Satellite-Based Precipitation Measurement Using PERSIANN System View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Kuo-Lin Hsu , Soroosh Sorooshian

ABSTRACT

PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) is a satellite-based rainfall estimation algorithm. It uses local cloud textures from longwave infrared images of the geostationary environmental satellites to estimate surface rainfall rates based on an artificial neural network algorithm. Model parameters are frequently updated from rainfall estimates provided by low-orbital passive microwave rainfall estimates. The PERSIANN algorithm has been evolving since 2000, and has generated near real-time rainfall estimates continuously for global water and energy studies. This paper presents the development of the PERSIANN algorithm in the past 10 years. In addition, the validation and merging PERSIANN rainfall with ground-based rainfall measurements for hydrologic applications are also discussed. More... »

PAGES

27-48

References to SciGraph publications

Book

TITLE

Hydrological Modelling and the Water Cycle

ISBN

978-3-540-77842-4
978-3-540-77843-1

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-77843-1_2

DOI

http://dx.doi.org/10.1007/978-3-540-77843-1_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048825619


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