= Visualisation and Animation of ERA5 Reanalysis Climate Data =
== Dataformats and Tools ==
=== NetCDF and HDF5 on JURECA ===
The typical data format in climate science is NetCDF. Though NetCDF files can be loaded in !ParaView, this approach is very unflexible, as !ParaViews NetCDF reader makes some fixed assumptions regarding some naming conventions of the variables.
Fortunately, NetCDF files geenrated with the newer NetCDF version 4 are based on HDF5 and can be loaded with !ParaViews [http://xdmf.org XDMF] reader.
In case one has older NetCDF files of version 3, a conversion from NetCDF-3 to NetCDF-4 is necessary. So one has to deal with som NetCDF and HDF5 related tools.

How to find out what modules to load for NetCDF/HDF5 on JURECA:
{{{
#!bash
module spider netcdf
module spider hdf5
}}}

Load modules e.g. by:
{{{
#!bash
module load Stages/2017a  GCC/5.4.0  ParaStationMPI/5.1.9-1
module load netCDF/4.4.1.1
module load HDF5/1.8.18
}}}

Just as a reminder some examples of how to use h5dump:
{{{
#!bash
h5dump -H wrf_rv025_r07_test.wrfout_3km.20100702130000-20100703000000.h5 | grep DATASET
h5dump -a /T/description test.h5 
h5dump -d XLAT -c "1,1,100" test.h5 # shows the 100 first entries from the 3d array XLAT
}}}

How to find out the version of a netcdf file:
{{{
#!bash
ncdump -k foo.nc
}}}
If ncdump returns netCDF-4, or netCDF-4  classic  model, then congratulations, you already have an HDF file, as netCDF-4 is the netCDF data model implemented using HDF5 as the storage layer. These files can be ready by the HDF library version 1.8 or later, and from what I can tell, pytables. If ncdump returns classic or 64-bit offset, then you are using netCDF-3 and will need to convert to netCDF-4 (and thus HDF5). The good news is that if you have netCDF installed, you can do the conversion pretty easily:
{{{
#!bash
nccopy -k 4 foo3.nc foo4c.nc
}}}

Graphical HDF5 viewer:
HDFView is a very handy tool to investigate hdf5 files. To open HDFView on JURECA, either click on the HDFView icon on the desktop. Or launch HDFView by
{{{
#!bash
/usr/local/jsc/etc/xdg/scripts/launch_hdfview.sh
}}}

=== Python on JURECA ===
To use python on JURECA, you have to load some modules first. Here are some useful commands:
{{{
#!bash
module spider python
}}}
{{{
#!bash
module use otherstages
module --force purge && module load Stages/Devel-2017b  GCC/7.2.0  ParaStationMPI/5.2.0-1 h5py/2.7.1-Python-2.7.14
}}}

== Overview about the Data Files ==
The files are stored at /data/slmet/slmet111/met_data/ecmwf/era5/netcdf4/2017/

The files cover the months June and August 2017 in 1 h steps.
For every file, its date is recorded in its filename. e.g. 2017061516_ml.nc (YYYYMMDDHH)

Some interesting variables stored in the 3D *ml-files:
- cc (1 x 137 x 601 x 1200): Fraction of cloud cover
- ciwc (1 x 137 x 601 x 1200): Specific cloud ice water content
- clwc (1 x 137 x 601 x 1200): Specific cloud liquid water content
- d (1 x 137 x 601 x 1200): divergence_of_wind
- o3 (1 x 137 x 601 x 1200): Ozone mass mixing ratio
- q (1 x 137 x 601 x 1200): Specific humidity
- t (1 x 137 x 601 x 1200): Temperature
- u (1 x 137 x 601 x 1200): U component of wind (eastward wind)
- v (1 x 137 x 601 x 1200): V component of wind (northward wind)
- w (1 x 137 x 601 x 1200): Vertical velocity
- vo (1 x 137 x 601 x 1200): Vorticity (relative)

Variables related to coordinates:\\
- lat (601): latitude (degrees north), ranging from 90 to -90
- lon (1200): longitude (degrees east), ranging from 0 to 359.7
- lev, lev_2 (137): hybrid_sigma_pressure, ranging from 1 to 137 (137 is ground level!)

Calculation of coordinates:\\
!ParaView natively does not understand the original coordinates (lat, lon, lev_2). Therefore, these must be converted into a "structured grid" data structure, where every node of the 3D grid has its own 3D coordinate. This calculation is done in the "generate_coordinates.py" script.
In this script also the conversion to Cartesian coordinates takes place essentially via:
{{{
#!python
  height = (137.0 - levIn[i])*.5 + 150
  x = height * np.cos(lat*3.14/180)*np.cos(lon*3.14/180*1.002)
  y = height * np.cos(lat*3.14/180)*np.sin(lon*3.14/180*1.002)
  z = height * np.sin(lat*3.14/180)
}}}
The generated coordinates are stored in the new created file "coordinates.h5".
ATTENTION: !ParaView can read this "structured grid", but cannot volume-render it, as volume rendering with [https://www.ospray.org/ OspRay] or GPU-based only works for data of type "image data", wich is a 3D rectiliniear grid. Therefore, the filter "Resample to Image" must be applied to the reader in !ParaView!

Create XDMF file:\\
The hdf5 files are loaded via an xdmf reader. To enable this, an xdmf file must be created first, containing information about alle time steps and all variables. The script "make_xdmf.py" does this. The script essentially scans the directory where the data files are located and gets the names of all files for one month (the definition of the month is fixed in the script).
Variables that can be later loaded into !ParaView are noted in the script in form of a python list:
{{{
#!python
 scalars=["cc", "ciwc", "clwc", "q", "d", "vo", "o3", "w"]
}}}
The name of the xdmf-output file is structuredgrid_201709.xdmf.

== !ParaView ==
=== Loading the necessary modules ===
The needed modules can be found out with "module spider !ParaView/5.5.0".
Load modules e.g. with:
{{{
#!bash
module load Stages/2018a Intel/2018.2.199-GCC-5.5.0 ParaStationMPI/5.2.1-1 ParaView/5.5.0
}}}

=== ParaView GUI ===
First load the modules as described above, then start !ParaView GUI 
{{{
#!bash
vglrun paraview
}}}
The GUI is well suited to prototype the scene. In the GUI one can define the visualization pipeline with its parameters, i.e. the readers and filters, the color tables and the camera positions. However, for various reasons it makes sense to script the visualization with paraview-python:
- In the script all parameters are recorded in text form, so one can look up these parameters later
- Loading !ParaView GUI state files sometimes does not work
- !ParaView has a memory leak, so after a number of render steps you have to quit !ParaView and restart it at the aborted location (otherwise !ParaView would crash). This can be automated using a script.


=== From GUI to Script ===
How To transfer pipeline parameters from GUI to script:\\
In the !ParaView-GUI, start a Python trace by Tools->Start Trace.
Then create the pipeline you want. (Most of) the corresponding Python commands are displayed in the trace. These can be transferred into a script with copy & paste and, if needed, modified there.

How To transfer colormaps from GUI to script:\\
Once you have designed a good colormap in the GUI, you can save it there as a preset. This preset can then be renamed and saved to disk as a *.json file.
Since a different colormap makes sense for each variable, one will end up with more then one colormap file. In this example the naming scheme for colormap files is "stein_''variable''.json", e.g. "stein_vo.json" for the vorticity. This naming scheme is expected in the Python scripts, which among other things load the color tables.

How To transfer camera parameters from GUI to script:\\
You can save four (and more) camera positions in the !ParaView GUI. Click on the camera icon ("Adjust Camera"), then "configure", then "Assign current view". The camera positions can be saved in an XML file via "export" and can later be loaded and used in the Python script e.g. with:
{{{
#!python
import xml.etree.ElementTree as ET
from paraview.simple import *
def assignCameraParameters(root, camera, camIdx):
   camera.SetPosition(float(root[camIdx-1][1][0][0][0][0].attrib['value']), float(root[camIdx-1][1][0][0][0][1].attrib['value']), float(root[camIdx-1][1][0][0][0][2].attrib['value']))
   camera.SetFocalPoint(float(root[camIdx-1][1][0][0][1][0].attrib['value']), float(root[camIdx-1][1][0][0][1][1].attrib['value']), float(root[camIdx-1][1][0][0][1][2].attrib['value']))
   camera.SetViewUp(float(root[camIdx-1][1][0][0][2][0].attrib['value']), float(root[camIdx-1][1][0][0][2][1].attrib['value']), float(root[camIdx-1][1][0][0][2][2].attrib['value']))
   camera.SetParallelScale(float(root[camIdx-1][1][0][0][6][0].attrib['value']))

tree = ET.parse('camera_' + attribute + '.pvcvbc')
root = tree.getroot()
camera = GetActiveCamera()
assignCameraParameters(root, camera, 1)
}}}

As you can see above, the script expects the naming convention "camera_''variable''.pvcvbc", e.g. "camera_vo.pvcvbc" for the vorticity.

=== How-To start a !ParaView script ===
Generally a !ParaView python script is started with "pvpython script.py" (or, on a VNC-server, like JURECA, by "vglrun pvpython script.py"). On JURECA, however, the following approach is necessary: first start a pvserver on display :0.0 and let pvpython connect to this server. This can be done in one command line:
{{{
#!bash
bash -c 'export DISPLAY=:0.0 && pvserver --disable-xdisplay-test' & sleep 2; see pvpython ./script.py
}}}

== Sample session for using the scripts: ==
All files needed to run this sample session can be found in the attachment at the end of this page! Just download from there!
In this sample session, the typical usage of the scripts is demonstrated. All necessary files (scripts, colormaps, camera positions, texture) should be copied into one directory. At the end, 10 images for the two variables "ciwc" and "clwc" should be generated.

=== Prerequisites ===
- Data is located as described above in /data/slmet/slmet111/met_data/ecmwf/era5/netcdf4/2017/
- On JURECA, the modules for Python and H5PY are loaded, e.g. with "module use otherstages && modules --force purge && module load !Stages/Devel-2017b GCC/7.2.0 ParaStationMPI/5.2.0-1 h5py/2.7.1-Python-2.7.14"
- The following scripts are available: generate_coordinates.py, make_xdmf.py, make_movie_pvserver.sh, paraview_make_movie_pvserver.py
- Color tables for two attributes are available: stein_ciwc.json and stein_clwc.json
- Camera positions for two attributes are available: camera_ciwc.pvcvbc and camera_clwc.pvcvbc
- Texture earth_heller_transformed.jpg is available

=== Step 1: Create coordinates ===
{{{
#!bash
python ./generate_coordinates.py
}}}
As a result, the file "./coordinates.h5" should be generated.

=== Step 2: Create XDMF file ===
{{{
#!bash
python ./make_xdmf.py
}}}
As a result, the file "./structuredgrid_201709.xdmf" should be created.

=== Step 3: Render images ===
{{{
#!bash
./make_movie_pvserver.sh
}}}
Rendering takes about 10 minutes. As a result, 10 images for the variables "ciwc" and "clwc" should be generated. The images can be viewed on JURECA with "/usr/local/jsc/etc/xdg/scripts/launch_gpicview.sh image*.jpg".