Satellite‐based Precipitation Estimation using Watershed Segmentation and Growing Hierarchical Self‐organizing Map

This paper outlines the development of a multi‐satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high‐resolution, short‐duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self‐organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co‐registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground‐radar data in lieu of a dense constellation of polar‐orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground‐radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004–February 2005) at various temporal (daily and monthly) and spatial (0.04° and 0.25°) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub‐layers rather than a single layer. Furthermore, 2‐year (2003–2004) satellite rainfall estimates generated by the current algorithm were compared with gauge‐corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite‐based rainfall estimations