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 Nove...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.展开更多
Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published ...Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published their work on ISO, yet they all agree that there is no clear understanding of this matter. By using daily observed surface temperature (T2m), this study reveals the presence of significant biweekly ISO over Tanzania, a period shorter than the anticipated Madden-Julian Oscillation (MJO) period of 30 to 60 days. It also reveals significant changes in wind direction when comparing the cold phase to the warm phase, highlighting a distinct atmospheric circulation pattern associated with each phase. Furthermore, the analysis reveals the presence of MJO-like eastward movement of pressure systems in the Subtropical High region, which is associated with this variability. This study presents a new analysis by providing a detailed analysis of the intraseasonal variability (ISV) of temperature over Tanzania, focusing on understanding the 2020 spatial-temporal patterns within the October-November-December (OND) season that may play a role in weather forecasting, agricultural planning, climate adaptation, reducing heat-related illnesses and contributing to the international effort to refine climate models and predictability.展开更多
文摘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.
文摘Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published their work on ISO, yet they all agree that there is no clear understanding of this matter. By using daily observed surface temperature (T2m), this study reveals the presence of significant biweekly ISO over Tanzania, a period shorter than the anticipated Madden-Julian Oscillation (MJO) period of 30 to 60 days. It also reveals significant changes in wind direction when comparing the cold phase to the warm phase, highlighting a distinct atmospheric circulation pattern associated with each phase. Furthermore, the analysis reveals the presence of MJO-like eastward movement of pressure systems in the Subtropical High region, which is associated with this variability. This study presents a new analysis by providing a detailed analysis of the intraseasonal variability (ISV) of temperature over Tanzania, focusing on understanding the 2020 spatial-temporal patterns within the October-November-December (OND) season that may play a role in weather forecasting, agricultural planning, climate adaptation, reducing heat-related illnesses and contributing to the international effort to refine climate models and predictability.
