A maximum entropy approach to satellite quantitative precipitation estimation (QPE)

Abstract

This paper presents a new algorithm to generate quantitative precipitation estimates from infrared (IR) satellite imagery using passive microwave (PMW) data from Special Sensor Microwave/Imager sensor (SSM/I) satellites as ancillary information. To generate the estimates, we model the probabilistic distribution function (PDF) of the rainfall rates through the maximum entropy method (MEM), applying a cumulative histogram matching (HM) technique to the IR brightness temperatures. This results in a straightforward algorithm that can be formulated as an algebraic expression, providing a simple method to derive rainfall estimates using only IR data. The main application of the method is the direct estimation of rainfall rates and accumulated rainfall from geostationary satellites, providing appropriate temporal and spatial resolutions (up to 15 min/4 km when the Meteosat Second Generation satellite becomes available). The proposed method can be easily applied at GOES or current Meteosat satellite reception stations to generate instantaneous rainfall rates estimates with little computational cost. Here we provide examples of applications using the Global Infrared Database and Meteosat images. Our results have been compared with GOES Precipitation Index (GPI) and validated against Global Precipitation Climatology Centre (GPCC)-land rain gauge measurements, at 5°, monthly accumulations. We have obtained correlations of 0.88 for the algorithm, while the GPI yields correlations of 0.85. Preliminary comparisons with other algorithms over Australia also show how the performances of the algorithm are similar to those of more complex models. Finally, we propose some improvements and fine-tuning procedures that can be applied to the algorithm.