Changes between Version 1 and Version 2 of Examples/WRF

Timestamp:

07/20/18 11:24:18 (6 years ago)

Author:

Herwig Zilken

Comment:

--

Examples/WRF

@@ -6 +6 @@
 == 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.
+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:
 
@@ -15 +15 @@
 
 === 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
+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)
@@ -22 +22 @@
 
 === 2D Scalar Variables ===
-There are also a couple of surface related 2D variables includes in the data, located on a grid of size 12x1552x1600.
+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.