ArticleJournal of advances in modeling earth systems2021
Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting.
Article in Journal of advances in modeling earth systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Climate-invariant machine learning.Science advances · 2024Article
- Deep Learning Based Cloud Cover Parameterization for ICON.Journal of advances in modeling earth systems · 2022Article
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.GeoHealth · 2022Article
- A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning.Entropy (Basel, Switzerland) · 2022Article
- Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting.Journal of advances in modeling earth systems · 2021Article
- Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.