Articles | Volume 12, issue 7
https://doi.org/10.5194/tc-12-2287-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-12-2287-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2018
Research article |
| 12 Jul 2018
| 12 Jul 2018
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
Gaia Piazzi
CORRESPONDING AUTHOR
CIMA Research Foundation, Savona, 17100, Italy
Guillaume Thirel
Catchment Hydrology Research Group, HYCAR Research Unit, Irstea,
Antony, 92160, France
Lorenzo Campo
CIMA Research Foundation, Savona, 17100, Italy
Simone Gabellani
CIMA Research Foundation, Savona, 17100, Italy
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- Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings J. Magnusson et al. https://doi.org/10.1016/j.coldregions.2025.104435
- Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling R. Kim et al. https://doi.org/10.1016/j.rse.2019.03.016
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. https://doi.org/10.5194/gmd-15-9127-2022
- Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments S. Terzago et al. https://doi.org/10.5194/hess-24-4061-2020
- Forcing and evaluating detailed snow cover models with stratigraphy observations L. Viallon-Galinier et al. https://doi.org/10.1016/j.coldregions.2020.103163
25 citations as recorded by crossref.
- Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation K. Aalstad et al. https://doi.org/10.5194/gmd-19-8565-2026
- Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables J. Revuelto et al. https://doi.org/10.1016/j.jhydrol.2021.126966
- Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations B. Cluzet et al. https://doi.org/10.1016/j.coldregions.2019.102918
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran https://doi.org/10.3390/rs15041052
- Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery C. Deschamps‐Berger et al. https://doi.org/10.1029/2021WR030271
- IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021) F. Avanzi et al. https://doi.org/10.5194/essd-15-639-2023
- Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging Y. You et al. https://doi.org/10.1016/j.jhydrol.2022.128877
- Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey J. Helmert et al. https://doi.org/10.3390/geosciences8120489
- A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates Y. You et al. https://doi.org/10.5194/hess-27-2919-2023
- Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network B. Cluzet et al. https://doi.org/10.5194/tc-16-1281-2022
- 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude) Y. Lejeune et al. https://doi.org/10.5194/essd-11-71-2019
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. https://doi.org/10.5194/gmd-14-1595-2021
- Assimilation of synthetic observations of radar backscatters at Ku-band improves SWE estimates N. Leroux et al. https://doi.org/10.5194/tc-20-2773-2026
- An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian https://doi.org/10.3390/su141811395
- Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment E. Alonso-González et al. https://doi.org/10.5194/tc-17-3329-2023
- Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir H. Wander et al. https://doi.org/10.1002/ecs2.4752
- Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks A. Teweldebrhan et al. https://doi.org/10.3390/rs11010028
- Snow data assimilation for seasonal streamflow supply prediction in mountainous basins S. Metref et al. https://doi.org/10.5194/hess-27-2283-2023
- Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt F. Avanzi et al. https://doi.org/10.5194/gmd-15-4853-2022
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. https://doi.org/10.3389/feart.2020.00325
- Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings J. Magnusson et al. https://doi.org/10.1016/j.coldregions.2025.104435
- Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling R. Kim et al. https://doi.org/10.1016/j.rse.2019.03.016
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. https://doi.org/10.5194/gmd-15-9127-2022
- Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments S. Terzago et al. https://doi.org/10.5194/hess-24-4061-2020
- Forcing and evaluating detailed snow cover models with stratigraphy observations L. Viallon-Galinier et al. https://doi.org/10.1016/j.coldregions.2020.103163
Saved (final revised paper)
Latest update: 01 Nov 2026
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.
The study focuses on the development of a multivariate particle filtering data assimilation...
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