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Keyword: subseasonal-to-seasonal forecasting ×Clear all

5 claims from 3 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

   Weyn, Durran, Caruana and Cresswell‐Clay · Journal of Advances in Modeling Earth Systems · 2021

   The authors built a fast deep-learning weather model ensemble and tested it for forecasts up to six weeks, finding it modestly behind the ECMWF sub-seasonal ensemble over land at longer lead times.

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   1. UncheckedIn a two-year global test, the deep-learning ensemble mean beat climatology beyond two weeks, with anomaly correlation above 0.6 through six days.“Averaged globally and over a two-year test set, the ensemble mean RMSE retains skill relative to climatology beyond two-weeks, with anomaly correlation coefficients remaining above 0.6 through six days.”
   2. UncheckedA deep-learning ensemble forecast is only modestly worse than the ECMWF sub-seasonal ensemble over land at lead times of 4 and 5-6 weeks.“The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead times of 4 and 5-6 weeks.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges

   Boukabara, Krasnopolsky, Stewart, Maddy, Shahroudi and Hoffman · Bulletin of the American Meteorological Society · 2019

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   1. Unchecked“Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remot…

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models

   Chen, Zhong, Li et al. · arXiv (Cornell University) · 2023

   The paper introduces FuXi-S2S, a machine learning model giving global daily forecasts up to 42 days ahead, and reports that it outperforms the ECMWF subseasonal model on some variables.

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   1. UncheckedFuXi-S2S, a machine learning model, is reported to beat ECMWF's leading subseasonal model on precipitation and outgoing longwave radiation forecasts.“FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast.”
   2. UncheckedThe paper attributes FuXi-S2S's better forecasts mainly to how it handles forecast uncertainty and the MJO, extending skilful MJO prediction from 30 to 36 days.“The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days.”