A short-range weather prediction system for South Africa based on a multi-model approach

Abstract

Predicting the location and timing of rainfall events has important social and economic impacts. It is also important to have the ability to predict the amount of rainfall accurately. At many operational centres, such as the South African Weather Service, forecasters use deterministic model output data as guidance to produce subjective probabilistic rainfall forecasts. The aim of this research is to determine the skill of a new objective multi-model, multi-institute probabilistic ensemble forecast system for South Africa. This was achieved by obtaining and combining the rainfall forecasts of two high-resolution regional atmospheric models operational in South Africa. The first model is the Unified Model (UM), which is operational at the South African Weather Service. The UM contributed three ensemble members which differ in physics, data assimilation techniques and horisontal resolution. The second model is the conformal-cubic atmospheric model (CCAM) which is operational at the Council for Scientific and Industrial Research, which in turn contributed two members to the ensemble system differing in horisontal resolution. A single-model ensemble forecast, with each of the ensemble members having equal weights, was constructed for the UM and CCAM models, respectively. The UM and CCAM single-model ensemble predictions have been used in turn to construct a multi-model ensemble prediction system, using simple un-weighted averaging. The probabilistic forecasts produced by single-model system as well as the multi-model system are here tested against observed rainfall data over three austral summer half-years from 2006/07 to 2008/09, by using verification metrics such as the Brier skill score, relative operating characteristics, and the reliability diagram. The forecast system is found to be skillful. Moreover, the system outscores the forecast skill of the individual models.