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Spacebased estimation of moisture transport in marine atmosphere using support vector regression

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dc.contributor.author Xie, Xiaosu
dc.contributor.author Liu, W. Timothy
dc.contributor.author Tang, Benyang
dc.date.accessioned 2008-08-18T22:44:51Z
dc.date.available 2008-08-18T22:44:51Z
dc.date.issued 2007
dc.identifier.citation Remote Sensing of Environment 112 (2008) 1846–1855, http://dx.doi.org/10.1016/j.rse.2007.09.003 en_US
dc.identifier.clearanceno 07-3052
dc.identifier.uri http://hdl.handle.net/2014/40922
dc.description.abstract An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere (Θ) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data (Θ calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of Θ derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation comparedwith rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Elsevier Inc en_US
dc.subject Moisture transport en_US
dc.subject Water cycle en_US
dc.subject Support vector regression en_US
dc.subject Remote sensing en_US
dc.title Spacebased estimation of moisture transport in marine atmosphere using support vector regression en_US
dc.type Article en_US


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