Hybrid Rainfall Estimates from Satellite, Lightning and Ground Station Data in West Africa
Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.
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