Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP
Anne ROJAHN, Nora Schenk, Peter Jan van Leeuwen, Roland Potthast
Abstract
In a global numerical weather prediction (NWP) modeling framework we study the implementation of Gaussian uncertainty of individual particles into the assimilation step of a localized adaptive particle filter (LAPF). We obtain a local representation of the prior distribution as a mixture of basis functions. In the assimilation step, the filter calculates the individual weight coefficients and new particle locations. It can be viewed as a combination of the LAPF and a localized version of a Gaussian mixture filter, i.e., a Localized Mixture Coefficients Particle Filter (LMCPF).
Topics & Concepts
Particle filterGaussianData assimilationNumerical weather predictionParticle (ecology)Ensemble Kalman filterGaussian filterMixture modelFilter (signal processing)MeteorologyRepresentation (politics)MathematicsStatistical physicsComputational physicsEnvironmental scienceAlgorithmApplied mathematicsPhysicsComputer scienceKalman filterGeologyStatisticsExtended Kalman filterComputer visionQuantum mechanicsPolitical sciencePoliticsOceanographyLawMeteorological Phenomena and SimulationsClimate variability and modelsTropical and Extratropical Cyclones Research