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Status: Unchecked ×Keyword: 2 m air temperature ×Clear all

5 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation

   Hu, Chen, Wang and Li · Journal of Advances in Modeling Earth Systems · 2023

   The paper presents SwinVRNN, a data-driven weather model that learns its own perturbation distribution for ensemble forecasts, and compares it with other perturbation methods on WeatherBench.

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   1. UncheckedOn WeatherBench, learning the noise distribution in SwinVRNN gave better forecast accuracy and a reasonable ensemble spread than the other perturbation methods compared.“Comparisons on WeatherBench dataset show the learned distribution perturbation method using our SwinVRNN model achieves superior forecast accuracy and reasonable ensemble spread due to joint optimization of the two targets.”
   2. UncheckedOn the WeatherBench dataset, SwinVRNN is reported to beat the operational IFS on 2-m temperature and 6-hourly rainfall at every lead time up to five days.“More notably, SwinVRNN surpasses operational IFS on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   A variable-resolution unstructured spatio-temporal graph neural network: an application to very short-range weather forecasting in Guangdong, China

   Li, Chen, Huang, Xu and Zhang · Geoscience Letters · 2026

   The authors built a graph neural network for six-hour forecasts over Guangdong, China, with finer resolution over the Pearl River Delta, and report good skill for some variables and growing errors for others.

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   1. UncheckedTested on held-out data and observations, the model forecasts 2 m temperature and sea-level pressure well and keeps them consistent across space and time.“Verification against an independent test-set and observations, using multiple metrics, demonstrates high predictive skill and strong spatio-temporal coherence for 2 m temperature and sea-level pressure.”
   2. UncheckedIn this Guangdong forecasting model, errors for 10 m winds and 2 m humidity grow larger as the forecast looks further ahead.“For 10 m winds and 2 m humidity, model errors grow larger with lead time.”
   3. UncheckedIn case studies of a Guangzhou heatwave and Super Typhoon Saola, the model captured how events evolved but underestimated peak temperatures, winds and pressure lows.“Case studies of an extreme heatwave event in Guangzhou and the post-landfall evolution of Super Typhoon Saola effectively capture the evolution characteristics and spatial patterns, while underestimates the peak temperatures, maximum winds and minimum sea-level pressures.”