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Libro
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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
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108,98 €
103,53 €
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TRAMA
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.SOMMARIO
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.ALTRE INFORMAZIONI
- Condizione: Nuovo
- ISBN: 9783319120805
- Collana: Springer Theses
- Dimensioni: 235 x 155 mm Ø 313 gr
- Formato: Copertina rigida
- Illustration Notes: XXI, 68 p. 41 illus., 38 illus. in color.
- Pagine Arabe: 68
- Pagine Romane: xxi