Journal paper on Gaussian anamorphosis for data assimilation has been published

The journal paper “Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations” has been accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing.

This research work contributes to the SCO-FloodDAM-DT project, subsidized by the SCO-France.

Paper: https://ieeexplore.ieee.org/document/10336771
Project page can be found here.

Abstract
Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from Earth Observations from space. This article focuses on the assimilation of 2-D flood observations derived from synthetic aperture radar (SAR) images acquired during a flood event with a dual state-parameter ensemble Kalman filter (EnKF). Resulting binary wet/dry maps are here expressed in terms of wet surface ratios (WSRs) over a number of subdomains of the floodplain. This ratio is assimilated jointly with in situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with these SAR-derived measurements breaks a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this article lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis (GA) process. This DA strategy was validated and applied over the Garonne Marmandaise catchment (southwest of France) represented with a TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January and February 2021. It was shown that assimilating SAR-derived WSR observations in complement to the in situ water-level observations significantly improves the representation of the flood dynamics. The GA process brings further improvement to the DA analysis while also demonstrating to be a nonessential element. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote sensing datasets.

Keywords—flooding, hydrodynamics, data assimilation, Gaussianity, anamorphosis, ensemble Kalman Filter, remote sensing, Synthetic Aperture Radar, Sentinel-1.

Recommended citation:
T. H. Nguyen, S. Ricci, A. Piacentini, E. Simon, R. Rodriguez Suquet and S. Pena Luque, “Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-21, 2024, Art no. 4200521, doi: 10.1109/TGRS.2023.3338296.