Installation¶

Recommended method¶

We recommend using the Anaconda Python distribution. Alternative installation instructions are given at the bottom.

WPS/WRF-SFIRE¶

Please follow the instructions on OpenWFM to run WPS/WRF with real data. Ensure that you have working WPS/WRF installation is available and compatible with the MPI on your cluster. Note that wrfxpy will not modify the WPS/WRF installation, instead for each job, it will clone their directories into its workspace.

Attention

Past this point, you should be able to run a fire simulation yourself, that is have a working WPS/WRF-SFIRE installation including WPS-GEOG and fuels/topography downloaded. You should be able to submit a parallel job into your cluster/supercomputer to run wrf.exe

Python and packages¶

Download and install the Python 2 Anaconda Python distribution for your platform. We recommend an installation into the users home directory.

::: wget https://repo.continuum.io/archive/Anaconda2-5.3.0-Linux-x86_64.sh chmod +x Anaconda2-5.3.0-Linux-x86_64.sh ./Anaconda2-5.3.0-Linux-x86_64.sh

Install pre-requisites:

conda install basemap netcdf4 pyproj paramiko dill
conda install -c conda-forge simplekml pygrib f90nml
conda install -c anaconda numpy
pip install MesoPy
pip install python-cmr

Add your environment (~/.custom.csh) the following line: setenv PROJ_LIB “$HOME/anaconda2/share/proj”
or if you are using bash to ~/.profile or ~/.bashrc: export PROJ_LIB=”$HOME/anaconda2/share/proj”

Note that conda and pip are package managers available in the Anaconda Python distribution.

wrfxpy¶

Next, clone the wrfxpy code:

git clone https://github.com/openwfm/wrfxpy.git

configuration¶

And finally, a etc/conf.json file must be created with the keys discussed below. A template file etc/conf.json.initial is provided as a starting point.

Configure the system directories, WPS/WRF-SFIRE locations and workspace locations by editing the following keys:

"workspace_path": "wksp"
"wps_install_path": "path/to/WPS"
"wrf_install_path": "path/to/WRF"
"sys_install_path": "/path/to/wrfxpy"
"wps_geog_path" : "/path/to/wps-geogrid"

Optionally, the wrfxpy installation can be connected to a visualization server wrfxweb. The following keys are all optional (and only used if the postprocessed results of simulations are uploaded).

"shuttle_ssh_key": "path/to/your/priv_ssh_key/to/remote/host",
"shuttle_remote_user" : "remote_username",
"shuttle_remote_host" : "remote_hostname",
"shuttle_remote_root" : "remote directory for output storage"

This concludes the etc/conf.json file.

Next, wrfxpy needs to know how jobs are submitted on your cluster. Create an entry for your cluster, here we use speedy as an example:

{
  "speedy" : {
    "qsub_cmd" : "qsub",
    "qsub_script" : "etc/qsub/speedy.sub"
  }
}

And then the file etc/qsub/speedy.sub should contain a submission script template, that makes use of the following variables supplied by wrfxpy based on job configuration:

%(nodes)d the number of nodes requested
%(ppn)d the number of processors per node requested
%(wall_time_hrs)d the number of hours requested
%(exec_path)d the path to the wrf.exe that should be executed
%(cwd)d the job working directory
%(task_id)d a task id that can be used to identify the job
%(np)d the total number of processes requested, equals nodes x ppn

Note that not all keys need to be used, as shown in the speedy example:

#$ -S /bin/bash
#$ -N %(task_id)s
#$ -wd %(cwd)s
#$ -l h_rt=%(wall_time_hrs)d:00:00
#$ -pe mpich %(np)d
mpirun_rsh -np %(np)d -hostfile $TMPDIR/machines %(exec_path)s

The script template should be derived from a working submission script.

Attention

You are now ready for your first fire simulation, continue with Quickstart.

Custom installation¶

If Anaconda python is not practical, a different python distribution can be used. Below is a list of packages the system requires:

Python 2.7+
Basemap to render the rasters
simplekml to build KMZ files
f90nml to manipulate Fortran namelists
pyproj to place domains dynamically in LCC projection
paramiko to communicate over SSH with remote hosts
netCDF4 to manipulate WPS and WRF files
MesoPy to retrieve fuel moisture observations from Mesowest

wrfxpy is installed by cloning a GitHub repository

git clone https://github.com/openwfm/wrfxpy.git

Configure wrfxpy by editing etc/conf.json as above and then continue with Quickstart.