Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP

• Published in: Journal of the Meteorological Society of Japan Vol. 101; no. 4; pp. 233 - 253
• Main Authors: ROJAHN, Anne, LEEUWEN, Peter Jan VAN, POTTHAST, Roland, SCHENK, Nora
• Format: Journal Article
• Language: English
• Published: Meteorological Society of Japan 2023
• Subjects: data assimilation; high dimensional; Non-Gaussian; numerical weather prediction; particle filter
• ISSN: 0026-1165; 2186-9057
• DOI: 10.2151/jmsj.2023-015

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)”.Here, we investigate the feasibility of the LMCPF within a global operational framework and evaluate the relationship between prior and posterior distributions and observations. Our simulations are carried out in a standard pre-operational experimental set-up with the full global observing system, 52 km global resolution and 106 model variables. Statistics of particle movement in the assimilation step are calculated. The mixture approach is able to deal with the discrepancy between prior distributions and observation location in a real-world framework and to pull the particles towards the observations in a much better way than the pure LAPF. This shows that using Gaussian uncertainty can be an important tool to improve the analysis and forecast quality in a particle filter framework.