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YRD1km: A 1-km Hourly High-Resolution Regional Reanalysis Dataset over the Yangtze River Delta with Emphasis on Thermodynamic Structure

Aug 2026 · Advances in Atmospheric Sciences · 0 citations · 32 references

Abstract

High-resolution atmospheric thermodynamic information is essential for resolving localized weather processes in complex urbanized regions such as the Yangtze River Delta (YRD). However, existing global reanalysis products are limited by coarse spatial resolution, constraining their ability to represent fine-scale thermodynamic structures. In this study, we develop YRD1km, a 1-km hourly regional thermodynamic reanalysis dataset for the YRD, generated through dynamical downscaling of ERA5 using the WRF model with a three-level nested configuration. The framework integrates optimized physical parameterizations, a combined observation and analysis nudging strategy, and updated high-resolution land-use information. The current dataset covers the summer seasons (June–August) from 2021 to 2023, a period characterized by frequent convection and strong land–atmosphere interactions. Comprehensive validation against dense surface and radiosonde observations demonstrates that YRD1km consistently outperforms ERA5 in near-surface temperature, relative humidity, and surface pressure, with reductions of approximately 35% in MAE and RMSE for temperature and humidity and more than 78% for surface pressure. Vertical evaluations reveal substantial improvements throughout the troposphere, particularly for moisture fields. YRD1km also exhibits enhanced temporal stability and reduced discontinuities associated with assimilation window transitions in ERA5. A severe convective case further illustrates its ability to resolve multi-level thermodynamic anomalies preceding convective initiation. Overall, YRD1km provides a physically consistent, high-resolution thermodynamic reanalysis framework that supports detailed weather diagnostics, urban meteorology studies, and future regional AI-based weather modeling applications.

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