ArticleScience advances2024
Generative emulation of weather forecast ensembles with diffusion models.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bridging the weather and climate divide with artificial intelligence.Nature communications · 2026Review
- Generative AI for efficient statistical computation of fluids.Nature communications · 2026Article
- ArchesWeatherGen: Skillful and compute-efficient probabilistic weather forecasting with machine learning.Science advances · 2026Article
- A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018).Scientific data · 2026Article
- Addressing the data imbalance issue in machine learning modeling of rare and disruptive outage events.Scientific reports · 2026Article
- FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction.Science advances · 2025Article
- A data-to-forecast machine learning system for global weather.Nature communications · 2025Article
- Can AI weather models predict out-of-distribution gray swan tropical cyclones?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dynamical-generative downscaling of climate model ensembles.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evaluation of precipitation forecasting base on GraphCast over mainland China.Scientific reports · 2025Article
- Confronting Earth System Model trends with observations.Science advances · 2025Review
- Generative AI as a tool to accelerate the field of ecology.Nature ecology & evolution · 2025Review
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts by running physics-based simulations under different conditions, which is a computationally costly process. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data. The learned models are highly scalable with respect to high-performance computing accelerators and can sample thousands of realistic weather forecasts at low cost. When designed to emulate operational ensemble forecasts, the generated ones are similar to physics-based ensembles in statistical properties and predictive skill. When designed to correct biases present in the operational forecasting system, the generated ensembles show improved probabilistic forecast metrics. They are more reliable and forecast probabilities of extreme weather events more accurately. While we focus on weather forecasting, this methodology may enable creating large climate projection ensembles for climate risk assessment.
Identifiers
What OpenQuestion holds
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.