TY  - JOUR
A1  - van Leeuwen, Peter Jan
A1  - Kunsch, Hans R.
A1  - Nerger, Lars
A1  - Potthast, Roland
A1  - Reich, Sebastian
T1  - Particle filters for high-dimensional geoscience applications: A review
JF  - Quarterly journal of the Royal Meteorological Society
N2  - 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.
KW  - hybrids
KW  - localization
KW  - nonlinear data assimilation
KW  - particle filters
KW  - proposal densities
Y1  - 2019
U6  - https://doi.org/10.1002/qj.3551
SN  - 0035-9009
SN  - 1477-870X
VL  - 145
IS  - 723
SP  - 2335
EP  - 2365
PB  - Wiley
CY  - Hoboken
ER  -