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

Nasrin Nasrollahi

ISBN 10: 3319120808 / ISBN 13: 9783319120805

Published by Springer International Publishing, 2014

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319120805

Bibliographic Details

• Title: Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
• Publisher: Springer International Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3319120808
• ISBN 13: 9783319120805
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 10D millimeters 241 millimeters width by 160 millimeters height by 10 millimeters depth
• Weight: 313 grams

About this title

Synopsis

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.