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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery (Springer Theses) - Hardcover
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.
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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.
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery (Publisher series: Springer Theses Recognizing Outstanding PH.D Research.)
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery (Springer Theses)
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 92 pp. Englisch. Seller Inventory # 9783319120805
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nominated by the University of California, Irvine, USA, as an outstanding Ph.D. thesisPresents data sets that reduce false rain signals in satellite precipitation measurementsProvides advances in the accuracy of satellite-based precipitatio. Seller Inventory # 4499189
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
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Buch. Condition: Neu. 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
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