The Impact of Cumulus Parameterization on Rainfall Simulations over East Africa

Otieno G, Mutemi J, Opijah F, Ogallo L, Omondi H. "The Impact of Cumulus Parameterization on Rainfall Simulations over East Africa." Atmospheric and Climate Sciences. 2018;8(3):355.


The study explored the ability of four cumulus parameterization schemes (CPSs) from Weather and Research Forecasting model (WRF) to simulate mean rainfall patterns, number of rainy days (NRD) and vertically integrated moisture flux (VIMF) during the composite of wet years for the core rainfall seasons of March-April-May (MAM; 1989, 1998 and 2012) and Octo-ber-November-December (OND; 1997, 2006 and 2015) seasons. The CPSs used were Kain-Fritsch (KF), Kain-Fritsch with a moisture-advection based trigger function (KFT), Grell Dévényi (GRELL) and Betts Miller Janjic (BML). The simulations by the GRELL and KF schemes were clearly separated by the dry and wet rainfall gradient in the simulations. For example, the GRELL scheme rainfall simulations were drier over the eastern parts of the region bet-ter. The KF and KFT schemes generated wetter rainfall conditions mainly con-fined to the western parts of the region. The BML scheme simulations were not consistent with the observations. The western and eastern parts of the region were characterized by more and fewer NRD, in both the KF and GRELL schemes. The root mean square error (RMSE) and spatial correlation by KF scheme was 2 mm/day and 0.6. The GRELL scheme however simulated low correlation of 0.45 and RMSE of about 3.0 mm/day over most of the sub-domains. The moisture convergence biases were found to be larger conti-nentally and parts of the nearby Indian Ocean. The persisting rainfall biases constituting of too wet and dry conditions were associated with the KF and GRELL cumulus schemes. The findings from the current study are very funda-mental for the improvement of numerical weather prediction (NWP) tools and cumulus modification processes over the region. The accurate and higher skill rainfall forecasts would provide early warning information for disaster risk re-duction and the related risks on the livelihoods.

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