• DOI:10.1175/JAM2173.1
• Corpus ID: 11229720

Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

@article{Hong2004PrecipitationEF,
  title={Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System},
  author={Yang Hong and Kuo-lin Hsu and Soroosh Sorooshian and Xiaogang Gao},
  journal={Journal of Applied Meteorology},
  year={2004},
  volume={43},
  pages={1834-1853}
}

• Y. Hong, K. Hsu, +1 author Xiaogang Gao
• Published 1 December 2004
• Environmental Science
• Journal of Applied Meteorology

Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfal...