Evaluation of Precipitation in the Chinese Regional Reanalysis Using Satellite Estimates, Gauge-Based Observations and Global Reanalysis

Yutong Lu,Jianping Tang,Juan Fang,Yinong Pan,Min Shao

doi:10.3389/feart.2021.647580

Yutong Lu, Jianping Tang + Show 3 more

Open Access

https://doi.org/10.3389/feart.2021.647580

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Abstract

Two high-resolution Chinese regional reanalysis (CNRR) datasets at a resolution of 18 km during the period of 1998–2009 are generated by Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) method. The precipitation from CNRR is comprehensively evaluated against the observational datasets and global reanalysis ERA5 over East-Asia. The climatology mean, seasonal variability, extreme events, and summer diurnal cycle of precipitation are analyzed. Results show that CNRR reasonably reproduces the observed characteristics of rainfall, although some biases exist. The spatial distribution of climatology mean precipitation is well simulated by CNRR, while overestimation exists especially on the west side of Tibetan-Plateau (TP). CNRR reproduces the unimodal feature of the annual cycle with overestimations of summer precipitation, and well produces the probability of light and moderate rainfall but tend to overestimate heavy and extreme precipitation over most regions in China. The overall spatial distribution of extreme precipitation indices can be captured by CNRR. The diurnal cycle of summer precipitation, as well as the amplitude of diurnal cycle, are better reproduced by CNRR-GSI, capturing eastward propagation of diurnal phase from TP along the Yangtze River. CNRR-GSI generally outperforms CNRR-SN over most regions of China except in reproducing heavy and extreme rainfall in the Yangtze River Basin (YRB) and South China (SC) regions. CNRR-GSI shows comparable results with the latest ERA5 and outperforms it in simulating the diurnal cycle of precipitation. This dataset can be considered as a reliable source for precipitation related applications.

Highlights

Precipitation is a vital component of the hydrological cycle and the predominant and direct sources of water for the land surface water budget (Trenberth, 1999; Maurer et al, 2001; Risi et al, 2010; Zhou et al, 2011; Schneider et al, 2017)
This study aims to (1) comprehensively evaluate the capability of Chinese regional reanalysis (CNRR) in reproducing the mean climate, seasonal cycle, extreme rainfall, daily and diurnal variability of precipitation over China, (2) compare the effects of Gridpoint Statistical Interpolation (GSI) data assimilation technique and spectral nudging (SN) method in generating precipitation in CNRR
To detailed evaluate the precipitation characteristics in CNRR products, the mean climatology, seasonal cycle of precipitation, daily precipitation and diurnal variations of precipitation are assessed against the CMORPH, OBS_Land, and, ERA5

Summary

Introduction

Precipitation is a vital component of the hydrological cycle and the predominant and direct sources of water for the land surface water budget (Trenberth, 1999; Maurer et al, 2001; Risi et al, 2010; Zhou et al, 2011; Schneider et al, 2017). Over TP, WRF-driven regional reanalysis shows advantage in simulating precipitation compared to ERA5 (He et al, 2019). The ability of such global reanalysis datasets in depicting regional-scale, complex terrain climate and diurnal-scale variability requires further evaluation and improvement (von Storch, 1999; Sotillo et al, 2005; Bromwich et al, 2016; Zhang et al, 2017). Long-term reanalysis datasets with higher spatial and temporal resolutions are still in need for regional climate researches and investigating phenomena such as extreme rainfall events and mesoscale weather systems

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