Abstract
While current satellite techniques are theoretically capable of producing precipitation estimates to image pixel resolutions, significant uncertainty is present in such high-resolution products. This uncertainty is frequently difficult to characterize using scalar measures of additive error. This paper describes the development of a methodology to more fully represent the uncertainty in satellite precipitation retrievals. The methodology derives conditional probability distribution functions of rainfall on a pixel-by-pixel basis. This array of distribution functions is then combined with a simple model of the spatiotemporal covariance structure of the uncertainty in the precipitation field to stochastically generate an ensemble precipitation product. Each element of the ensemble represents an equiprobable realization of the precipitation field that is consistent with the original satellite data while containing a random element commensurate with the uncertainty in that field. The technique has been tested using data from the Tropical Rainfall Measuring Mission (TRMM) Texas and Florida Underflight Experiment (TEFLUN-B) field campaign.
Corresponding author address: Timothy J. Bellerby, Department of Geography, The University of Hull, Hull HU6 7RX, United Kingdom. Email: T.J.Bellerby@hull.ac.uk
