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

2015
ISBN: 978-3-319-12080-5
Verlag: Springer International Publishing

Buch, Englisch, 68 Seiten, HC gerader Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 313 g

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Nasrollahi, Nasrin

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Satellitenkommunikation, Satellitentechnik

Inhaltsverzeichnis

Introduction to the Current States of Satellite Precipitation Products.- False Alarm in Satellite Precipitation Data.- Satellite Observations.- Reducing False Rain in Satellite Precipitation Products Using CloudSat Cloud Classification Maps and MODIS Multi-Spectral Images.- Integration of CloudSat Precipitation Profile in Reduction of False Rain.- Cloud Classification and its Application in Reducing False Rain.- Summary and Conclusions.