0]:lat_ii[0,-1], lon_ii[0,0]:lon_ii[0,-1]]
gpm_pp_mi = gpm_pp_mi[lat_ii[0,0]:lat_ii[0,-1], lon_ii[0,0]:lon_ii[0,-1]]
#print gpm_pp.shape, gpm_pp_ir.shape, gpm_pp_mi.shape
gpm_time = gpm_time[lat_ii[0,0]:lat_ii[0,-1], lon_ii[0,0]:lon_ii[0,-1]]
print 'gpm_time: ',gpm_time
if np.sum(gpm_time)==0:
ht, mt = imerg_zeit[10:12], str(int(imerg_zeit[12:14])+15)
print mt
if np.sum(gpm_time)!=0:
ht, mt = imerg_zeit[10:12], str(((int(np.nanmax(gpm_time))/5)*5)+15)
print mt
## Read RADOLAN Data
## -----------------
#TODO: Mean von 30 min RADOLAN nehmen! [lit. Oliveira, Charactersistics and Durial....]
r_pro = 'rx'
pfad = ('/automount/radar/dwd/'+ r_pro +'/'+str(year)+'/'+str(year)+'-'+
str(m)+'/'+ str(year)+'-'+str(m)+'-'+str(d)+'/raa01-'+r_pro+'_10000-'+
str(ye)+str(m)+ str(d)+str(ht)+str(mt)+'-dwd---bin.gz')
pfad_radolan = pfad[:-3]
#pfad_radolan = '/automount/radar/dwd/rw/2014/2014-10/2014-10-07/raa01-rw_10000-1410070250-dwd---bin.gz'
try:
rw_filename = wradlib.util.get_wradlib_data_file(pfad)
except EnvironmentError:
rw_filename = wradlib.util.get_wradlib_data_file(pfad_radolan)
rwdata, rwattrs = wradlib.io.read_radolan_composite(rw_filename)
radolan_zeit = rwattrs['datetime'].strftime("%Y.%m.%d -- %H:%M:%S")
print radolan_zeit
#Binaere Grid
rn = rwdata.copy()
rn[rn != -9999] = 1
rn[rn == -9999] = 0
radolan_grid_xy = wradlib.georef.get_radolan_grid(900,900)
x = radolan_grid_xy[:,:,0]
y = radolan_grid_xy[:,:,1]
rwdata = np.ma.masked_equal(rwdata, -9999) / 2 - 32.5 # Einheit 1/100mm pro 5min
#rwdata[rwdata < 0] = np.nan
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
xx, yy = wradlib.georef.reproject(x, y, projection_target=proj_wgs , projection_source=proj_stereo)
## INTERLOLATION
## --------------
gx, gy = np.meshgrid(gpm_lon, gpm_lat)
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack((gx.ravel(), gy.ravel())).transpose()
xy = np.vstack((xx.ravel(), yy.ravel())).transpose()
mask = ~np.isnan(rwdata)
#rwdata[rwdata < 0] = np.nan
result = wrl.ipol.interpolate(xy, grid_gpm_xy, rwdata[mask].reshape(900*900,1), wrl.ipol.Idw, nnearest=4)
result = np.ma.masked_invalid(result)
rrr = result.reshape(gx.shape)
Z = wradlib.trafo.idecibel(rrr)
rrr = wradlib.zr.z_to_r(Z, a=200., b=1.6)
Z2 = wradlib.trafo.idecibel(rwdata)
rwdata = wradlib.zr.z_to_r(Z2, a=200., b=1.6)
## Interpolation of the binary Grid
## ------------------------------
res_bin = wrl.ipol.interpolate(xy, grid_gpm_xy, rn.reshape(900*900,1), wrl.ipol.Idw, nnearest=4)
res_bin = res_bin.reshape(gx.shape)
res_bin[res_bin!=0]= 1 # Randkorrektur
rand_y_unten = 46.952580411190304
rand_y_oben = 54.896591448461479
rand_x_rechts = 15.704155593113517
rrr[np.where(gy < rand_y_unten)] = np.nan
rrr[np.where(gy > rand_y_oben)] = np.nan
rrr[np.where(gx > rand_x_rechts)] = np.nan
res_bin[np.where(gy < rand_y_unten)] = np.nan
res_bin[np.where(gy > rand_y_oben)] = np.nan
res_bin[np.where(gx > rand_x_rechts)] = np.nan
res_bin[res_bin == 0] = np.nan #check nur 1 un NaN
gpm_pp = gpm_pp * res_bin
gpm_pp_ir = gpm_pp_ir * res_bin
gpm_pp_mi = gpm_pp_mi * res_bin
gpm_pp[gpm_pp<= 0]= np.nan
gpm_pp_ir[gpm_pp_ir<= 0]= np.nan
gpm_pp_mi[gpm_pp_mi<= 0]= np.nan
#Todo: Radolan ueber 30 min mitteln
# PLot ------------------------------------------------------------------------
from pcc import plot_world
fig = plt.figure(figsize=(16,12))
ax11 = fig.add_subplot(242, aspect='equal')
pm2 = plt.pcolormesh(gpm_lon,gpm_lat,np.ma.masked_invalid(gpm_pp),
vmin=0.1, vmax=20,cmap=my_cmap)
cbar = plt.colorbar(pm2, shrink=0.75, orientation='horizontal')
cbar.set_label("RainRate [mm/h]")
#plot_world(ax11,limit[0],limit[1],limit[2],limit[3])
plt.xlabel("lon")
plt.ylabel("lat")
plt.title('IMERG Multisat Gaug Calibration :\n ' + imerg_zeit)
plt.xlim(limit[0],limit[1])
plt.ylim(limit[2],limit[3])
plt.grid()
ax12 = fig.add_subplot(241, aspect='equal')
pm2 = plt.pcolormesh(gpm_lon, gpm_lat,np.ma.masked_invalid(rrr),
vmin=0.1, vmax=20,cmap=my_cmap)
cbar = plt.colorbar(pm2, shrink=0.75, orientation='horizontal')
cbar.set_label("RainRate [mm/h]")
#plot_world(ax12,limit[0],limit[1],limit[2],limit[3])
plt.xlabel("lon")
plt.ylabel("lat")
plt.title('RADOLAN IMERG GRID: \n' + ZP +'--' + ht+':'+mt)
plt.xlim(limit[0],limit[1])
plt.ylim(limit[2],limit[3])
plt.grid()
ax13 = fig.add_subplot(243, aspect='equal')
pm2 = plt.pcolormesh(gpm_lon,gpm_lat,np.ma.masked_invalid(gpm_pp_ir),
vmin=0.1, vmax=20,cmap=my_cmap)
cbar = plt.colorbar(pm2, shrink=0.75, orientation='horizontal')
cbar.set_label("RainRate [mm/h]")
#plot_world(ax13,limit[0],limit[1],limit[2],limit[3])
plt.xlabel("lon")
plt.ylabel("lat")
plt.title('IMERG IR : \n' + imerg_zeit)
plt.xlim(limit[0],limit[1])
plt.ylim(limit[2],limit[3])
plt.grid()
ax14 = fig.add_subplot(244, aspect='equal')
pm2 = plt.pcolormesh(gpm_lon,gpm_lat,np.ma.masked_invalid(gpm_pp_mi),
vmin=0.1, vmax=20,cmap=my_cmap)
cbar = plt.colorbar(pm2, shrink=0.75, orientation='horizontal')
cbar.set_label("RainRate [mm/h]")
#plot_world(ax14,limit[0],limit[1],limit[2],limit[3])
plt.xlabel("lon")
plt.ylabel("lat")
plt.title('IMERG Microwave : \n' + imerg_zeit)
plt.xlim(limit[0],limit[1])
plt.ylim(limit[2],limit[3])
plt.grid()
ax15 = fig.add_subplot(245, aspect='equal')
pm25 = plt.pcolormesh(xx, yy,rwdata,
vmin=0.1, vmax=20,cmap=my_cmap)
cbar = plt.colorbar(pm25, shrink=0.75, orientation='horizontal')
cbar.set_label("RainRate [mm/h]")
#plot_world(ax15,limit[0],limit[1],limit[2],limit[3])
plt.xlabel("lon")
plt.ylabel("lat")
plt.title('RADOLAN: \n' + ZP +'--' + ht+':'+mt)
plt.xlim(limit[0],limit[1])
plt.ylim(limit[2],limit[3])
plt.grid()
from pcc import plot_scatter
ax16 = fig.add_subplot(246, aspect='equal')
try:
plot_scatter(gpm_pp, rrr)
except:
pass
import matplotlib as mpl
mean = [ np.nanmean(gpm_pp),np.nanmean(rrr)]
width = np.nanstd(gpm_pp)
height = np.nanstd(rrr)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax11.add_patch(ell)
plt.xlabel(('GPM IMERG (mm/h)'))
plt.ylabel(('RADOLAN (mm/h)'))
plt.grid()
ax17 = fig.add_subplot(247, aspect='equal')
try:
plot_scatter(gpm_pp_ir, rrr)
except:
pass
import matplotlib as mpl
mean = [ np.nanmean(gpm_pp_ir),np.nanmean(rrr)]
width = np.nanstd(gpm_pp_ir)
height = np.nanstd(rrr)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax11.add_patch(ell)
plt.xlabel(('GPM IMERG IR (mm/h)'))
plt.ylabel(('RADOLAN (mm/h)'))
plt.grid()
ax18 = fig.add_subplot(248, aspect='equal')
try:
plot_scatter(gpm_pp_mi, rrr)
except:
pass
import matplotlib as mpl
mean = [ np.nanmean(gpm_pp_mi),np.nanmean(rrr)]
width = np.nanstd(gpm_pp_mi)
height = np.nanstd(rrr)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax11.add_patch(ell)
plt.xlabel(('GPM IMERG MI (mm/h)'))
plt.ylabel(('RADOLAN (mm/h)'))
plt.grid()
plt.tight_layout()
#plt.savefig('/home/velibor/shkgpm/plot/imerg/test_imerg_'+imerg_zeit + '.png' )
#plt.show()
#plt.close()
plt.show()
#R_all.append(rrr.reshape(rrr.shape[0]*rrr.shape[1]))
#G_all_HQ.append(gpm_pp.reshape(gpm_pp.shape[0]*gpm_pp.shape[1]))
#G_all_IR.append(gpm_pp_ir.reshape(gpm_pp_ir.shape[0]*gpm_pp_ir.shape[1]))
#G_all_MI.append(gpm_pp_mi.reshape(gpm_pp_mi.shape[0]*gpm_pp_mi.shape[1]))
"""
#print R_all.shape, G_all_HQ.shape, G_all_IR.shape, G_all_MI.shape
G_all_HQ = np.concatenate(G_all_HQ,axis=0)
G_all_IR = np.concatenate(G_all_IR,axis=0)
#G_all_MI = np.concatenate(G_all_MI,axis=0)
R_all = np.concatenate(R_all,axis=0)
fig = plt.figure(figsize=(12,12))
ax111 = fig.add_subplot(121, aspect='equal')
plot_scatter(G_all_HQ, R_all)
import matplotlib as mpl
mean = [ np.nanmean(G_all_HQ),np.nanmean(R_all)]
width = np.nanstd(G_all_HQ)
height = np.nanstd(R_all)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax111.add_patch(ell)
plt.xlabel(('GPM IMERG HQ (mm/h)'))
plt.ylabel(('RADOLAN (mm/h)'))
plt.grid()
ax121 = fig.add_subplot(122, aspect='equal')
plot_scatter(G_all_IR, R_all)
import matplotlib as mpl
mean = [ np.nanmean(G_all_IR),np.nanmean(R_all)]
width = np.nanstd(G_all_IR)
height = np.nanstd(R_all)
angle = 0
ell = mpl.patches.Ellipse(xy=mean, width=width, height=height,
angle=180+angle, color='blue', alpha=0.8,
fill=False, ls='--', label='Std')
ax121.add_patch(ell)
plt.xlabel(('GPM IMERG IR (mm/h)'))
plt.ylabel(('RADOLAN (mm/h)'))
plt.grid()
plt.savefig('/home/velibor/shkgpm/plot/imerg/neu/all_gpm_imerg_radolan_'+ZP + '.png' )
#plt.show()
plt.close()"""