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Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting
Author(s) -
Rémy S.,
Pannekoucke O.,
Bergot T.,
Baehr C.
Publication year - 2011
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.915
Subject(s) - data assimilation , ensemble kalman filter , particle filter , meteorology , filter (signal processing) , kalman filter , numerical weather prediction , humidity , initial value problem , environmental science , particle (ecology) , control theory (sociology) , mathematics , computer science , statistics , extended kalman filter , geology , geography , artificial intelligence , control (management) , computer vision , mathematical analysis , oceanography
COBEL‐ISBA, a boundary‐layer 1D numerical model, has been developed for the very‐short‐term forecasting of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature, specific humidity and liquid water content. As fog forecasting is a threshold problem, the model is strongly nonlinear. A new assimilation method based on a genetic selection particle filter was tested to produce the initial conditions. The particle filter was adapted for a deterministic forecast and to take into account the time dimension by minimizing the error on a time window. A simplified particle filter was also used to determine the initial conditions in the soil. The filter was tested with two sets of simulated observations. In all cases, the initial conditions produced by this algorithm were of considerably better quality than the ones obtained with a Best Linear Unbiased Estimator (BLUE). The forecast of the control variables and of fog events was also improved. When comparing scores with the ones obtained with an ensemble Kalman filter (EnKF), the particle filter showed better performances for most of the cases. The ensemble size impacted the frequency of filter collapse and the quality of the initial temperature and specific humidity profiles in the lower part of the domain. Copyright © 2011 Royal Meteorological Society