Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

  • NasrinĀ Nasrollahi

Part of the Springer Theses book series (Springer Theses)

About this book

Introduction

This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. 

Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved. 

The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation.

Keywords

CloudSat precipitation data CloudSat texts MODIS satellite observations award-winning thesis current satellite precipitation products false rain reduction introduction to satellite precipitation multi-spectral satellite imagery precipitation retrival algorithms satellite precipitation data satellite precipitation measurements satellite-based precipitation estimation

Authors and affiliations

  • NasrinĀ Nasrollahi
    • 1
  1. 1.University of California, IrvineIrvineUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-12081-2
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-319-12080-5
  • Online ISBN 978-3-319-12081-2
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • About this book
Industry Sectors
Aerospace
Energy, Utilities & Environment