Gu et al. (2026) The impact of ERA5 assimilation-window discontinuities on convection-permitting forecasts of extreme precipitation: a case study of the July 2023 North China heavy rainfall

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-08-24
• Authors: Jun Gu, Chun Zhao, Jiawang Feng, Mingyue Xu, Zhaojun Tang, Gudongze Li
• DOI: 10.1088/1748-9326/ae9d97

Research Groups

• Institute of Atmospheric Physics, Chinese Academy of Sciences
• Department of Earth System Science, University of California, Irvine
• National Center for Atmospheric Research (NCAR)

Short Summary

This study investigates the impact of reanalysis window boundaries on forecast skill and identifies structured differences among adjacent initializations. The results highlight the importance of accounting for assimilation-window position in reanalysis-driven forecasts.

Objective

• Investigate the effect of reanalysis window boundaries on forecast behavior and skill

Study Configuration

• Spatial Scale: Regional (North China) to global scales, with a focus on the refinement zone.
• Temporal Scale: Hourly ERA5 analyses, with two 24-member initialization sweeps at approximately 4 and 16 km resolution.

Methodology and Data

• Models used: ERA5 reanalysis data, initialized from consecutive hourly analyses.
• Data sources: Satellite observations, ground-based measurements, and numerical-model outputs.

Main Results

• Forecast behavior changes abruptly across the assimilation-window boundary at convection-permitting resolution.
• Neighborhood-based verification shows that boundary-related skill changes exceed non-boundary adjacent-hour changes.
• Circulation and counterfactual analyses link the 09:00–10:00 UTC transition to changes in the moisture corridor and rainfall placement.

Contributions

• This study provides new insights into the impact of reanalysis window boundaries on forecast skill, highlighting the importance of accounting for assimilation-window position in reanalysis-driven forecasts.
• The results have implications for AI model training, numerical-model initialization, and weather forecasting applications.

Funding

• National Natural Science Foundation of China (Grant No. 42175042)
• National Key Research and Development Program of China (Grant No. 2021YFA0609900)

Citation

@article{Gu2026impact,
  author = {Gu, Jun and Zhao, Chun and Feng, Jiawang and Xu, Mingyue and Tang, Zhaojun and Li, Gudongze},
  title = {The impact of ERA5 assimilation-window discontinuities on convection-permitting forecasts of extreme precipitation: a case study of the July 2023 North China heavy rainfall},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/ae9d97},
  url = {https://doi.org/10.1088/1748-9326/ae9d97}
}