Mastodawn

After diving into this field for the last year, I very much agree with this bit:

"most of the near-term results using ML will be in areas where the ML allows us to tackle big data type problems more efficiently than we could do before. This will lead to more skillful models, and perhaps better predictions, and allow us to increase resolution and detail faster than expected. Real progress will not be as fast as some of the more breathless commentaries have suggested, but progress will be real."

https://fediscience.org/@Ruth_Mottram/113775294023850288
Ruth_Mottram - One of few #ClimateBlogs to still reliably get good comments, likely because of insightful content : @RealClimate has a very good piece by @climateofgavin on #AI in #climatemodels with which I concur completely

https://www.realclimate.org/index.php/archives/2024/12/ai-caramba/

Ruth Mottram (@Ruth_Mottram@fediscience.org)

One of few #ClimateBlogs to still reliably get good comments, likely because of insightful content : @RealClimate@portal.0svc.com has a very good piece by @climateofgavin@beta.birdsite.live on #AI in #climatemodels with which I concur completely https://www.realclimate.org/index.php/archives/2024/12/ai-caramba/

FediScience.org

Ruth Mottram Jan 5

One of few #ClimateBlogs to still reliably get good comments, likely because of insightful content : @RealClimate has a very good piece by @climateofgavin on #AI in #climatemodels with which I concur completely

https://www.realclimate.org/index.php/archives/2024/12/ai-caramba/

RealClimate: ¡AI Caramba!

RealClimate: Rapid progress in the use of machine learning for weather and climate models is evident almost everywhere, but can we distinguish between real advances and vaporware? First off, let's define some terms to maximize clarity. Machine Learning (ML) is a broad term to distinguish any kind of statistical fitting of large data sets to complicated

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