Sensitivity of simulations of Zambian heavy rainfall events to the atmospheric boundary layer schemes

Weather forecasting relies on the use of Numerical Weather Prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in Planetary Boundary Layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), the nonlocal first-order medium-range forecasting (MRF), the University ofWashington (UW) and the Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations are made for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. Use of the YSU produced the highest rainfall peaks across all three cases, however it produces performance statistics similar to UW that are higher than the two other schemes. These statistics are not maintained when adjusted for random hits indicating that the extra events are mainly random rather than being skilfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this is not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak, however MYNN is associated with a slower decay, and higher PBL heights at night. WRF underestimates the maximum temperature during all the cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicate a need for more ground observations to assist with studies like this one