Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-639-2023
https://doi.org/10.5194/essd-15-639-2023
Data description paper
 | 
08 Feb 2023
Data description paper |  | 08 Feb 2023

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris

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Cited articles

Alfieri, L., Avanzi, F., Delogu, F., Gabellani, S., Bruno, G., Campo, L., Libertino, A., Massari, C., Tarpanelli, A., Rains, D., Miralles, D. G., Quast, R., Vreugdenhil, M., Wu, H., and Brocca, L.: High-resolution satellite products improve hydrological modeling in northern Italy, Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, 2022. a
Apicella, L., Puca, S., Lagasio, M., Meroni, A., Milelli, M., Vela, N., Garbero, V., Ferraris, L., and Parodi, A.: The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy, Bulletin of Atmospheric Science and Technology, 2, 1–14, 2021. a
Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari, D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.: Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation, Remote Sens.-Basel, 10, 765, https://doi.org/10.3390/rs10050765, 2018. a
Avanzi, F., Maurer, T., Glaser, S. D., Bales, R. C., and Conklin, M. H.: Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters, J. Hydrol., 582, 124478, https://doi.org/10.1016/j.jhydrol.2019.124478, 2020. a, b
Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.: Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, 2021a. a, b, c, d, e, f, g
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Short summary
Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
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