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Añadir al carritoHardcover. Condición: Brand New. 2015 edition. 68 pages. 9.00x6.00x0.25 inches. In Stock.
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Añadir al carritoTaschenbuch. Condición: Neu. Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery | Nasrin Nasrollahi | Taschenbuch | xxi | Englisch | 2016 | Springer | EAN 9783319363325 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Springer International Publishing, 2014
ISBN 10: 3319120808 ISBN 13: 9783319120805
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Añadir al carritoBuch. Condición: 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.
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Publicado por Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 3319363328 ISBN 13: 9783319363325
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Añadir al carritoTaschenbuch. Condición: 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.
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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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Publicado por Springer 978-3-319-12080-5, Cham
ISBN 10: 3319120808 ISBN 13: 9783319120805
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Añadir al carrito[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).
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Idioma: Inglés
Publicado por Springer International Publishing Nov 2014, 2014
ISBN 10: 3319120808 ISBN 13: 9783319120805
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoBuch. Condición: 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.
Idioma: Inglés
Publicado por Berlin Springer International Publishing Springer Sep 2016, 2016
ISBN 10: 3319363328 ISBN 13: 9783319363325
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: 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. 68 pp. Englisch.
Idioma: Inglés
Publicado por Springer International Publishing, 2014
ISBN 10: 3319120808 ISBN 13: 9783319120805
Librería: moluna, Greven, Alemania
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Añadir al carritoGebunden. Condición: 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.
Idioma: Inglés
Publicado por Springer International Publishing, 2016
ISBN 10: 3319363328 ISBN 13: 9783319363325
Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: 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.
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Idioma: Inglés
Publicado por Springer, Palgrave Macmillan Nov 2014, 2014
ISBN 10: 3319120808 ISBN 13: 9783319120805
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 92 pp. Englisch.