Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery, Softcover reprint of the original 1st ed. 2015 Springer Theses Series
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.
Date de parution : 09-2016
Ouvrage de 68 p.
15.5x23.5 cm
Date de parution : 11-2014
Ouvrage de 68 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 105,49 €
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Mots-clés :
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; meteorology