Seasonal forecasting: Difference between revisions
extended the application of ML to seasonal forecasting by introducing the challenge of polar vortex prediction. It may fall more into the extreme weather event prediction.
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(extended the application of ML to seasonal forecasting by introducing the challenge of polar vortex prediction. It may fall more into the extreme weather event prediction.) |
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{{Disclaimer}}
Seasonal forecasting has traditionally been modeled using complex dynamical models, rather than statistical methods, often called [https://en.wikipedia.org/wiki/General_circulation_model general circulation models] (GCMs). However, seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) and polar vortices, are difficult to predict using traditional methods. ML and deep learning can
==Background Readings==
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