A tempered particle filter to enhance the assimilation of SAR derived flood extent maps into flood forecasting models. [* Cross-Reference *]

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Concetta Di Mauro; Nancy Nichols; Renaud Hostache; Patrick Matgen; Ramona Pelich; Marco Chini; Peter Jan van Leeuwen; Guenter Bloeschl, 2022, "A tempered particle filter to enhance the assimilation of SAR derived flood extent maps into flood forecasting models. [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.KU5WDU, LIST Open Repository, V1

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Description	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. (2022-01-19) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1002/essoar.10510211.1 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Physics
Keyword	Data assimilation, Particle filter, Ensemble Kalman filter, Mean squared error, Flood myth, Probabilistic logic, Meteorology, Environmental science, Computer science, Algorithm, Mathematics, Mathematical optimization, Statistics, Kalman filter, Geography, Extended Kalman filter
License/Data Use Agreement	CC BY 4.0


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Persistent Identifier	perma:LIST.KU5WDU
Publication Date	2026-07-06
Title	A tempered particle filter to enhance the assimilation of SAR derived flood extent maps into flood forecasting models. [* Cross-Reference *]
Other Identifier	https://doi.org/10.1002/essoar.10510211.1 OpenAlex ID: https://openalex.org/W4205800358
Author	(Luxembourg Institute of Science and Technology)https://orcid.org/0000-0003-4026-6850 (University of Reading)https://orcid.org/0000-0003-1133-5220 (TU Wien, Luxembourg Institute of Science and Technology)https://orcid.org/0000-0002-8109-6010 (Luxembourg Institute of Science and Technology, Colorado State University)https://orcid.org/0000-0001-6668-4693 (TU Wien, Luxembourg Institute of Science and Technology)https://orcid.org/0000-0002-4313-3116 (TU Wien, Luxembourg Institute of Science and Technology)https://orcid.org/0000-0002-9094-0367 (University of Reading, Colorado State University)https://orcid.org/0000-0003-2325-5340 Guenter Bloeschl (TU Wien, Luxembourg Institute of Science and Technology)
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	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. (2022-01-19) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1002/essoar.10510211.1 for the original and latest version of the publication* (2026-07-01)
Subject	Earth and Environmental Sciences; Physics
Keyword	Data assimilation Particle filter Ensemble Kalman filter Mean squared error Flood myth Probabilistic logic Meteorology Environmental science Computer science Algorithm Mathematics Mathematical optimization Statistics Kalman filter Geography Extended Kalman filter
Topic Classification	Flood Risk Assessment and Management Hydrology and Drought Analysis Hydrology and Watershed Management Studies
Deposit Date	2022-01-19
Data Type	Preprint

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