A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
<p>The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-07-01
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| Series: | The Cryosphere |
| Online Access: | https://www.the-cryosphere.net/12/2287/2018/tc-12-2287-2018.pdf |
| Summary: | <p>The accuracy of hydrological predictions in
snow-dominated regions deeply depends on the quality of the snowpack
simulations, with dynamics that strongly affect 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 to
investigate 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 parameter
perturbation on the filter updating of the snowpack state; the system
sensitivity to (3) the frequency of the assimilated observations, and (4) the
ensemble size.</p><p>The perturbation of the meteorological forcing data generally turns out to be
insufficient for preventing the sample impoverishment of the particle sample,
which is highly limited when jointly perturbating key model parameters. However, the
parameter perturbation sharpens the system sensitivity to
the frequency of the assimilated observations, which can be successfully
relaxed by introducing indirectly estimated information on snow-mass-related
variables. The ensemble size is found not to greatly impact the filter
performance in this point-scale application.</p> |
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| ISSN: | 1994-0416 1994-0424 |