Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record‐Breaking Meiyu Season Over the Yangtze–Huaihe River Valley in China. (3rd March 2023)

• Record Type: Journal Article
• Authors: Di, Zhenhua; Zhang, Shenglei; Quan, Jiping; Ma, Qian; Qin, Peihua; Li, Jianduo
• Is Part Of: GeoHealth. Volume 7:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-03
• Subjects: (Rapid Radiative Transfer Model); Environmental health -- Periodicals; Electronic journals; Periodicals; 616.98
• DOI: 10.1029/2022GH000757
• Languages: English
• ISSNs: 2471-1403

Abstract

Abstract: In 2020, the Yangtze–Huai river valley (YHRV) experienced the highest record‐breaking Meiyu season since 1961, which was mainly characterized by the longest duration of precipitation lasting from early‐June to mid‐July, with frequent heavy rainstorms that caused severe flooding and deaths in China. Many studies have investigated the causes of this Meiyu season and its evolution, but the accuracy of precipitation simulations has received little attention. It is important to provide more accurate precipitation forecasts to help prevent and reduce flood disasters, thereby facilitating the maintenance of a healthy and sustainable earth ecosystem. In this study, we determined the optimal scheme among seven land surface model (LSMs) schemes in the Weather Research and Forecasting model for simulating the precipitation in the Meiyu season during 2020 over the YHRV region. We also investigated the mechanisms in the different LSMs that might affect precipitation simulations in terms of water and energy cycling. The results showed that the simulated amounts of precipitation were higher under all LSMs than the observations. The main differences occurred in rainstorm areas (>12 mm/day), and the differences in low rainfall areas were not significant (<8 mm/day). Among all of the LSMs, the Simplified Simple Biosphere (SSiB) model obtained the best performance, with the lowest root mean square error and the highest correlation. The SSiB model even outperformed the Bayesian model averaging result. Finally, some factors responsible for the differences modeling results were investigated to understand the related physical mechanism. Plain Language Summary: The record‐breaking precipitation that occurred in the Meiyu season during 2020 significantly affected humans and ecosystems. Can using different land surface model (LSMs) schemes affect the accuracy of precipitation simulations in numerical weather prediction models? If this is the case, how does their performance affect predictions of critical water and energy processes? In this study, we found that simulations using all of the LSMs overestimated the observations, but Simplified Simple Biosphere (SSiB) performed relatively better, with the lowest root mean square error and highest correlation. SSiB performed better than the multi‐model ensemble averaging model with all seven LSMs. Key Points: Seven land surface schemes in the WRFv4.3 model were evaluated to obtain the optimal scheme for precipitation simulation Simulated rainfall amounts under all land surface models were generally higher than observations, but Simplified Simple Biosphere (SSiB) performed best Water heat flux, geopotential height, and moisture flux convergence all indicated the superiority of SSiB at precipitation simulation