This is hopefully the only script you will have to tweak to obtain the animations you are looking for.
It consist on a simple call to Animate, with all the sensible variables for which diagnostics are required.
These should simply be put in wvarlist, and the directory path to your wrfout files should be set in dir_path.
This should in time replace the "Run" script, as it parses the arguments from the command line, and is able to do everything from a single line of code. The main arguments to use include:
task, which can be diagnostic, wrfcompare, mp4diff or mp4stitch. var, which can be any of the predefined SensibleVariables. dirs, which is the directory (or directories) of the source files. files, which is the list of names of files, when being compared. labels, which is the list of labels added when files are being compared or stitched. outdir, which is the directory where outputs will be saved.
The following are sample calls for this function:
python csf.py --task=diagnostic --var=Rain --dir_path=./MyData/ --outdir=./
python csf.py --task=wrfcompare --var=SeaLevelPressure --dirs="Data1,Data2" --outdir=./ --difflabel=Data2-Data1
python csf.py --task=mp4diff --var=DewpointTemp850 --dirs="./MP4_1,./MP4_2/" --labels="MP4_1,MP4_2,MP4Diff"
python csf.py --task=mp4stitch --dirs="MyVideos" --files="f1,f2,f3,f4" --N=2 --M=2
| svariable | min |
|---|---|
| DewpointTemp2m | 19 |
| AirTemp2m | 20 |
| DewpointTemp850 | 26 |
| AirTemp850 | 28 |
| StaticStability700500 | 28 |
| StaticStability850700 | 28 |
| GeoPotHeight500 | 30 |
| SeaLevelPressure | 30 |
| Rain | 44 |
| RelHum2m | 44 |
| RelHum700 | 44 |
| CIN | 45 |
| SimRadarRefl1km | 76 |
| SimRadarReflMax | 98 |
| CAPE | 99 |
This is the core of the diagnostic generation. In this function all your wrfout files are loaded, the variables are extracted using GetSensVar, plotted using Plot2DField, and combined into an mp4.
All the files in dir_path will be loaded and combined, so make sure you want that.
The information on the diagnostic being generated should be provided in an svariable object, as defined in SensibleVariables.
Should you wish to override the default windbarb overlapping defined for some SensibleVariables, the call to this function is the best place to do so.
By default, when the animation is processed and the mp4 is successfully generated all png files are deleted. This can be controled with the flag cleanpng.
This function simply plots a given variable (var) with the metadata found in the svariable object.
A flag for windbarbs may be turned on, for which the wind components u and v must be given as inputs too.
This defines a class for variables with more sensible names than the ones used in wrf, which makes it a bit more amicable.
The object is basically composed of metadata for the diagnostics that are of interest to this particular project, but the list can easily be expanded.
The information in each object is used as instructions in the extraction of the variable from netcdf files and during plotting.
See the description of each predefined svariable inside the file.
This function is an adaptation of wrf-python's getvar, but makes it simpler to obtain the diagnostics of interest, and uses the information in svariable objects
It deals with all the tecnical details on how to load the variables from the netcdf file so that they can be passed to the Plot2DField function. This includes loading the wind velocity components when windbarbs is set to 1.
During this process, it also takes care of some variable computation, which is not universally implemented in getvar.
The outputs are the processed variable var, the wind velocity components u and v (if windbarbs=0 these will be None), and the raw variable values varv (for use in isdif svariable computation).
This function is a very quick way to compare mp4 files.
It gets the absolute value pixel to pixel difference of each frame, and concatenates it to the two original videos side by side.
This function simply stitches videos on a grid with N rows and M columns.
This is a slightly more advanced way of comparing two sets of data.
It gets the difference directly from the wrfout files, and then animates the result using a divergent colourscale. If the flag smooth is set to 1, it smooths the data before making the diff, so that slight positional changes are not as strongly reflected in the output.
The workflow to generate new results involves using the csf.
Transfering files to and from the csf is simpler if using sshfs, which mounts a folder from the csf to your computer. So, create an empty folder to host the contents from the CSF:
mkdir sshfs_to_csf
Then, use sshfs as sshfs user@dest:/path/to/folder local/path,
sshfs mbcxpfh2@csf3.itservices.manchester.ac.uk:/mnt/seaes01-data01/dmg/dmg/mbcxpfh2/SpanishPlume/Analysis sshfs_to_csf/
You should see everything in that folder, and so copy to and from that folder.
The CSF/Submit.sh does most of the heavy lifting.
All the commands below asume you are in the base folder (.../mbcxpfh2/SpanishPlume/Analysis).
To use it, you need a file with the inputs, e.g. TestCSF/test.inputs, that looks something like this:
--task=diagnostic --var=SeaLevelPressure --dir_path=/mnt/seaes01-data01/dmg/dmg/mbessdl2/Spanish_Plume/WRF/run-zrek/ --outdir=Test_SLP/
--task=diagnostic --var=DewpointTemp2m --dir_path=/mnt/seaes01-data01/dmg/dmg/mbessdl2/Spanish_Plume/WRF/run-zrek/ --outdir=Test_DewpTemp/
Note that csf.py is ommited, this is just a list of the arguments passed to the csf.py script.
You can find some sample files in the CSF directory.
Now you can call the submition script with:
./CSF/Submit.sh testCSF/test.inputs testCSF/Results
The script will create a jobarray and submit each line in the .inputs file.
It will also make sure that the outdir directories exist (or it will create them) in testCSF/Results to save the results.
In case there is no access to the csf, but you do have access to some wrfout files, you can run the code directly skipping the submition script.
The script csf.py is programmed to accept arguments, so you can call it with
python csf.py --task=diagnostic --var=SeaLevelPressure --dir_path=/my/wrfout/files/dir --outdir=Test_Control/
Alternatively, you can tweak and use the tests/test.py script.
The script has the advantage of being able to call csf.py with many arguments.
It assumes you have wrfout files in tests/wrfdata/, and places the results in tests/results/.
Modify the inputs for your test in the all_args list, and run with
python tests/test.py
Make sure you have the conda environment set up and active before you call csf.py or test.py.
If you do not have the environment, make sure you have anaconda/miniconda/micromamba installed. This will install it with defaults:
echo | "${SHELL}" <(curl -L micro.mamba.pm/install.sh)
source ~/.bashrc
Then you can create and activate the environment with
micromamba env create --name wrf-py-env --file environment.yml
micromamba activate wrf-py-env
You are now set up to use the code.
Visualization and comparison of WRF data on the Spanish Plume, modifying the geographical terrain and or heat/moisture flux over the spanish peninsula.
Spanish Plume -- Weather patter usually favourable to light thunderstorm and heavy rains
Historically, Spain's high regions were thought to warm the air up, and add moisture
David: That doesnt seem to be true, It most likely originated in North Africa and was not affected as much over Spain.
Preliminar results show that it is related to the plateou, but because it descends (therefore increases P and T), not because it was warmed up over spain.
A way to proove it would be to check whether wiping out the spanish plateu would change anything.
- Get rid of heights.
- Get rid of the heat/moisture flux and check whether spain did add heat or not.
Interest in how to display info to be able to diagnose.
- 1,2 -> Air temperature and dewpoint temperature at the surface and 850 hPa
- 3,4 -> Static stability (difference in air temperature) T(700 hPa) – T(500 hPa) and T(850 hPa) – T(700 hPa)
- 5 -> Sea-level pressure and Precipitation at surface and wind barbs
- 5/6? 500-hPa geopotential height (wind barbs)
- 7 -> CAPE (convective available potential energy), CIN (convective inhibition)
Is static stability really just Temp1-Temp2? Implementations elsewhere suggest otherwise... Rain was computed as the sum of RAINC and RAINNC.. is that correct? Fussy literature...