Augmenting satellite precipitation estimation with
lightning information殖民扩张的影响
划龙舟
惊蛰的特点期刊名称： International Journal of Remote Sensing
作者： Majid Mahrooghy,Valentine G. Anantharaj,Nicolas H. Younan,Walter A.
Petern,Kuo-Lin Hsu,Ali Behrangi,James Aanstoos
作者机构： Geosystems Rearch Institute,National Center for Computational Sciences,Department of Electrical and Computer Engineering,NASA Marshall Space Flight Center,Center for Hydrometeorology and Remote Sensing
(CHRS),Jet Propulsion Laboratory
女孩青春期叛逆的表现年份： 2013年
PAGEBOY桃英语期号： 第15-16期
关键词： RAINFALL ESTIMATION;CONVECTIVE PRECIPITATION;PASSIVE
皂荚子
MICROWAVE;NETWORK;TRMM;SYSTEM
摘要：We have ud lightning information to augment the precipitation estimation from remotely nd imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are ud to gregate cloud patches, gmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A t of features is extracted parately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a lf-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T–R)
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