Augmenting satellite precipitation estimation with lightning information

We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental...

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Bibliographic Details
Published in:	International journal of remote sensing Vol. 34; no. 16; pp. 5796 - 5811
Main Authors:	Mahrooghy, Majid, Anantharaj, Valentine G, Younan, Nicolas H, Petersen, Walter A, Hsu, Kuo-Lin, Behrangi, Ali, Aanstoos, James
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 2013
Subjects:	Animal, plant and microbial ecology Applied geophysics artificial neural network Biological and medical sciences Clouds correlation Earth sciences Earth, ocean, space Exact sciences and technology freezing Fundamental and applied biological sciences. Psychology General aspects. Techniques ice image analysis Internal geophysics Lightning machine learning neural networks Precipitation probability Rain Rainfall remote sensing satellite rainfall estimation Seasons spring summer Teledetection and vegetation maps temperature Thresholds Winter maps atmospheric precipitation Estimation Space remote sensing Infrared observation neural networks satellite measurements clouds classification windows Information artificial intelligence System Rainfall rate Relation Self organization brightness temperature Lightning imagery Threshold
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