Preprints
https://doi.org/10.5194/essd-2022-429
https://doi.org/10.5194/essd-2022-429
 
12 Jan 2023
12 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

East Asia Reanalysis System (EARS)

Jinfang Yin1, Xudong Liang1, Yanxin Xie1, Feng Li1, Kaixi Hu2, Lijuan Cao2, Feng Chen3, Haibo Zou4, Feng Zhu5, Xin Sun5, jianjun Xu6, Geli Wang7, Ying Zhao8, and Juanjuan Liu7 Jinfang Yin et al.
  • 1The State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 2National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
  • 3Zhejiang Institute of Meteorological Sciences, Hangzhou 310008, China
  • 4Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
  • 5Inner Mongolia Meteorological Observatory, Inner Mongolia Hohhot 010051, China
  • 6College of Oceanography and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
  • 7Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 8School of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Abstract. Reanalysis data plays a vital role in weather and climate study, as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre of Medium-Range Weather Forecasts (ECMWF) global reanalysis EAR-Interim data at 6-h intervals; and hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in a three-dimensional variational data assimilation (3D-VAR) mode at analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12-km horizontal resolution, 74 sigma levels, and 3-hour intervals. Finally, an evaluation of EARS has been performed with the respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of the EAR-Interim, the regional reanalysis data of the EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of China Meteorological Administration (CMA) at http://data.cma.cn.

Jinfang Yin et al.

Status: open (until 09 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jinfang Yin et al.

Data sets

East Asia Reanalysis System (EARS) Yin, Jinfang; Liang, Xudong; Xie, Yanxin; Li, Feng; Hu, Kaixi; Cao, Lijuan; Chen, Feng; Zou, Haibo; Zhu, Feng; Sun, Xin; Xu, Jianjun; Wang, Geli; Zhao, Ying https://doi.org/10.5281/zenodo.7404918

Jinfang Yin et al.

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Short summary
A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there is no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.