Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1595–1614, 2021
https://doi.org/10.5194/gmd-14-1595-2021
Geosci. Model Dev., 14, 1595–1614, 2021
https://doi.org/10.5194/gmd-14-1595-2021
Development and technical paper
19 Mar 2021
Development and technical paper | 19 Mar 2021

CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework

Bertrand Cluzet et al.

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Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b, c
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a, b
Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. a
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely-sensed fractional snow-covered area, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-335, in review, 2020. a
Baba, M., Gascoin, S., and Hanich, L.: Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco, Remote Sens.-Basel, 10, 1982, https://doi.org/10.3390/rs10121982, 2018. a
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In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.