Articles | Volume 12, issue 7
Research article
12 Jul 2018
Research article |  | 12 Jul 2018

A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani

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Cited articles

Ades, M. and Van Leeuwen P. J.: An exploration of the equivalent weights particle filter, Q. J. Meteorol., 139, 820–840, 2013.
Anderson, E. A.: A point of energy and mass balance model of snow cover, Tech. Rep., Office of Hydrology – National Weather Service, 1976.
Andreadis, K. M. and Lettenmaier D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, 2005.
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Process., 50, 174–188, 2002.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407,, 2015.
Short summary
The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.