ychen-nyu/hurricanetrack

Use historical hurricane track data from National Hurricane Center to train a neural network and predict future hurricane track

HurricaneTrack

This would be something tough, challenging, but with significant influence on the fundamental research of big, complex, nonlinear natural system.

Hurricane is one of the extreme phenomenon generated by the complex climate system on Earth. Through decades or even centuries, countless pioneers have been trying to understanding the cause of it through various methods, like observations by on ground, in the air or in the space or using supercomputing to model the evolution of the system through differential equations with tons of parameters. Yet little has been achieved.

So I've been thinking about this problem for a long time. the idea is to use historical hurricane track data from National Hurricane Center to train a neural network and predict possible future hurricane track while it's still on the way. If this works, it would be both fun and economically useful for the whole society.

Steps to work on this: (1) Select and pull in data from National Hurricane Center. Start with simple case and look into the features available in the data. (2) To begin with, clean up the data and map out (date, time, position) evolution of hurricane for a few cases and see what's going on. (3) Explore fundamental features like date of the year, locations, pressure, wind speed, stage etc. Think about the most critical feature that would affect the physical system to evolute continuously or abruptly. (4) Try out with machine learning machine to train and validate historical data on the prediction accuracy. (5) This would be challenging but let's take a look on how it goes.