Cardona-Soto, Melvin J.

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    A new cloud classification system for rainfall detection over puerto rico using remotely sensed data
    (2011) Cardona-Soto, Melvin J.; Parsiani, Hamed; College of Engineering; Cruz-Pol, Sandra L.; Harmsen, Eric W.; Department of Electrical and Computer Engineering; Huérfano, Victor
    A new rainfall detection algorithm was developed to overcome challenges algorithms like the operational NOAA/NESDIS Hydro-Estimator (HE) present over Puerto Rico when detecting rainfall. The HE, a brightness temperature and numerical weather prediction based algorithm, detects about half of the rainfall received throughout a year, and when it does, the detection of rainfall is inconsistent. Part of this may due to the fact that the HE uses brightness temperature to discriminate between rain and no rain, and a large amount of the rainfall received in Puerto Rico is produced by warm clouds. In order to achieve greater accuracy of detection over PR, the new rainfall detection algorithm utilizes data from multiple channels of GOES-12 to extract several features from clouds (e.g., Brightness Temperature, Visible Reflectance, and Albedo). These features are utilized to perform a supervised classification of the image pixels into 4 previously defined classes. The classes were defined using NEXRAD rainfall detection information. Radar and satellite information from five heavy storms that occurred from 2003 to 2007 were used to define the parameters of the algorithm. Once the algorithm was developed a discrete validation was performed to both the cloud classification system and the HE using NEXRAD information as ground truth. The performance of both algorithms was measured in terms of rainfall detection and compared. Warm cloud detection capability was also measured and compared between both algorithms.