Oceanic Rain Identification using Multi-Fractal Analysis of QuikSCAT Sigma-0

Abstract

The presence of rain over oceans interferes with the measurement of sea surface wind speed and direction from the Sea Winds scatterometer, and as a result, in rain regions wind measurements contain biases. In past research at the Central Florida Remote Sensing Lab, it has been observed that rain has multifractal behavior. In this paper we present an algorithm to detect the presence of rain so that rain regions are flagged. The forward and aft views of the high resolution horizontal polarization backscatter are used for the extraction of textural information with the help of multifractals. A negated multifractal exponent is computed to discriminate between wind and rain. Pixels with exponent value above a threshold are classified as rain pixels and those that do not meet the threshold are further examined with the help of correlation of the multifractal exponent within a predefined neighborhood of individual pixels. It was observed that the rain has less correlation within a neighborhood compared to wind. This property is utilized for reactivation of the pixels that fall below a certain threshold of correlation. An adaptive multifractal exponent and threshold is used, as we deal with a wide range of latitudes. Validation results are presented through comparison with the Tropical Rainfall Measurement Mission Microwave Imager (TMI) 2A12 rain retrieval product for one day. The results show that the algorithm is effective in identifying rain pixels. Some algorithm deficiencies in high wind speed regions are also discussed. Comparisons with other proposed approaches are also presented.