Visualization and animating of Pan European Climate Simulation with the WRF Model using ParaView Python-Scripting

Data

In this example, one data file containing 12 timesteps was given. The file is stored at /arch/software/grafprod/data/slts.fz-juelich.de/k.goergen/WRF_initial_example/wrf_rv025_r07_test.wrfout_3km.20100702130000-20100703000000.nc in netCDF format. As netCDF 3 is used, the file had to be converted to netCDF 4 (which is hdf5) via "nccopy -k 4 foo3.nc foo4c.h5".

Variables of Interest

3D Scalar Variables

The 3D variables are located on a grid of size 49x1552x1600. As for each variable all 12 timesteps are stored in one hdf5 array, the size of this array is 12x49x1552x1600, obviously.
Here is a list of interesting scalar variables:

• CLDFRA: Cloud Fraction (values from 0 to 1)
• QNICE: Ice Number concentration (values from -1.1e-8 to 1.3e6, good value for visualisation: 10000)
• QNRAIN: Rain Number concentration (values from -3e-5 to 620000, good value for visualisation: 5000)
• QVAPOR: Water vapor mixing ratio (values from -0.0043 bis 0.0207, good value for visualisation: 0.005)

3D Vector Variables

The wind components are stored on a staggered grid. Please note, that therefore the array size of those components vary along one axis each.
Vector Components:

• U: x-wind component (12 x 49 x 1552 x 1601)
• V: y-wind component (12 x 49 x 1553 x 1600)
• w: z-wind component (12 x 50 x 1552 x 1600)

2D Scalar Variables

There are also a couple of surface related 2D variables includes in the data, located on a grid of size 12x1552x1600.
Some examples:

• HGT: Terrain Height
• LH: Latent Heat Flux at the Surface
• LANDMASK
• LU_INDEX
• QFX
• SNOWH
• SST
• T2
• TMN
• VEGFRA

Coordinates

The coordinates of the 3D grid are given by lon, lat and two pressure values:

• XLON: 12 x 1552 x 1600
• XLAT: 12 x 1552 x 1600
• PH: 12 x 50 x 1552 x 1600
• PHB: 12 x 50 x 1552 x 1600

As ParaView can not load the coordinates of the grid from the original data, a new data file coordinates.h5 is generated by the Python script generate_coordinates.py. In this script, the longitude values are corrected by the cosine of the latitude values:

xlat = np.cos(xlat/180.0*np.pi)
print "generating longitude"
dsetIn = fIn['/XLONG']
dsetOut = fOut.create_dataset("/XLONG", (num_x, num_y, num_z), dtype=np.float32)
xlon = dsetIn[0, : , :]
mi = np.min(xlon)
ma = np.max(xlon)
xlon = (xlon-(ma+mi)/2.0) * xlat

Also the height value is calculated as PH+PHB. As these pressure values are located on a staggered grid, two grid levels must be averaged:

dsetIn1 = fIn['/PH']
dsetIn2 = fIn['/PHB']
dsetOut = fOut.create_dataset("/Z", (num_x, num_y, num_z), dtype=np.float32)
ph1 = dsetIn1[0, 0, : , :]
phb1 = dsetIn2[0, 0, : , :]

for z in range(num_x):
  sys.stdout.write("%d " % (z))
  ph2 = dsetIn1[0, z+1, : , :]
  phb2 = dsetIn2[0, z+1, : , :]
  result = (ph1+phb1+ph2+phb2)*0.00025/(9.81*2)
  dsetOut[z, : , :] = result
  print "min=%f, max=%f" % (np.min(result), np.max(result))
  ph1 = ph2
  phb1 = phb2

Time Information

The time value of the 12 timesteps is store in the variable XTIME, which is just a one-dimensional array of size 12.