A tempered particle filter to enhance the assimilation of SAR derived
flood extent maps into flood forecasting models.
Data Assimilation (DA) is a powerful tool to optimally combine uncertain
model simulations and observations. Among DA techniques, the Particle
Filter (PF) has gained attention for its capacity to deal with
non-linear systems and for its relaxation of the Gaussian assumption.
However, the PF may suffer from degeneracy and sample impoverishment. In
this study, we propose an innovative approach, based on a Tempered
Particle Filter (TPF), aiming at mitigating PFs issues, thus extending
over time the assimilation benefits. Flood probabilistic maps derived
from Synthetic Aperture Radar data are assimilated into a flood
forecasting model through an iterative process including a particle
mutation in order to keep diversity within the ensemble. Results show an
improvement of the model forecasts accuracy, with respect to the Open
Loop (OL): on average the RMSE of water levels decrease by 80% at the
assimilation time and by 60% two days after the assimilation. A
comparison with the Sequential Importance Sampling (SIS), is carried out
showing that although SIS performances are generally comparable to the
TPF ones at the assimilation time, they tend to decrease more quickly.
For instance, on average TPF-based RMSE are by 20% lower compared to
the SIS-based ones two days after the assimilation. The application of
the TPF determines higher CSI values compared to the SIS. On average the
increase in performances lasts for almost 3 days after the assimilation.
Our study provides evidence that the application of the variant of the
TPF enables more persistent benefits compared to the SIS.