2026-10-14T20:00:05Z https://www.scidb.cn/oai

10.57760/sciencedb.27940 2026-02-06T11:00:25Z

2026-02-06 A 1 km Hourly High-Resolution Thermodynamic Dataset over the Yangtze River Delta during June-August 2021-2023 doi:10.57760/sciencedb.27940 en High-resolution atmospheric thermodynamic fields are essential for elucidating the evolution mechanisms of localized weather systems over complex terrain regions such as the Yangtze River Delta. The thermodynamic dataset was generated using the same dynamical downscaling strategy and numerical simulation framework as the corresponding high-resolution three-dimensional wind field dataset. Specifically, the dataset was produced through dynamic downscaling with the Weather Research and Forecasting (WRF) model, driven by ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulations incorporate multi-source observational nudging, optimized physical parameterization schemes, and updated high-resolution land use data to better represent local atmospheric processes.This thermodynamic dataset includes five variables: 2-meter air temperature (T2m), 2-meter relative humidity (RH2m), surface pressure (PSFC), and vertical profiles of air temperature (T) and relative humidity (RH). The three-dimensional thermodynamic profiles are provided on 32 ERA5-standard pressure levels ranging from 1000 hPa to 10 hPa. All variables are stored in NetCDF format with lossless compression enabled. Key variables are encoded using the scale_factor and add_offset attributes following the relation: real_value = scale_factor × packed_value + add_offset.The dataset fully complies with CF conventions and can be automatically decoded by commonly used tools such as xarray, NCL, and CDO, without requiring any manual post-processing. Dynamical downscaling; Multi-source observation nudging; High-resolution land use; Yangtze River Delta; Thermodynamic filed; Temperature profile; Humidity profile Zhengyan Zhang Jun Li Yan-An Liu PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank