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Hurricane Prediction with Python

Minwoo Lee
Colorado State University

Charles W. Anderson
Colorado State University

Mark DeMaria


The National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) is a global spectral model used for aviation weather forecast. It produces forecasts of wind speed and direction, temperature, humidity and precipitation out to 192 hr every 6 hours over the entire globe. The horizontal resolution in operational version of the GFS is about 25 km. Much longer integration of similar global models are run for climate applications but with much lower horizontal resolution. Although not specifically designed for tropical cyclones, the model solutions contain smoothed representations of these storms. One of the challenges in using global atmospheric model for hurricane applications is objectively determining what is a tropical cyclone, given the three dimensional solutions of atmospheric variables. This is especially difficult in the lower resolution climate models. To address this issue, without manually selecting features of interests, the initial conditions from a low resolution version of the GFS (2 degree latitude-longitude grid) are examined at 6 hour periods and compared with the known positions of tropical cyclones. Several Python modules are used to build a prototype model quickly, and the prototype model shows fast and accurate prediction with the low resolution GFS data.


hurricane, prediction, GFS, SVM

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