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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Keyword: uncertainty quantification ×Clear all

6 claims from 3 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Deep learning for post-processing ensemble weather forecasts

   Grönquist, Yao, Ben‐Nun et al. · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2021

   The authors propose using fewer ensemble weather simulations plus deep neural network post-processing, and report better forecast skill, especially for extreme events, and comparable results to the full ensemble.

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   1. UncheckedApplied to global data, the authors' mixed models improve ensemble weather forecast skill, measured by CRPS, by more than 14% in relative terms.“Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.”
   2. UncheckedThe paper reports that its deep-learning post-processing improves forecasts more for extreme weather events, in selected case studies.“Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies.”
   3. UncheckedThe authors say their deep-learning post-processing can reach results comparable to a full forecast ensemble while using fewer simulated trajectories.“We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Generative emulation of weather forecast ensembles with diffusion models

   Li, Carver, Lopez‐Gomez, Sha and Anderson · Science Advances · 2024

   The authors train diffusion models on historical data to emulate physics-based ensemble weather forecasts, and to correct their biases, at much lower computational cost.

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   1. UncheckedThe authors say their learned diffusion models scale well on high-performance computing accelerators and can sample thousands of realistic weather forecasts cheaply.“The learned models are highly scalable with respect to high-performance computing accelerators and can sample thousands of realistic weather forecasts at low cost.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Ensemble Methods for Neural Network‐Based Weather Forecasts

   Scher and Messori · Journal of Advances in Modeling Earth Systems · 2020

   Unchecked2 claims
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   1. Unchecked“The ensemble mean forecasts obtained from these four approaches all beat the unperturbed neural network forecasts, with the retraining method yielding the highest improvement.”
   2. Unchecked“However, the skill of the neural network forecasts is systematically lower than that of state-of-the-art numerical weather prediction models.”