Preprints
https://doi.org/10.5194/essd-2022-429
https://doi.org/10.5194/essd-2022-429
12 Jan 2023
 | 12 Jan 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

East Asia Reanalysis System (EARS)

Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu

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: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-429', Dick Dee, 09 Feb 2023
    • AC1: 'Reply on RC1', Jinfang Yin, 26 Feb 2023
  • RC2: 'Comment on essd-2022-429', Anonymous Referee #2, 10 Feb 2023
    • AC2: 'Reply on RC2', Jinfang Yin, 26 Feb 2023
  • AC3: 'Comment on essd-2022-429', Jinfang Yin, 22 Mar 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-429', Dick Dee, 09 Feb 2023
    • AC1: 'Reply on RC1', Jinfang Yin, 26 Feb 2023
  • RC2: 'Comment on essd-2022-429', Anonymous Referee #2, 10 Feb 2023
    • AC2: 'Reply on RC2', Jinfang Yin, 26 Feb 2023
  • AC3: 'Comment on essd-2022-429', Jinfang Yin, 22 Mar 2023

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.