= 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:
{{{
#!python
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:
{{{
#!python
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.