Answers.Body: <p>People have used machine learning models on aspects of weather forecasting, as here: <a href="http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting" rel="nofollow noreferrer">Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting</a> or here <a href="https://ieeexplore.ieee.org/abstract/document/6102379/" rel="nofollow noreferrer">Predicting Solar Generation from Weather Forecasts using Machine Learning</a>.  I've been loosely associated with an effort to use ML techniques to predict utility demand from weather data.  But note that these are looking to predict the <em>implications</em> of the weather forecast, not the forecast itself.  Lots of effort goes into refining the physics-based models that underlie our everyday weather forecasts, and AFAIK machine learning hasn't resulted in any "secret sauce" that gives better results than these models provide.</p>
  
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Answers.UserDisplayName: antlersoft
Answers.Body: <p>A recent development is Google's GenCast.</p>
  <p>See <a href="https://arxiv.org/abs/2312.15796" rel="nofollow noreferrer">GenCast: Diffusion-based ensemble forecasting for medium-range weather</a></p>
  <p>It is a machine learning based weather prediction model based on empirical fits. It currently outperforms a physics based extrapolation model.</p>
  <p>It is not unexpected that such a model can work better. You can compare it to a scale up of a farmer's gut feeling, intuition and common sense. No underlying mechanistic computations (which can be wrong) but instead experience based predictions.</p>
  
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Answers.UserDisplayName: Sextus Empiricus
Body: <p>This seems like a natural fit, though I've not heard of any, yet.</p>
  
  <p>I would love to know if any MET office, government, military or academic institution has taken all (or sizeable portion of) recorded global weather data for, say, the last 50 years (or since we, as a race, have been using weather satellites) and used it in an AI system to predict future weather.</p>
  
Tags: <prediction>
Title: Are any organisations using AI to predict weather?
UserDisplayName: Matt W
ThreadText: # Are any organisations using AI to predict weather?
  
  (Asked by: username_0 on 2018-07-24)
  
  This seems like a natural fit, though I've not heard of any, yet.
  
  I would love to know if any MET office, government, military or academic institution has taken all (or sizeable portion of) recorded global weather data for, say, the last 50 years (or since we, as a race, have been using weather satellites) and used it in an AI system to predict future weather.
  
  **Tags:** prediction
  
  
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  ## 2 answers
  
  ### Answer by username_1 on 2018-07-24. Score: 2 | Accepted answer
  
  People have used machine learning models on aspects of weather forecasting, as here: [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting) or here [Predicting Solar Generation from Weather Forecasts using Machine Learning](https://ieeexplore.ieee.org/abstract/document/6102379/). I've been loosely associated with an effort to use ML techniques to predict utility demand from weather data. But note that these are looking to predict the *implications* of the weather forecast, not the forecast itself. Lots of effort goes into refining the physics-based models that underlie our everyday weather forecasts, and AFAIK machine learning hasn't resulted in any "secret sauce" that gives better results than these models provide.
  
  
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  ### Answer by username_2 on 2024-12-15. Score: 0
  
  A recent development is Google's GenCast.
  
  See [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://arxiv.org/abs/2312.15796)
  
  It is a machine learning based weather prediction model based on empirical fits. It currently outperforms a physics based extrapolation model.
  
  It is not unexpected that such a model can work better. You can compare it to a scale up of a farmer's gut feeling, intuition and common sense. No underlying mechanistic computations (which can be wrong) but instead experience based predictions.
  
  
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