Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm

Kuo Lin Hsu, Ali Behrangi, Bisher Imam, Soroosh Sorooshian

Research output: Chapter in Book/Report/Conference proceedingChapter

16 Scopus citations

Abstract

The need for frequent observations of precipitation is critical to many hydrological applications. The recently developed high resolution satellite-based precipitation algorithms that generate precipitation estimates at sub-daily scale provide a great potential for such purpose. This chapter describes the concept of developing high resolution Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Evaluation of PERSIANN-CCS precipitation is demonstrated through the extreme precipitation events from two hurricanes: Ernesto in 2006 and Katrina in 2005. Finally, the global near real-time precipitation data service through the UNESCO G-WADI data server is introduced. The query functions for viewing and accessing the data are included in the chapter.

Original languageEnglish (US)
Title of host publicationSatellite Rainfall Applications for Surface Hydrology
PublisherSpringer Netherlands
Pages49-67
Number of pages19
ISBN (Print)9789048129140
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Extreme precipitation
  • Hurricane Katrina
  • Image segmentation
  • Probability matching method
  • Self-organizing feature map

ASJC Scopus subject areas

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

Fingerprint

Dive into the research topics of 'Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm'. Together they form a unique fingerprint.

Cite this