It sounds like the next step, but even if weather were reducible to machine-discoverable patterns, you first must face the need to collect an immense amount of

It sounds like the next step, but even if weather were reducible to machine-discoverable patterns, you first must face the need to collect an immense amount of high-resolution condition data from around the globe on an ongoing basis. There has been some success using Ensemble Kalman Filters (EnKF) to predict hurricanes [1]. I think that these filters sit somewhere between machine learning and deterministic models. The filters are based on a semi-realistic mathematical model but are updated with statistics every few hours so that they can improve their predictions. As you said, going full on machine learning "sounds like the next step". However, I would disagree that the ideas at the end of the article are unrealistic. If the new technique can beat the Kalman filters it is already useful. [1] http://hfip.psu.edu/realtime/AL2016/forecast_track.html http://hfip.psu.edu/realtime/AL2016/forecast_track.html