Cantitate/Preț
Produs

Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery: Springer Theses

Autor Nasrin Nasrollahi
en Limba Engleză Paperback – 10 sep 2016
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 61237 lei  6-8 săpt.
  Springer International Publishing – 10 sep 2016 61237 lei  6-8 săpt.
Hardback (1) 61822 lei  6-8 săpt.
  Springer International Publishing – 27 noi 2014 61822 lei  6-8 săpt.

Din seria Springer Theses

Preț: 61237 lei

Preț vechi: 72044 lei
-15% Nou

Puncte Express: 919

Preț estimativ în valută:
11721 12216$ 9757£

Carte tipărită la comandă

Livrare economică 04-18 ianuarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319363325
ISBN-10: 3319363328
Pagini: 89
Ilustrații: XXI, 68 p. 41 illus., 38 illus. in color.
Dimensiuni: 155 x 235 x 5 mm
Greutate: 0.14 kg
Ediția:Softcover reprint of the original 1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Seria Springer Theses

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Nominated by the University of California, Irvine, USA, as an outstanding Ph.D. thesis Presents data sets that reduce false rain signals in satellite precipitation measurements Provides advances in the accuracy of satellite-based precipitation estimation Includes supplementary material: sn.pub/extras