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A tempered particle filter to enhance the assimilation of SAR derived flood extent maps into flood forecasting models.
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  • Concetta Di Mauro,
  • Nancy K Nichols,
  • Renaud Hostache,
  • Patrick Matgen,
  • Ramona-Maria Pelich,
  • Marco Chini,
  • Peter Jan van Leeuwen,
  • Guenter Bloeschl
Concetta Di Mauro

Corresponding Author:concetta.dimauro@gmail.com

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Nancy K Nichols
University of Reading
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Renaud Hostache
Luxembourg Institute of Science and Technology
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Patrick Matgen
Public Research Center - Gabriel Lippmann
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Ramona-Maria Pelich
Luxembourg Institute of Science and Technology
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Marco Chini
Luxembourg Institute of Science and Technology
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Peter Jan van Leeuwen
Colorado State University
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Guenter Bloeschl
Vienna University of Technology
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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.