• DOI:10.1175/JTECH-D-11-00146.1
• Corpus ID: 1200034

On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

@article{Mahrooghy2012OnAE,
  title={On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation},
  author={Majid Mahrooghy and Valentine G. Anantharaj and Nicolas H. Younan and James Aanstoos and Kuo-lin Hsu},
  journal={Journal of Atmospheric and Oceanic Technology},
  year={2012},
  volume={29},
  pages={922-932}
}

• Majid Mahrooghy, V. Anantharaj, +2 authors K. Hsu
• Published 1 July 2012
• Environmental Science
• Journal of Atmospheric and Oceanic Technology

AbstractBy employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the dail...