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Using GTG

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Kim Whitehall edited this page Jan 5, 2016 · 21 revisions

Using GTG

Download the source code tarball

Once you unzip the tarball, you will find the following two folders.

code/

  • mccSearch.py contains all the functions needed
  • mccSearchUI.py contains a command line wizard for running the program
  • mainProg.py contains a sample of the general workflow of the order the modules should be called.
  • mainProgTemplate.py the general workflow of the order the modules should be called in order to complete an evaluation.

GrADsScripts/

  • accuTRMM.gs
  • c3.gs
  • cloudElementVars_template
  • cloudElementVars_template2
  • cs1.gs
  • cs3.gs

How to run the GTG

Run mccSearchUI.py

python mccSearchUI.py

mccSearchUI.py is the use of GTG for the weather application of finding mesoscale convective complexes (MCCs) in When you run the command line, mccSearchUI.py, there are a number of inputs that will be required. There are the main inputs you will have to supply (as well as their variable names in the code - especially useful for editing mainProg.py or tracing the code:

  • mainDirStr : this is the working directory where you wish all the output files –images, textfiles, clipped netCDF files- to be store
  • There is an option to preprocess data. The program
  • CEoriDirName : this is the directory where the original MERG data in netCDF format is stored
  • TRMMdirName : this is the directory where the original TRMM data in netCDF format is stored
  • Start date and time in the format yyyymmddhr
  • End date and time in the format yyyymmddhr

The following assumptions are made:

  • input data are in one folder. For MERG data this is CEoriDirName and for the TRMM data this is TRMMdirName in mainProg.py. These directories cannot be the same.
  • THERE IS NO FILE CHECKING. So please ensure ALL your files are there in netCDF format.

GTG workflow in weather application mccSearchUI.py

The general workflow of the program. The dashed lines indicate optional paths.

More specifically, the findCloudElements (find_cloud_elements) is completed in a parallel fashion leveraging Python's multiprocessing libraries. This design was contributed by Gabriel Mel de Fontenay, who was a final year CS student at USC during his capstone project. The design is illustrated below.(https://github.com/kwhitehall/grab-tag-graph/blob/master/images/parallelized.png)

Successful run

Once everything went well, the directory you indicated where outputs should be stored when prompted in the CL will be generated, and four folders should appear in it.

  • image/ stores all images generated from plots.
  • textFiles/ stores all the text files generated during the run, e.g. cloudElementsUserFile.txt that contains information about each cloud element identified
  • MERGnetcdfCEs/ contains the infrared data in masked netCDF files that have been generated for each cloud element identified
  • TRMMnetcdfCEs/ contains the precipitation data in clipped netCDF files that have been generated for each cloud element identified.
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