A novel approach for assimilating SAR derived floodextent map into flood forecasting model: the temperedparticle filter

Data Assimilation can improve forecast accuracy of flood inundation models by an optimal combination of uncertain model simulations and observations. Particle Filter (PF) has gained interest in the research community for its ability of dealing with non-linear systems and with any kind of observation and model error distributions. Using PF, one of the most difficult issues to deal with are degeneracy and sample impoverishment. In this study, we have investigated a novel approach, based on a Tempered Particle Filter (TPF), aiming to circumvent these issues, and increase the persistence in time of the assimilation benefits. Flood probabilistic maps derived from Synthetic Aperture Radar (SAR)
data are assimilated into a flood forecasting model. The process is iterative and includes a particle mutation in order to keep diversity within the ensemble. Results show an improvement of the model forecast accuracy as a result of the assimilation with respect to the ensemble without any assimilation (Open Loop, OL): on average the Root Mean Square Error decreases by 80% at the assimilation time and by 60% 2 days after the assimilation.
A comparison with a standard PF, the Sequential Importance Sampling (SIS), where degeneracy occurred, is carried out. Results are similar at the assimilation time but the increase of performance using the TPF are lasting longer. For instance, TPF-based RMSE are still lower than the OL RMSE 3 days after the assimilation, while the SIS-based RMSE becomes larger than the RMSE-OL 2 days after the assimilation.