PyStormTracker: A Parallel Object-Oriented Cyclone Tracker in Python

Extra-tropical cyclones moving along storm tracks are responsible for much of the day-to-day weather variability and extreme weather events at mid-latitudes. Hence, it is of great interest to study and understand cyclone track dynamics and variability. With the availability of large ensemble datasets, such as the CMIP5 archive, it is critical that any cyclone tracking program used to study these dynamics is capable of processing such large datasets.

We have begun development on a parallel Python package, named PyStormTracker, which locates cyclone centers from model outputs and compiles a list of cyclone tracks. In the PyStormTracker's initial implementation, we define cyclones as local minima of the mean sea level pressure field. The cyclones are detected using SciPy's multi-dimensional image processing library. Cyclone center detection is accelerated using the MPI4Py message passing library, parallelizing over time steps in the input dataset. The tracks are generated by linking cyclone centers together, in time, using the nearest neighborhood method. We have evaluated the performance of the package on NCAR's Yellowstone cluster. The source code is available at GitHub, and is designed to be extensible such that new detection and linking algorithms can be independently implemented.