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Status: Unchecked ×Keyword: downscaling ×Clear all

5 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Machine Learning Methods for Weather Forecasting: A Survey

   Zhang, Liu, Zhang and Li · Atmosphere · 2025

   A survey of machine learning weather forecasting methods that reviews active research areas, notes unresolved issues such as interpretability and rare events, and offers a roadmap for future work.

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   1. UncheckedThe survey says research on global forecasting, downscaling, extreme weather and combining machine learning with physics is very active in ML weather forecasting.“Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field.”
   2. UncheckedThe survey says machine-learning weather forecasting still faces open problems, notably model interpretability and predicting rare weather events.“However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events.”

2. Environmental Science › Urban Heat Island Mitigation

   Machine learning bias correction and downscaling of urban heatwave temperature predictions from kilometre to hectometre scale

   Blunn, Ames, Croad et al. · Meteorological Applications · 2024

   The authors used machine learning to bias correct and downscale Met Office UKV temperature forecasts to 100 m resolution over London, using citizen weather station data from eight heatwaves.

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   1. UncheckedMachine learning models cut the average error in London heatwave air temperature predictions by up to 0.12°C (11%) compared with the Met Office UKV model.“The ML models improve the T mean absolute error (MAE) by up to 0.12°C (11%) relative to the UKV.”
   2. UncheckedMachine learning models cut the error in London's urban heat island temperature profile from 0.64°C in the Met Office UKV model to 0.15°C.“They also improve the UHI diurnal and spatial representation, reducing the UHI profile MAE from 0.64°C (UKV) to 0.15°C.”
   3. UncheckedIn this study, the forecast model's latent heat flux was found to be the most important predictor of its air temperature bias over London.“UKV latent heat flux is found to be the most important predictor of T bias.”