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Machine learning models improve wind profile reconstruction accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed machine learning models to reconstruct atmospheric boundary layer wind profiles, outperforming traditional analytical methods. The study, which analyzed over 158,000 IGRA soundings, found that models like CatBoost, TabM, and FT-Transformer achieved significantly lower RMSE values compared to power-law and Monin-Obukhov fits. A Fourier Neural Operator was also employed as a post-hoc corrector, further improving near-surface accuracy. AI

IMPACT Advances in ML for atmospheric science could improve weather forecasting and renewable energy siting.

RANK_REASON The cluster contains a research paper detailing a comparative study of machine learning models for a specific scientific application.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning models improve wind profile reconstruction accuracy

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Fedor Buzaev (HSE University), Dmitry Efremenko (HSE University), Daria Pugacheva (HSE University, Generalizable AI Systems), Fedor Ratnikov (HSE University), Egor Bugaev (HSE University), Vladimir Gordin (HSE University, MIRAI), Denis Derkach (HSE Unive… ·

    Reconstructing Boundary Layer Wind Profiles with Machine Learning: A Comparative Study on IGRA Radiosonde Data

    arXiv:2611.13359v1 Announce Type: new Abstract: Wind profiles in the atmospheric boundary layer (ABL) drive short-range forecasting and wind-energy siting, yet sparse radiosonde coverage forces practitioners to interpolate them from a handful of physical predictors. We benchmark …

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Denis Derkach ·

    Reconstructing Boundary Layer Wind Profiles with Machine Learning: A Comparative Study on IGRA Radiosonde Data

    Wind profiles in the atmospheric boundary layer (ABL) drive short-range forecasting and wind-energy siting, yet sparse radiosonde coverage forces practitioners to interpolate them from a handful of physical predictors. We benchmark modern tabular learners against analytical param…

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