EGU22-10582, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Novel Approach to Estimate Snowfall over an Alpine Terrain via the Assimilation of Sentinel-1 Snow Depth Observations

Manuela Girotto1, Giuseppe Formetta2, Shima Azimi2,3, Sara Modanesi3, Gabrielle De Lannoy4, Hans Lievens4, Riccardo Rigon2, and Christian Massari3
Manuela Girotto et al.
  • 1University of California, Berkeley USA (
  • 2University of Trento, Trento Italy
  • 3CNR National Research Council
  • 4KU Leuven

Estimating snowfall over mountain regions is an extremely challenging task due to the high variability of spatial and temporal precipitation gradients. Traditional methods to estimate snowfall include in-situ gauges, doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods, alone, is unable to capture the complex orographic precipitation. For example, in-situ gauges are often too sparse and lead to significant interpolation errors; radar beams are shielded by the complex mountainous terrains; satellite estimates are sub-optimal over snowy mountains regions; while the physical parameterization of mountainous orography remains challenging for estimating precipitation in numerical models. A potential method to overcome model and observational shortcomings in precipitation estimation is land surface data assimilation, which leverages the information content in both land surface observations and models while minimizing their limitations due to uncertainty. Recently, the ESA and Copernicus Sentinel-1 constellation has been used to map snow-depth across the Northern Hemisphere mountains with 1 km spatial resolution by exploiting C-band cross-polarized backscatter radar measurements. This work aims at characterizing and estimating snowfall precipitation errors over an alpine watershed located in Trentino Alto Adige, Italy. We derive the snowfall errors via the data assimilation of 1 km Sentinel-1 snow-depth observations within a numerical model. The data assimilation applies a particle batch smoother to the coupled snow-17 and Sacramento hydrological models.

How to cite: Girotto, M., Formetta, G., Azimi, S., Modanesi, S., De Lannoy, G., Lievens, H., Rigon, R., and Massari, C.: A Novel Approach to Estimate Snowfall over an Alpine Terrain via the Assimilation of Sentinel-1 Snow Depth Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10582,, 2022.