EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

S3M Italy: a real-time, open-source cryospheric-forecasting chain for applications on a large scale 

Francesco Avanzi1, Simone Gabellani1, Fabio Delogu1, Francesco Silvestro1, Silvia Puca2, Alexander Toniazzo2, Pietro Giordano2, Marco Falzacappa2, Sara Ratto3, Hervè Stevenin3, Antonio Cardillo4, Edoardo Cremonese5, and Umberto Morra di Cella1,5
Francesco Avanzi et al.
  • 1CIMA Research Foundation, Savona, Italy (
  • 2Italian Civil Protection Department, Rome, Italy
  • 3Regione Autonoma Valle d’Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
  • 4Molise Region, Civil Protection, Regional Functional Center, Campochiaro (Cb) - Italy
  • 5Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy

Monitoring the state of the cryosphere in real time is a key to improved risk and water resources management, especially in a warming climate. All around the world, this goal is achieved through forecasting chains combining models with in-situ and remote-sensing measurements. Here, we discuss lessons learned while developing S3M Italy, one such chain delivering hourly estimates of snow water equivalent, density, snow and glacier melt, and bulk liquid water content across the Italian territory (300k+ km2, 200 m resolution1.5 hour turnaround). S3M Italy includes downloaders to ingest input data from automatic weather stations, spatialization tools to convert these data into weather-input maps, blending routines for deriving daily snow-covered-area maps from ESA Sentinel 2, NASA MODIS, and EUMETSAT H-SAF products, mapping algorithms based on multilinear regressions for assimilating on-the-ground snow-depth data, as well as algorithms to manage parallelized runs and then mosaic model outputs. S3M Italy has been developed to support decisions by the Italian Civil Protection Agency and is fully open source, not only in terms of underlying models (, but also in terms of all pre-processing routines ( 

How to cite: Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Puca, S., Toniazzo, A., Giordano, P., Falzacappa, M., Ratto, S., Stevenin, H., Cardillo, A., Cremonese, E., and Morra di Cella, U.: S3M Italy: a real-time, open-source cryospheric-forecasting chain for applications on a large scale , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-528,, 2022.