East Asia Reanalysis System (EARS)

Abstract

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.

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory

“…In Equation (13), the first term represents the strong turbulent layer conditions that often occur in the tropopause, the second term represents the turbulent layer conditions in the boundary layer, and the third term represents the turbulent layer conditions in the free atmosphere [ 41 ]. According to our inquiry of relevant data and the data of the National Meteorological Center, the wind speed during the experiment was about 2 m/s [ 42 ]. As for the turbulence in the boundary layer and the free atmosphere, according to the long-term observation results in the laboratory, it is found that the turbulence intensity in the boundary layer in Yinchuan is about 3.02 × 10 −18 m −2/3 , and that in the free atmosphere is about 5.01 × 10 −17 m −2/3 .…”

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.…”

East Asia Reanalysis System (EARS)

“…For this purpose, EARS was launched in late 2014, and a 39year (1980-2018) reanalysis dataset is now available to the public. This is our first open documentation of the project, on the basis of several progress reports (e.g., Liang et al, 2020;Yin et al, 2019), which briefly describes EARS and documents its performance. It includes the numerical model set, data assimilation method, assimilated observational datasets, and preliminary results.…”