Premium
Improving particle filters in rainfall‐runoff models: Application of the resample‐move step and the ensemble Gaussian particle filter
Author(s) -
Plaza Guingla Douglas A.,
Keyser Robin,
Lannoy Gabriëlle J. M.,
Giustarini Laura,
Matgen Patrick,
Pauwels Valentijn R. N.
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/wrcr.20291
Subject(s) - ensemble kalman filter , particle filter , auxiliary particle filter , gaussian , monte carlo localization , particle (ecology) , mathematics , computer science , control theory (sociology) , extended kalman filter , kalman filter , statistics , physics , artificial intelligence , geology , oceanography , quantum mechanics , control (management)
The objective of this paper is to analyze the improvement in the performance of the particle filter by including a resample‐move step or by using a modified Gaussian particle filter. Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure. Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall‐runoff model. The results indicate that the inclusion of the resample‐move step in the standard particle filter and the use of an optimal importance density function in the Gaussian particle filter improve the effectiveness of particle filters. Moreover, an optimization of the forecast ensemble used in this study allowed for a better performance of the modified Gaussian particle filter compared to the particle filter with resample‐move step.