Optimal areal rainfall estimation using raingauges and satellite data

Abstract The main aim of this paper is to present a new method of areal rainfall estimation using satellite and ground-based data. This method involves optimal merging of the estimates provided by satellite information and estimates obtained from raingauges. In the merging procedure, each estimate is weighted according to its uncertainty given by its estimation variance. The uncertainty attributed to the raingauge estimates is obtained using block kriging, while for the satellite uncertainties, a novel regression approach is developed. A standard error is also attached to the new merged estimates. In order to test the algorithm, a case study has been undertaken using the EPSAT dense raingauge network in Niger. The complete EPSAT raingauge network (94 gauges distributed over a 1×1° square) has been used to obtain a detailed picture of the rainfall pattern which is then used as a reference for comparing the estimation schemes. The schemes compared are: (1) estimates based on satellite data only; (2) kriged estimates from a randomly selected subset of four gauges; (3) kriging with external drift using both satellite data and the subset of gauges; and (4) the new merging algorithm. The merging process gives more reliable results both for the mean areal rainfall and its spatial distribution.