East Asia Reanalysis System (EARS)

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).

A Regionally Refined and Mass‐Consistent Atmospheric and Hydrological De‐Aliasing Product for GRACE, GRACE‐FO and Future Gravity Missions

“…As long as no high‐resolution non‐hydrostatic global NWP reanalysis exists, another option will be to nest multiple regional NWP products simultaneously into one global model. Currently available regional high‐resolution reanalyses focus on the Northern hemisphere and include the North American Regional Reanalysis (NARR; Hunter et al., 2020) and the East Asia Reanalysis System (EARS; Yin et al., 2023). Yet, we first need to understand if high‐resolution non‐hydrostatic models indeed represent more realistic mass variability.…”

Classification of Cloud Phase Using Combined Ground-Based Polarization Lidar and Millimeter Cloud Radar Observations Over the Tibetan Plateau

“…Because of the relative lack of field observational experiments on the microphysical characteristics of clouds and precipitations on the TP, little is known about the cloud phase behavior there [38]; moreover, the specific natural environment on the TP results in unique cloud and precipitation properties, especially when compared to low-altitude regions [39], and can prominently limit the simulation capabilities of numerical weather prediction models. He et al [40] and Zhao et al [41] investigated the microphysical properties of clouds on the TP based on lidar and cloud radar observations, respectively.…”

High resolution Tibetan Plateau regional reanalysis 1961-present

“…four-dimension variational data assimilation (4DVAR), and re-initialization method 35,[37][38][39][40][41][42][43][44] . Based on the above methods, RR has been applied in many regions; for example, North American Regional Reanalysis (NARR) 37 is applied in North America using 3DVAR, East Asia Reanalysis System (EARS) 44 is applied in East Asia using 4DVAR/3DVAR on surface/upper observations, the Arctic System Reanalysis version 2 (ASRv2) 35 is applied in Arctic using 3DVAR, COSMO-REA2 40 is applied in Central Europe using continuous nudging method, and the Indian Monsoon Data Assimilation and Analysis (IMDAA) 41 is applied in the Indian subcontinent using 4DVAR.…”