Probabilistic Estimation of Precipitation Combining Geostationary and TRMM Satellite Data
De Marchi, Carlo
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Environmental satellites represent an economic and easily accessible monitoring means for a plethora of environmental variables, the most important of which is arguably precipitation. While precipitation can also be measured by conventional rain gages and radar, in most world regions, satellites provide the only reliable and sustainable monitoring system. This thesis presents a methodology for estimating precipitation using information from the satellite-borne precipitation radar of the Tropical Rainfall Measurement Mission (TRMM). The methodology combines the precise, but infrequent, TRMM data with the infrared (IR) and visible (VIS) images continuously produced by geostationary satellites to provide precipitation estimates at a variety of temporal and spatial scales. The method is based on detecting IR patterns associated with convective storms and characterizing their evolution phases. Precipitation rates are then estimated for each phase based on IR, VIS, and terrain information. This approach improves the integration of TRMM precipitation rates and IR/VIS data by differentiating major storms from smaller events and noise, and by separating the distinct precipitation regimes associated with each storm phase. Further, the methodology explicitly quantifies the uncertainty of the precipitation estimates by computing their full probability distributions instead of just single optimal values. Temporal and spatial autocorrelation of precipitation are fully accounted for by using spatially optimal estimator methods (kriging), allowing to correctly assess precipitation uncertainty over different spatial and temporal scales. This approach is tested in the Lake Victoria basin over the period 1996-1998 against precipitation data from more than one hundred rain gages representing a variety of precipitation regimes. The precipitation estimates were shown to exhibit much lower bias and better correlation with ground data than commonly used methods. Furthermore, the approach reliably reproduced the variability of precipitation over a range of temporal and spatial scales.