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. With a high-resolution digital elevation model (DEM), we can use such measurements to retrieve snow depth profiles even in remote areas where snow amounts are poorly constrained. However, the adoption of these retrievals remains low since they are very sparse in space, as the satellite measures along profiles, and in time, as the revisit is 3 months. Data assimilation (DA) methods can exploit snow observations to constrain snow models and provide gap-free distributed simulations. The assimilation of observations related to snow cover is well-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 a topographical index and the melt-out date climatology. We demonstrate this framework for a small experimental catchment in the Spanish Pyrenees through three experiments. We assimilate different snow observations in an intermediate-complexity snow model: fractional snow-covered area (fSCA) retrievals from Sentinel-2, snow depth profiles from ICESat-2 located in proximity of the catchment or both fSCA and depth in a joint assimilation experiment. Results show that assimilating ICESat-2 snow depth profiles successfully updates the neighbouring unobserved catchment, improving the simulated average snow depth compared to the prior run. Another encouraging finding is that adding the snow depth profiles to fSCA observations leads to an accurate reconstruction of the snow depth spatial distribution. Evaluating the simulations with a set of independent drone-based snow depth maps using a probabilistic skill score, we find that for the accumulation season the joint assimilation’s score improves by 19 % the established approach of only assimilating fSCA. The direct but incomplete snow depth information from ICESat-2 is a key constraint on simulated basin-average snow depth. This study makes use of globally available datasets and shows the promise of adopting ICESat-2’s snow depth retrievals in seasonal snow modelling, especially when also assimilating complementary observations. In light of our encouraging results, more research with different experimental designs in varying snow conditions combined with continued methodological development is desirable to further catalyse the use of these retrievals in cryospheric and hydrologic applications.