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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Vendeur : Burton Lysecki Books, ABAC/ILAB, Winnipeg, MB, Canada
[978-3-319-12080-5] 2015. (Hardcover) Fine, no dust jacket. 68pp. Black & white and color illustrations, tables, formulas, references. Publisher series: Springer Theses Recognizing Outstanding PH.D Research. (Science, Earth Sciences, Science). N° de réf. du vendeur 159889
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : 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. N° de réf. du vendeur 9783319120805
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Gebunden. Etat : 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. N° de réf. du vendeur 4499189
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Hardcover. Etat : Brand New. 2015 edition. 68 pages. 9.00x6.00x0.25 inches. In Stock. N° de réf. du vendeur x-3319120808
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