Abstract
The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterisations, and unresolved terrain features. In-situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large scale modelling errors by means of data assimilation. In this work, we assimilate HS observations from an in-situ network of 295 stations covering the French Alps, Pyrenees and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialised snow cover modelling framework, we investigate whether such observations can be used to correct neighbouring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modelling, based on a Particle Filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localisation strategies to assimilate snow observations. These approaches are evaluated in a Leave-One-Out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that intermediate localisation radius of 35–50 km yield a slightly lower root mean square error (RMSE), and a better Spread-Skill than the strategy assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modelling errors are the largest, e.g. the Haute-Ariège, Andorra and the Extreme Southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses, and therefore snow simulations. In-situ HS observations thus shows an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.
Highlights
20 Better monitoring the spatio-temporal variability of the mountainous snow cover is paramount to improve the forecasting of snow-related hazards (Morin et al, 2020) and anticipate downstream river flow (Lettenmaier et al, 2015)
Strong increases in the oper and open-loop biases match with precipitation events, and they are only partly compensated by the following snow settling period, suggesting 345 that it is likely that error compensations take place in the oper chain, between solid precipitation amounts, fresh snow density, snow compaction, and ablation processes as suggested by results from Quéno et al (2016)
This study investigates the potential for localised versions of the Particle Filter to spatially propagate information from in-situ observations of the height of snow (HS) in an ensemble of snowpack simulations
Summary
20 Better monitoring the spatio-temporal variability of the mountainous snow cover is paramount to improve the forecasting of snow-related hazards (Morin et al, 2020) and anticipate downstream river flow (Lettenmaier et al, 2015). The snow cover inherits a high spatial variability from several factors. The topography controls on the precipitation phase, air temperature, wind exposition and radiation fluxes (Durand et al, 1993; Oliphant et al, 2003). Wind drift redistributes snow at every scale (Mott et al, 2018). Snowpack models are commonly used to derive snowpack properties in the mountains. Their ability to represent snow cover variability over large areas is inherently limited by large errors in their meteorological forcings (Raleigh et al, 2015), and uncertain physical parameterisations (Essery et al, 2013; Krinner et al, 2018). Explicitly accounting for processes such as wind drift and snow-vegetation interaction is not yet affordable at large scales
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