@article{XU2026, 
author = {Zhifang XU and Jilin WANG and Lipeng JIANG and Qifeng LU and Jiandong GONG and Ruiwen WANG and Minyan WANG and Lijuan ZHU and Ting ZHU and Jian SUN and Ruichun WANG and Xiangwen LIU and Lin ZHANG and Yuan JIANG and Dan WANG and Yongzhu LIU and Xiaomin WAN and Yujie JING and Yuanzhe WANG and Hua TONG and Yue LI and Min HAO},
title = {The CMA Regional Re-Analysis (CMA-RRA): A 3-km Resolution, 1-h Cycling Analysis for China},
year = {2026},
journal = {Journal of Meteorological Research},
volume = {40},
number = {5},
pages = {1373-1392},
keywords = {CMA-RRA, data assimilation, observation, 3DVAR, CMA-MESO, ERA5},
url = {https://www.sciopen.com/article/10.1007/s13351-026-5285-4},
doi = {10.1007/s13351-026-5285-4},
abstract = {Atmospheric reanalysis data play a critical role in a wide range of meteorological applications, such as weather and climate studies, assessment of meteorological resources, and artificial intelligence-driven forecasting. The China Meteorological Administration (CMA) Regional Re-Analysis version 1.0 (CMA-RRA v1.0) system, developed using the CMA mesoscale (CMA-MESO) model and its three-dimensional variational (3DVAR) data assimilation system, aims to generate long-term, high-resolution 3D atmospheric fields. This paper describes the processing framework of the CMA-RRA v1.0 system and reports on the development of a regional reanalysis dataset (2010–2024) produced by the system. The dataset covers the entire China region with a 3-km horizontal resolution, 70 sigma levels, and a 1-h temporal resolution. High-density local observations are assimilated during the reanalysis process, including automatic surface weather observations, reflectivity and radial velocity data from the CMA weather radar network, and data from Fengyun series satellites. Compared with observations and the global ECMWF reanalysis version 5 (ERA5), CMA-RRA v1.0 demonstrates superior performance. It shows closer agreement with radiosonde observations and yields higher equitable threat scores for precipitation at all intensity levels. Furthermore, the probability density distribution of CMA-RRA v1.0 precipitation aligns more closely with observations. Owing to its high resolution and assimilation of denser local data, CMA-RRA v1.0 also accurately reproduces historical high-impact weather events, such as the 3–4 May 2017 squall line, Typhoon Lekima (2019), and the July 2023 extreme rainfall in North China.}
}