Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm

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

doi:10.1007/978-90-481-2915-7_4

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 proceeding › Chapter

21 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 language	English (US)
Title of host publication	Satellite Rainfall Applications for Surface Hydrology
Publisher	Springer Netherlands
Pages	49-67
Number of pages	19
ISBN (Print)	9789048129140
DOIs	https://doi.org/10.1007/978-90-481-2915-7_4
State	Published - 2010
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

General Environmental Science
General Earth and Planetary Sciences

Access to Document

10.1007/978-90-481-2915-7_4

Cite this

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title = "Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm",

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.",

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T1 - Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm

AU - Hsu, Kuo Lin

AU - Behrangi, Ali

AU - Imam, Bisher

AU - Sorooshian, Soroosh

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Extreme precipitation

KW - Hurricane Katrina

KW - Image segmentation

KW - Probability matching method

KW - Self-organizing feature map

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DO - 10.1007/978-90-481-2915-7_4

M3 - Chapter

AN - SCOPUS:84891382236

SN - 9789048129140

SP - 49

EP - 67

BT - Satellite Rainfall Applications for Surface Hydrology

PB - Springer Netherlands

ER -

Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm

Abstract

Keywords

ASJC Scopus subject areas

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