Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate

This paper presents the results of an investigation into the utility of rem ote sensing (RS) using meteorological satellites sensors and spatial interp olation (SI) of data from meteorological stations, for the prediction of sp atial variation in monthly climate across continental Africa in 1990. Infor mation from the Advanced Very High Resolution Radiometer (AVHRR) of the Nat ional Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteor ological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) onboard the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS pro xy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorol ogical station data were then used to test the accuracy of each methodology , so that the appropriateness of the two techniques for epidemiological res earch could be compared. SI was a more accurate predictor of temperature, w hereas RS provided a better surrogate for rainfall; both were equally accur ate at predicting atmospheric moisture. The implications of these results f or mapping short and long-term climate change and hence their potential for the study anti control of disease vectors are considered. Taking into acco unt logistic and analytical problems, there were no clear conclusions regar ding the optimality of either technique, but there was considerable potenti al for synergy.