Particle filters for high‐dimensional geoscience applications: A review
| Autor: | Sebastian Reich, Roland Potthast, Peter Jan van Leeuwen, Hans R. Künsch, Lars Nerger |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Atmospheric Science 010504 meteorology & atmospheric sciences Computer science Earth science particle filters FOS: Physical sciences Review Article proposal densities Statistics - Applications 01 natural sciences localization 010305 fluids & plasmas Physics - Geophysics Data assimilation Resampling 0103 physical sciences Applications (stat.AP) 62M86 0105 earth and related environmental sciences hybrids Emphasis (telecommunications) Kalman filter nonlinear data assimilation Numerical weather prediction Geophysics (physics.geo-ph) Nonlinear system 13. Climate action Inefficiency Particle filter |
| Zdroj: | Quarterly Journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) Quarterly Journal of the Royal Meteorological Society EPIC3Quarterly Journal of the Royal Meteorological Society, 145(723), pp. 2335-2365 Quarterly Journal of the Royal Meteorological Society, 145 (723) |
| ISSN: | 1477-870X 0035-9009 |
| Popis: | Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the geosciences has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localisation and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state of the art discussion of present efforts of developing particle filters for highly nonlinear geoscience state-estimation problems with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations, and unifications, highlighting hidden connections, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction suggesting that they will become mainstream soon. Comment: Review paper, 36 pages, 9 figures, Resubmitted to Q.J.Royal Meteorol. Soc |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text