A Particle Filter scheme for multivariate data assimilation into a
point-scale snowpack model in Alpine environment
Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, whose dynamics strongly affects the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims at investigating the performance of a multivariate Sequential Importance Resampling – Particle Filter scheme designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameters resampling on the filter updating of the snowpack state; (3) the system sensitivity to the frequency of the assimilated observations.