<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.</p></abstract>

The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site"}

The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production,...

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Publicado en línea: 07 nov 2018

Páginas: 4 - 19

Recibido: 18 may 2017

Aceptado: 11 dic 2017

DOI: https://doi.org/10.2478/johh-2018-0013

Palabras clave
Snow modeling, Energy-balance model, Data Assimilation, Ensemble Kalman Filter

© 2018 Gaia Piazzi, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

An Enkf-Based Scheme for Snow Multivariable Data Assimilation at an Alpine Site

Publicado en línea: 07 nov 2018

Páginas: 4 - 19

Recibido: 18 may 2017

Aceptado: 11 dic 2017

DOI: https://doi.org/10.2478/johh-2018-0013

Palabras claveSnow modeling, Energy-balance model, Data Assimilation, Ensemble Kalman Filter

© 2018 Gaia Piazzi, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
Snow modeling, Energy-balance model, Data Assimilation, Ensemble Kalman Filter