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2 - Global precipitation estimation from satellite imagery using artificial neural networks

Published online by Cambridge University Press:  15 December 2009

By
S. Sorooshian ,
K.-L. Hsu ,
B. Imam  and
Y. Hong
Edited by
Howard Wheater ,
Soroosh Sorooshian  and
K. D. Sharma
S. Sorooshian
Affiliation:
Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
K.-L. Hsu
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
B. Imam
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
Y. Hong
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
Howard Wheater
Affiliation:
Imperial College of Science, Technology and Medicine, London
Soroosh Sorooshian
Affiliation:
University of California, Irvine
K. D. Sharma
Affiliation:
National Institute of Hydrology, India
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Summary

INTRODUCTION

Precipitation is the key hydrologic variable linking the atmosphere with land-surface processes, and playing a dominant role in both weather and climate. The Global Water and Energy Cycle Experiment (GEWEX), recognizing the strategic role of precipitation data in improving climate research, strongly emphasized the need to achieve global measurement of precipitation with sufficient accuracy to enable the investigation of regional to global water and energy distribution. Additionally, many other international research programs have also placed high priority on the development of reliable global precipitation observation.

During the past few decades, satellite-sensor technology has facilitated the development of innovative approaches to global precipitation observations. Clearly, satellite-based technologies have the potential to provide improved precipitation estimates for large portions of the world where gauge observations are limited. Recently many satellite-based precipitation algorithms have been developed (Ba and Gruber, 2001; Huffman et al., 2002; Joyce et al., 2004; Negri et al., 2002; Sorooshian et al., 2000; Tapiador 2002; Turk et al., 2002; Vicente et al., 1998; Weng et al., 2003). These algorithms generate precipitation products consisting of higher spatial and temporal resolution with potential to be used in hydrologic research and water-resources applications. Evaluation of recently developed precipitation products over various regions is ongoing (Ebert, 2004; Kidd, 2004; Janowiak, 2004).

In this chapter, we will introduce one near-global precipitation product generated from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) algorithm.

Type
Chapter
Information
Hydrological Modelling in Arid and Semi-Arid Areas , pp. 21 - 28
Publisher: Cambridge University Press
Print publication year: 2007

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References

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  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
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  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
Available formats
×