Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter

Abstract. The satellite laser altimeter ICESat-2 provides accurate surface elevation observations across the globe. Where a high-resolution DEM is available, we can use these measurements to retrieve snow depth profiles even in areas where snow amounts are poorly constrained, despite being of great societal interest. However, the adoption of these retrievals remains low since they are very sparse in space (the satellite measures along profiles) and in time (the revisit is 3 months). Data assimilation methods can exploit snow observations to constrain snow models and provide gap-free snow map time series. Assimilation of observations like snow cover is established, but there are currently no methods to assimilate sparse ICESat-2 snow depth profiles. We propose an approach that spatially propagates information using – instead of the classic geographical distance – an abstract distance measured in a feature space defined by topographical parameters and the melt-out climatology. We demonstrate this framework for a small experimental catchment in the Spanish Pyrenees through three experiments. We assimilate different observations in an intermediate-complexity snow model: fractional snow cover retrievals from Sentinel-2, snow depth profiles from ICESat-2 located in the proximity of the catchment, or both snow cover and depth in a joint assimilation experiment. Results show that assimilating ICESat-2 snow depth profiles successfully updates the neighboring unobserved catchment, improving the simulated average snow depth compared to the prior run. Moreover, adding the snow depth profiles to fractional snow-covered area observations leads to an accurate reconstruction of the snow depth spatial distribution, improving the skill score by 22 %.