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A comment on Machine Learning’s ‘Amazing’ Ability to Predict Chaos. Parent comment

Re:Machine Learning’s ‘Amazing’ Ability to Predict Chaos by papaf on Thursday April 19, 2018 @01:35AM (#16873565)
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

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Re:Machine Learning’s ‘Amazing’ Ability to Predict Chaos by aeorgnoieang on Thursday April 19, 2018 @11:07AM (#16876530)
> machine learning and deterministic models

This is a confusing distinction. Aren't lots of machine learning algorithms deterministic? Sure, some include stochastic elements or components, but even those can be deterministic, e.g. by reusing the same seed for the random number generators.

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