Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

Abstract

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.

Modernizing the open-source community Noah-MP land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

“…Noah-MP can be applied to various spatial scales spanning from point scale locally to ~100-km resolution globally, and temporal scales spanning from sub-daily to decadal time scales. Since its original development, Noah-MP has been used in many important applications, including numerical weather prediction (Suzuki and Zupanski, 2018;Ju et al, 2022), high-resolution climate modeling (Gao et al, 2017;Liu et al, 2017;Rasmussen et al, 2023), land data assimilation (Xu et al, 2021;Nie et al, 2022), drought (Arsenault et al, 2020;Niu et al, 2020;Wu et al, 2021;Abolafia-Rosenzweig et al, 2023a), wildfire (Kumar et al, 2021;Abolafia-Rosenzweig et al, 2022a, 2023b, snowpack evolution (Wrzesien et al, 2015;He et al, 2019;Jiang et al, 2020), hydrology and water resources (Cai et al, 2014;Liang et al, 2019;X. Zhang et al, 2022a;Hazra et al, 2023), crop and agricultural management (Liu et al, 2016;Ingwersen et al, 2018;Warrach-Sagi et al, 2022;Valayamkunnath et al, 2022;Zhang et al, 2020Zhang et al, , 2023, urbanization and heat island (Xu et al, 2018;Salamanca et al, 2018;Patel et al, 2022), biogeochemical cycle (Cai et al, 2016;Brunsell et al, 2021), wind erosion (Jiang et al, 2021), wetland (Z.…”