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Matlab code associated with manuscript "Lognormal and Mixed Gaussian–Lognormal Kalman Filters"

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dc.contributor.authorFletcher, Steven
dc.date.accessioned2022-03-02T17:33:34Z
dc.date.available2022-03-02T17:33:34Z
dc.date.issued2022
dc.descriptionThis is a MATLAB m file that implements the mixed Gaussian-lognormal Kalman filter (MXKF) with the Lorenz 1963 model for lognormally distributed background and observational errors for the z component, Gaussian distributed background and observational errors for the x and y components, and compares this against the performance of the current Extended Kalman Filter (EKF). The code has four command line prompts to run, and produces two sets of figure: The first figure is of the trajectories of the z and x components for the true, MXKF, and the EKF, along with the observations, while the second plots contains the analysis errors for the z and x solutions from the MXKF and EKJF.en_US
dc.descriptionCooperative Institute for Research in the Atmosphere
dc.description.abstractIn this paper we shall present the derivation of two new forms of the Kalman filter equations; the first is for a pure lognormally distributed random variable, while the second set of Kalman filter equations will be for a combination of Gaussian and lognormally distributed random variables. We shall show that the appearance is similar to that of the Gaussian based equations, but that the analytical state is a multivariate median and not the mean. We shall show results of the mixed distribution Kalman filter with the Lorenz 1963 model with lognormal errors for the background and observations of the $z$ component, and compare them to results and forecasts from a traditional Gaussian based Kalman filter and show that under certain circumstances the new approach produces more accurate results.en_US
dc.description.sponsorshipThe National Science Foundation grant AGS-1738206 at CIRA/CSU supported authors 1, 3, 4, 5 and 6, while authors 2 and 3 were supported by NOAA's Hurricane Forecast Improvement Program Award NA18NWS4680059. Funding for authors 1 and 7 came from National Science Foundation grant AGS-2033405 at CIRA/CSU.en_US
dc.format.mediumMATLAB
dc.format.mediumTXT
dc.identifier.urihttps://hdl.handle.net/10217/234474
dc.identifier.urihttp://dx.doi.org/10.25675/10217/234474
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartofResearch Data
dc.relation.isreferencedbyFletcher, S. J., Zupanski, M., Goodliff, M. R., Kliewer, A. J., Jones, A. S., Forsythe, J. M., Wu, T., Hossen, M. J., & Van Loon, S. (2023). Lognormal and Mixed Gaussian–Lognormal Kalman Filters, Monthly Weather Review, 151(3), 761-774. https://doi.org/10.1175/MWR-D-22-0072.1en_US
dc.subjectNon-Gaussian Kalman Filteren_US
dc.subjectExtended Kalman Filteren_US
dc.subjectLorenz 63 modelen_US
dc.titleMatlab code associated with manuscript "Lognormal and Mixed Gaussian–Lognormal Kalman Filters"en_US
dc.typeDataseten_US

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MATLAB code for the extended Kalman filter and the mixed Gaussian-lognormal Kalman filter for the Lorenz 63 model
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A README file to explain what the matlab code does as well as what interactions occur between the user and the code for it to work
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