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Proceedings Paper

Creation of the BMA ensemble for SST using a parallel processing technique
Author(s): Kwangjin Kim; Yang Won Lee
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Paper Abstract

Despite the same purpose, each satellite product has different value because of its inescapable uncertainty. Also the satellite products have been calculated for a long time, and the kinds of the products are various and enormous. So the efforts for reducing the uncertainty and dealing with enormous data will be necessary. In this paper, we create an ensemble Sea Surface Temperature (SST) using MODIS Aqua, MODIS Terra and COMS (Communication Ocean and Meteorological Satellite). We used Bayesian Model Averaging (BMA) as ensemble method. The principle of the BMA is synthesizing the conditional probability density function (PDF) using posterior probability as weight. The posterior probability is estimated using EM algorithm. The BMA PDF is obtained by weighted average. As the result, the ensemble SST showed the lowest RMSE and MAE, which proves the applicability of BMA for satellite data ensemble. As future work, parallel processing techniques using Hadoop framework will be adopted for more efficient computation of very big satellite data.

Paper Details

Date Published: 23 October 2013
PDF: 6 pages
Proc. SPIE 8895, High-Performance Computing in Remote Sensing III, 889506 (23 October 2013); doi: 10.1117/12.2029203
Show Author Affiliations
Kwangjin Kim, Pukyong National Univ. (Korea, Republic of)
Yang Won Lee, Pukyong National Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 8895:
High-Performance Computing in Remote Sensing III
Bormin Huang; Antonio J. Plaza; Zhensen Wu, Editor(s)

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