0346616 20240103193802.320100914235959.9 2 s. cav_un_epca*0346615Atmospheric sciences1-2Americam Geophzsical Society2010 ensemble Kalman filter inflation factor adaptive tunning observation error Rao-Blackwellized particle filter cav_un_auth*0228606 Hofman Radek Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2010/AS/hofman-enkf within the marginalized particle filter and its application for adaptive estimation of inflation factor.pdf 1M0572 GA MŠk CZ cav_un_auth*0001814 CEZ:AV0Z10750506 We are concerned with Bayesian approach to data assimilation. Many common assimilation techniques, such as Kalman filtering and particle filtering, are special cases of the approximative solution of the Bayesian equations. Both of these techniques use a single approximation for the posterior probability density function (pdf) of the state vector. In this paper we advocate the use of marginalized particle filter (MPF, also known as Rao-Blackwelized particle filter), which splits the state vector in two disjoint parts and approximates their densities differently. Simulation study was performed as a twin experiment when measurements are sampled from a twin model and perturbed. The resulting assimilation scheme proved to be robust with respect to initial conditions and the inherent adaptivity of the inflation factor accelerated convergence to the simulated values. cav_un_auth*0263676 2010 The Meeting of the Americas Foz do Iguaçu 08.08.2010-12.08.2010 BR 2011 BC 4 O 4o 20231122134125.3 http://hdl.handle.net/11104/0187596 2010 Soubory v repozitáři: 0346616.pdf cav_un_epca*0346615 2010 The Meeting of the Americas Atmospheric sciences 1 2 Americam Geophzsical Society 2010