a coupled weather and wildland fire forecasting system
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A coupled weather and wildland fire forecasting system with assimilation of satellite remote sensing data Jan Mandel University of Colorado Denver and Institute for Informatics, Czech Academy of Sciences with Jonathan D. Beezley, Mary Ann


  1. A coupled weather and wildland fire forecasting system with assimilation of satellite remote sensing data Jan Mandel University of Colorado Denver and Institute for Informatics, Czech Academy of Sciences with Jonathan D. Beezley, Mary Ann Jenkins, Adam K. Kochanski, Volodymyr Y. Kondratenko, Lixin Lu, Sher Shranz, Martin Vejmelka Supported by NASA ROSES A.35 11-FIRES11-0038 and NSF DMS-1216481

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  20. t/%+/,(>%-$#( forecast state distribution gaussian, mean u f covariance Q : p f ( u ) = const exp( ! || u ! u f || Q ! 1 2 /2) observation Hu + gaussian error with zero mean, covariance R : p ( u | d ) = const exp( ! || Hu ! d || R ! 1 2 /2) analysis = after the data is taken into account, by Bayes theorem p a ( u ) = const p ( u | d ) p f ( u ) = const exp( ! (|| Hu ! d || R ! 1 + || u ! u f || Q ! 1 2 ) / 2) 2 = const exp( ! || u ! u a || P ! 1 2 /2) analysis mean u a = u f ! K ( d ! Hu f ) covariance Q a = ( I ! KH ) Q where K = QH T ( HQH T + R ) ! 1 is the Kalman gain matrix 34(

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