East Asia Reanalysis System (EARS)
-
Published:2023-06-06
Issue:6
Volume:15
Page:2329-2346
-
ISSN:1866-3516
-
Container-title:Earth System Science Data
-
language:en
-
Short-container-title:Earth Syst. Sci. Data
Author:
Yin JinfangORCID, Liang Xudong, Xie Yanxin, Li Feng, Hu Kaixi, Cao Lijuan, Chen FengORCID, Zou Haibo, Zhu Feng, Sun Xin, Xu Jianjun, Wang Geli, Zhao Ying, Liu JuanjuanORCID
Abstract
Abstract. Reanalysis data play 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 for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA)
scheme during the WRF model integration; upper observations are assimilated
in three-dimensional variational data assimilation (3D-VAR) mode at the 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 h intervals. Finally, an evaluation of EARS has been
performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global
reanalysis data of ERA-Interim, the regional reanalysis data of 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 the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference65 articles.
1. Albers, S. C., McGinley, J. A., Birkenheuer, D. L., and Smart, J. R.: The Local Analysis and Prediction System (LAPS): Analyses of Clouds, Precipitation, and Temperature, Weather Forecast., 11, 273–287, https://doi.org/10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2, 1996. 2. Andrys, J., Lyons, T. J., and Kala, J.: Multidecadal Evaluation of WRF Downscaling Capabilities over Western Australia in Simulating Rainfall and Temperature Extremes, J. Appl. Meteorol. Clim., 54, 370–394, https://doi.org/10.1175/JAMC-D-14-0212.1, 2015. 3. Bach, L., Schraff, C., Keller, J., and Hense, A.: Towards a probabilistic regional reanalysis system for Europe: Evaluation of precipitation from experiments, Tellus A, 68, 32209, https://doi.org/10.3402/tellusa.v68.32209, 2016. 4. Benjamin, S. G., Jamison, B. D., Moninger, W. R., Sahm, S. R., Schwartz, B. E., and Schlatter, T. W.: Relative Short-Range Forecast Impact from Aircraft, Profiler, Radiosonde, VAD, GPS-PW, METAR, and Mesonet Observations via the RUC Hourly Assimilation Cycle, Mon. Weather Rev., 138, 1319–1343, https://doi.org/10.1175/2009MWR3097.1, 2010. 5. Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S., and Steinke, S.: Towards a high-resolution regional reanalysis for the European CORDEX domain, Q. J. Roy. Meteor. Soc., 141, 1–15, https://doi.org/10.1002/qj.2486, 2015.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|