ReviewQuarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain)2019
Particle filters for high-dimensional geoscience applications: A review.
Review in Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Physically consistent global atmospheric data assimilation with machine learning in latent space.Science advances · 2026Article
- Data assimilation in operator algebras.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- The Potential Benefits of Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests With All-Sky Satellite Infrared Radiances.Journal of advances in modeling earth systems · 2023Article
- Calibration methods to fit parameters within complex biological models.Frontiers in applied mathematics and statistics · 2023Article
- A comparison of numerical approaches for statistical inference with stochastic models.Stochastic environmental research and risk assessment : research journal · 2023Article
- A Tempered Particle Filter to Enhance the Assimilation of SAR-Derived Flood Extent Maps Into Flood Forecasting Models.Water resources research · 2022Article
- Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo.Journal of chemical theory and computation · 2022Article
- A particle flow filter for high-dimensional system applications.Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models.Statistics and computing · 2021Article
- Article
- Estimation of Time-Varying Spectral Peaks and Decomposition of EEG Spectrograms.IEEE access : practical innovations, open solutions · 2020Article
- Particle filters for high-dimensional geoscience applications: A review.Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society (Great Britain) · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the geosciences, but their application to high-dimensional geoscience systems 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 localization 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 high-dimensional nonlinear geoscience state-estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo-code, 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.
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Registered trials
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