The development and evaluation of a seamless rainfall forecasting system for Ghana using Meteosat data and the GFS model.

There is an urgent need for reliable now- and forecasting of (extreme) precipitation on the African continent. Early warning for extreme rainfall contributes to disaster preparedness and can decrease the associated risks. Moreover, reliable, and seamless precipitation data are of high value for (hydrological) flood models. Flash floods are often caused by intense and localized rainfall over a short period of time. Timely anticipation on the highly dynamic causes of flashfloods requires precipitation data with a high temporal resolution as well as short lead times. The lack of ground-based radar stations on the African continent hinders the availability of such precipitation data and leaves many regions prone to high risks associated with extreme precipitation.

Numerical Weather Predication (NWP) models provide valuable information concerning precipitation forecasting. However, due to large computational demands NWP models are commonly not applicable for short lead times. Nowcasting methods which extrapolate observations show skillful lead times of 0-4 hours. Nevertheless, a significant decrease in skill is observed for longer lead times. Efforts by Imhoff et al., (2023), Radhakrishnan & Chandrasekar, (2020) and Nerini et al., (2019) show promising results using a blending approach which incorporates extrapolation based nowcast data derived from ground-radar and NWP-data.

The high spatio-temporal resolution of Meteosat data (15 minutes and 3 km) in combination with its relative short latency offers potential to partly overcome the shortage of ground-based radar data on the African continent. This research evaluates the applicability and accuracy of precipitation nowcasts based on Meteosat data. For these analyses the open-source Python nowcasting environment Pysteps is utilized. As rainfall retrieval algorithm, the Cloud Physical Properties (MSG-CPP) model, as developed by the Royal Dutch Meteorological institute (KNMI), is applied.

Additionally, this research explores the possibility for blended ensemble precipitation now- and forecasting, combining NWP forecasts with satellite-based observation extrapolations. Meteosat data and the open-source Global Forecast System (GFS) are used as input for this blended precipitation model. Ground measurement data collected by the Trans-African Hydro-Meteorological Observatory (TAHMO) organization is utilized to evaluate the performance of the nowcasting products.

For this study, Ghana is selected as case study area. Ghana has a tropical climate which is strongly influenced by West African monsoon winds. On a yearly basis, (flash) floods cause fatalities and large social-economic damages. This stresses the urgent need for disaster risk management actions wherein access to seamless precipitation now- and forecast models is of high value. The data sources used in this research are all openly available for the complete African continent. By solely utilizing open data sources with short latencies, this research aims to contribute to operational and open access of seamless precipitation now- and forecasts. These efforts are in line with the Early Warning for All initiative as called for by the United National Secretary-General in 2022.