Abstract
Abstract. 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 klocal 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.
Highlights
Seasonal snowpack is an essential element of mountainous areas
We introduced CrocO, a new ensemble data assimilation system able to reduce the errors of a spatialised snowpack model in locations that are not observed
We developed two variants of the particle filter (PF) using inflation or k localisation in order to spread the information from partial observations of the system, without degeneracy of the PF
Summary
Seasonal snowpack is an essential element of mountainous areas. Monitoring the evolution of its physical properties is essential to forecasting avalanche hazard (Morin et al, 2020) and rain-on-snow-related floods (Pomeroy et al, 2016; Würzer et al, 2016) as well as monitoring water resources (Mankin et al, 2015). B. Cluzet et al.: CrocO visible–near-infrared (Vis–NIR) reflectance and surface temperature provides comprehensive information over large areas but usually has a limited temporal resolution for a small set of variables. Cluzet et al.: CrocO visible–near-infrared (Vis–NIR) reflectance and surface temperature provides comprehensive information over large areas but usually has a limited temporal resolution for a small set of variables These observations are usually available in fractions of simulation domains only, even for spaceborne data (Davaze et al, 2018; Veyssière et al, 2019; Shaw et al, 2019). Note that pixel fractional snow cover (snow cover fraction, SCF) can be accurately retrieved even from noisy reflectances (Sirguey et al, 2009; Aalstad et al, 2020), but it inherits spatio-temporal limitations. SCF informativeness is limited in deep snowpack conditions (De Lannoy et al, 2012)
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