CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework
<p>Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited spatio-temporal cover...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | English |
| Publicado: |
Copernicus Publications
2021-03-01
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| Colección: | Geoscientific Model Development |
| Acceso en línea: | https://gmd.copernicus.org/articles/14/1595/2021/gmd-14-1595-2021.pdf |
| Sumario: | <p>Monitoring the evolution of snowpack properties in mountainous areas is crucial for avalanche hazard forecasting and water resources
management. In situ and remotely sensed observations provide precious information on the state of the snowpack but usually offer limited
spatio-temporal coverage of bulk or surface variables only. In particular, visible–near-infrared (Vis–NIR) reflectance observations can provide
information about the snowpack surface properties but are limited by terrain shading and clouds. Snowpack modelling enables the estimation of any
physical variable virtually anywhere, but it is affected by large errors and uncertainties. Data assimilation offers a way to combine both sources of
information and to propagate information from observed areas to non-observed areas. Here, we present CrocO (Crocus-Observations), an ensemble data
assimilation system able to ingest any snowpack observation (applied as a first step to the height of snow (HS) and Vis–NIR reflectances) in a
spatialised geometry. CrocO uses an ensemble of snowpack simulations to represent modelling uncertainties and a particle filter (PF) to reduce
them. The PF is prone to collapse when assimilating too many observations. Two variants of the PF were specifically implemented to ensure that
observational information is propagated in space while tackling this issue. The global algorithm ingests all available observations with an
iterative inflation of observation errors, while the <i>klocal</i> algorithm is a localised approach performing a selection of the observations to
assimilate based on background correlation patterns. Feasibility testing experiments are carried out in an identical twin experiment setup, with
synthetic observations of HS and Vis–NIR reflectances available in only one-sixth of the simulation domain. Results show that compared against
runs without assimilation, analyses exhibit an average improvement of the snow water equivalent continuous rank probability score (CRPS) of 60 %
when assimilating HS with a 40-member ensemble and an average 20 % CRPS improvement when assimilating reflectance with a 160-member
ensemble. Significant improvements are also obtained outside the observation domain. These promising results open a possibility for the assimilation of real
observations of reflectance or of any snowpack observations in a spatialised context.</p> |
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| ISSN: | 1991-959X 1991-9603 |