Systematic diurnal bias of the CMA-MESO model in southern China: Characteristics and correction - Chen, Yuxiao; Wang, Liwen; Leung, Jeremy Cheuk-Hin; Xu, Daosheng; Chen, Jing; Zhang, Banglin (Frontiers in Earth Science, 2023)
Model error is an important source of numerical weather prediction (NWP) errors. Among model errors, the systematic diurnal bias plays an important role in high-resolution NWP models. The main purpose of this study is to explore the characteristics of the systematic diurnal bias of a high-resolution NWP model in southern China and reduce the diurnal bias to improve the forecast results, hence providing a better background field for data assimilation. Based on the China Meteorological Administration Meso-scale (CMA-MESO) high-resolution NWP model, a 15-day sequential NWP experiment was performed in southern China, and the forecast results were analyzed. A sequential bias correction scheme (SBCS) based on analysis increments was designed to reduce the systematic diurnal bias of the CMA-MESO, and 15-day sequential comparative experiments were carried out. The analysis results showed that the CMA-MESO has apparent systematic diurnal biases, and the characteristics differ among variables. A large diurnal bias was mainly found in the lower model layers, and it was concentrated in areas with a complex underlying surface for the horizontal distribution, such as the Qinghai-Tibet Plateau and South China Coast. The results based on the 15-day sequential experiment showed that the SBCS partly reduced the systematic diurnal biases of the CMA-MESO. The mean biases of meridional wind, zonal wind, potential temperature, and water vapor mixing ratio were reduced by 45%, 35%, 20% and 10%, respectively, and the root mean square errors (RMSEs) were reduced by approximately 5%. This study revealed the characteristics of the systematic diurnal bias of CMA-MESO in southern China, which may be caused by the diurnal variation in the thermal and dynamic exchange on underlying surfaces. The effectiveness of the SBCS was also verified, and the results had good prospects for providing more reference information for high-resolution numerical prediction models and data assimilation.