Validation of remote-sensing precipitation products for Angola

In situ ground observation measurement of precipitation is difficult in sparsely populated areas with trying access conditions, as is the case in many countries in Africa. The use of remote sensors installed in satellites can be very useful in overcoming this challenge, enabling the improvement of the spatial variability description of this variable and the extension of data series. A number of standard products offering precipitation estimates on a regular basis is now available and may be used for water planning and management purposes. The present study examines the performance of four of these products in Angola, namely the Tropical Rainfall Measuring Mission (TRMM) 3B43 (version 6), Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set (version 2.2), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) Morphing Technique (CMORPH), by comparing annual and monthly precipitation estimates with ground observation measurements. The data set of precipitation ground observation measurements was collected by the authors from different sources in Angola and Portugal, and is the result of an intense effort to gather hydrological records from Angola. It is believed to be one of the most complete data sets of monthly precipitation data from Angola. The four remote-sensing products are able to describe the main features of the spatial and temporal variability of annual and monthly precipitation in Angola. The results also show that the estimates from the TRMM are more accurate than the estimates offered by the other products, a conclusion which is in line with previous studies and which may be explained by the fact that this is the first product to incorporate measurements from precipitation radar. The estimation bias of TRMM is also more consistent which means that the results presented in the present study can be used in an operational environment to reduce the precipitation estimation error.