Assessing forecasting models on prediction of the tropical cyclone Dineo and the associated rainfall over Botswana

Abstract

Abstract The tropical cyclone Dineo made landfall over southern Mozambique on 15 February 2017. It weakened to a remnant low on 17 February, which hit Botswana on the same day and triggered heavy rainfall that resulted in flooding over the country. This study assesses the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) and the European Center for Medium-Range Weather Forecast (ECMWF) models in forecasting the locations and intensity of the tropical cyclone and its remnant low, the associated cloud cover and rainfall over Botswana. The assessment includes comparison of the amount of predicted rainfall (areal-averaged rainfall) with rain gauge data, locations of predicted maximum rainfall with observed maximum rainfall and estimation of root mean square errors, forecast track and intensity errors. Data used in the performance assessment of the models are rainfall observations, best track data and Meteosat satellite visible images. The study period was 12–19 February 2017, which covered the lifespan of the weather system. Comparing model errors in forecasting the track and intensity of the tropical cyclone, both models had average forecast intensity errors greater than 17 mb while their average forecast track errors were 1.4 km or less. ECMWF performed better than GFS in three aspects: maximum rainfall values, location and intensity of the storm; and GFS performed better than ECMWF in three aspects: location of maximum rainfall, cloud band associated with the storm and overall rainfall amount (generally had lower root mean square errors). The relative performance of both models suggest that the models should be used to complement each other in forecasting tropical cyclone events in Botswana.