TY - JOUR
T1 - What the collapse of the ensemble Kalman filter tells us about particle filters
AU - Morzfeld, Matthias
AU - Hodyss, Daniel
AU - Snyder, Chris
N1 - Publisher Copyright:
© 2017 The Author(s).
PY - 2017
Y1 - 2017
N2 - The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.
AB - The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.
KW - Collapse of particle filters
KW - Ensemble kalman filter
KW - Particle filter
UR - https://www.scopus.com/pages/publications/85015045159
U2 - 10.1080/16000870.2017.1283809
DO - 10.1080/16000870.2017.1283809
M3 - Article
AN - SCOPUS:85015045159
SN - 0280-6495
VL - 69
JO - Tellus, Series A: Dynamic Meteorology and Oceanography
JF - Tellus, Series A: Dynamic Meteorology and Oceanography
IS - 1
M1 - 1283809
ER -