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Deng, C., Chi, Y., Huang, Y., Jiang, C., Su, L., Lin, H., et al. (2023) Sensitivity of WRF Multiple Parameterization Schemes to Extreme Precipitation Event over the Poyang Lake Basin of China. Frontiers in Environmental Science, 10, Article 1102864.
https://doi.org/10.3389/fenvs.2022.1102864

has been cited by the following article:

• TITLE: Evaluating the Performance of Land Surface Models and Microphysics Schemes on Simulation of an Extreme Rainfall Event in Tanzania Using the Weather Research and Forecasting Model

  AUTHORS: Daniel Gibson Mwageni, Shuzhou Wang, Godfrey Thomas Assenga

  KEYWORDS: WRF Model, Parameterization Scheme, Two-Way Nesting, Pattern Correlation

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.15 No.1, December 30, 2024

  ABSTRACT: Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.