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}
}
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… 

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