Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China

Abstract

The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes.

Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China

“…In addition, the average deviation and root mean square error values are relatively small. By comparing the correlation coefficients presented in this paper with those published in the literature for temperature [39], wind speed [40], and rainfall [41] in similar terrain, it can be observed in Table 6 that the calculated correlation coefficients for temperature and wind speed in this study are slightly higher than those reported in previous studies. The correlation coefficients for rainfall closely align with the published values.…”

Numerical Simulation of a Typical Convective Precipitation and Its Cloud Microphysical Process in the Yushu Area, Based on the WRF Model

“…This study reveals the microphysical process of convective cloud and the transformation law of hydrometeors over the Plateau, which is of great significance for further understanding of the microphysical mechanism of precipitation formation and water-cycle characteristics over the Plateau, improving the parameterization of modeled cloud physical processes, and improving the level of numerical prediction. However, due to data discretization, initial and boundary conditions, and parameterization uncertainty [44], the deterministic prediction of the WRF model has a large error. Among them, the uncertainty of the parameterization scheme has a great impact on precipitation forecast technology because the microphysics scheme, convection scheme, radiation scheme, and PBL scheme in the parameterization schemes have a great impact on the accuracy of simulation of temperature, humidity, and wind field [45].…”

Roles of the Tibetan Plateau and Yunnan‐Guizhou Plateau in the Regional Extreme Precipitation Over Sichuan Basin in Summer: A Case Study

“…Sichuan Basin (SCB) is located in southwestern China, and is surrounded by the Tibetan Plateau (TP), the Yunnan‐Guizhou Plateau (YGP), Southeast Hills, and Daba Mountains (Bin & Xiang, 2016; Du et al., 2022; J. Li et al., 2021). Affected by terrain, the precipitation (including extreme precipitation with different duration) over the SCB exhibits a significant diurnal feature (M. G. Wu & Luo, 2019; Zheng et al., 2019), which usually begins at night, peaks between midnight and early morning, and gradually ceases after sunrise (S. Chen et al., 2019; J. Liu et al., 2021; Y. Wu et al., 2018; Zhao et al., 2020; L. Zhu et al., 2018).…”