This code is used to calculate the potential intensity of tropical cyclones (TCs), using the code published by Kerry Emanuel. It is essentially a wrapper around the FORTRAN subroutine provided in the links below, for use with ERA5 Replicated Data: Single and pressure-levels data available at the National Computational Facility (NCI) (project rt52).
The code is intended to be run on the NCI's gadi machine, with access to rt52. There is a significant amount of bespoke code in this, designed around the folder structure and actual data holdings of the ERA5 data at NCI.
For example, pressure level data is held only for a limited subdomain over Australia and southeast Asia, while surface variables have global coverage. This requires some trick manipulation of the coordinate indices to ensure they align correctly.
Note also the pressure level data is ordered from lowest pressure to highest pressure - highest to lowest altitude. The pcmin.f subroutine requires the input to be in the opposite order. Before using the code here, please check the ordering of the pressure level data.
- python-netCDF4
- numpy
- scipy
- pandas
- cftime
- mpi4py
- gitpython
- seaborn
- cartopy
- shapely
- matplotlib
We use the standard Python setup tools to build the extension, making use of the numpy.f2py module to automatically wrap the FORTRAN code with a Python interface. You can build the Pyhton wrapper using a standard python setup call:
python setup.py install
Set up a suitable configuration file (see calculate.ini). In many cases, I suspect users will need to do more than simply update the configuration file to get this running. The configuration file is currently only used to specify top-level input paths, output location and the logging settings. Other Sections/Options are currently not used.
Run
python calculate.py -h
for a basic description of command line arguments.
To run in a parallel environment:
mpirun -np <ncpus> python calculate.py -c calculate.ini -y <year>calc_pi.sh is a shell script that loops through the available years and calculates daily PI values. It's a self-submitting script that runs the above command line, so each year is completed as a separate job. This reduces the walltime of submitted jobs to within queue limits.
qsub -v NJOB=1,NJOBS=41,YEAR=1979 calc_pi.shThere are also a couple of shell scripts, built around either cdo or nco, to calculate monthly means and daily long term means. Again, these are intended for use on the NCI's gadi platform (and using the PBS queuing system), so your mileage may vary.
As a general rule, the nco tools are faster to calculate means, etc., but cdo provides a more intuitive command line experience, especially when calculating statistics other than simple means.
calculate_daily_ltm.sh calculates a daily long term mean of potential intensity.
calculate_means.sh calculates monthly mean values for all available date ranges, monthly long term means, standard deviation and (10th and 90th) percentiles, trends and time series of trend for a number of sub-regions.
- Reference: https://emanuel.mit.edu/limits-hurricane-intensity
- Code: ftp://texmex.mit.edu/pub/emanuel/TCMAX/
- ERA5 data: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5
- Reanalysis project (rt52): https://my.nci.org.au/mancini/project/rt52
- CDO (Climate Data Operators): https://www.mpimet.mpg.de/cdo/
- NCO (NetCDF Operators): http://nco.sourceforge.net/
