Publications associées à Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Damien Bouffard, Firat Ozdemir, Cintia Luz Ramon Casanas, Camille Roland Marie Minaudo

We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, SPUX, with a hydrodynamics packa ...

COPERNICUS GESELLSCHAFT MBH2022

Radar-rain gauge merging and discharge data assimilation for flood forecasting in Alpine catchments

Alain Tommy Foehn

Floods are responsible for one third of the economic losses induced by natural hazards throughout the world. To better protect the population and infrastructures, flood forecasting systems make us of weather forecasts to foresee floods several days in adva ...

EPFL2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Gabriel Girard, Jonathan Rafael Patino Lopez, Erick Jorge Canales Rodriguez, Marco Pizzolato, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Sidi-Rachid Cherkaoui, Jean-Yves Le Boudec, Mario Paolone, Lorenzo Zanni

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

Real-time projections of cholera outbreaks through data assimilation and rainfall forecasting

Flavio Finger, Andrea Rinaldo, Enrico Bertuzzo, Damiano Pasetto

Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions still exact high death tolls mostly due to the unpreparedness of health care infrastructures to face unforeseen emergencies. In this context, mathematical models for the ...

Elsevier2016

Improved Estimation of the Specific Differential Phase Shift Using a Compilation of Kalman Filter Ensembles

Alexis Berne, Marc Schneebeli Zeugin, Jacopo Grazioli

A new algorithm for the accurate estimation of the specific differential phase shift on propagation (K-dp) from noisy total differential phase shift (Psi(dp)) measurements is presented for data acquired with a polarimetric weather radar. The new approach, ...

Institute of Electrical and Electronics Engineers2014