Impact of Land Surface processes on convection over Africa in ensemble model simulations: 2 Case studies using the UKMO model

Abstract:

Climate related risk is an obstacle in improving food security and rural livelihood in Africa. An effective system to provide reasonable forecasts of high impact weather can have a great positive impact on the disaster preparedness and response in African continent. Thus, predicting an event with accuracy is essential to provide early warning of heavy rainfall and floods that may lead to loss of life and property. Past studies have shown the importance of the land surface on the development of African convective storms. Here we are using ensemble model simulations to evaluate the representation of land and its influence on convection in 72 hour forecast models.

Two episodes of heavy rainfall events are identified over western Africa over late spring time of 2019 for this study. A heavy rainfall event is recorded over SW Mali on 25^th April 2019 and another intense convective event over northern Benin on 29^th April 2019. The latter develops into a mesoscale system on 30^th April extending up to western Nigeria and this continues until 3^rd May. We are looking at the UK Met Office Global and Regional Ensemble Model simulations that were run for a forecasting testbed within the African SWIFT (Science for Weather Information and Techniques) project. 18 ensemble members of the UK Met Office Unified Model (UKMO-UM) are used to understand the role of land surface temperature (LST) and soil moisture (SM) in the formation of mesoscale systems. Single-model ensemble simulations of the UM for a global domain at a resolution of 0.2813 X 0.1875 degrees and a regional convection permitting (CP) model at a horizontal resolutions of 8.8km are analysed. Results are compared with Land Surface Temperature from Meteosat Second Generation (MSG) satellite data and precipitation data from Global Precipitation Measurements (GPM). Both the global and regional model capture the main features though the convective initiation takes place much earlier in the models than in observations. We notice that the representation of rivers and wetlands in the global model affects the spatial patterns of surface fluxes, in turn introducing biases into the forecast. Further comparison of surface fluxes in the ensemble simulations of these case studies with observed LST and SM illustrate the importance of land initialisation for short term forecasts.