Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
Edoardo Raparelli
CORRESPONDING AUTHOR
Dept. Information Engineering, Electronics and Telecommunications, Sapienza Università di Roma, Rome, Italy
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Paolo Tuccella
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Dept. Physical and Chemical Sciences, Università degli Studi dell'Aquila, L'Aquila, Italy
Italian Glaciological Committee, Turin, Italy
Valentina Colaiuda
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
Dept. Physical and Chemical Sciences, Università degli Studi dell'Aquila, L'Aquila, Italy
Frank S. Marzano
Dept. Information Engineering, Electronics and Telecommunications, Sapienza Università di Roma, Rome, Italy
Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), L'Aquila, Italy
deceased
Related authors
No articles found.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Adrien Deroubaix, Marco Vountas, Benjamin Gaubert, Maria Dolores Andrés Hernández, Stephan Borrmann, Guy Brasseur, Bruna Holanda, Yugo Kanaya, Katharina Kaiser, Flora Kluge, Ovid Oktavian Krüger, Inga Labuhn, Michael Lichtenstern, Klaus Pfeilsticker, Mira Pöhlker, Hans Schlager, Johannes Schneider, Guillaume Siour, Basudev Swain, Paolo Tuccella, Kameswara S. Vinjamuri, Mihalis Vrekoussis, Benjamin Weyland, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2024-516, https://doi.org/10.5194/egusphere-2024-516, 2024
Preprint archived
Short summary
Short summary
This study assesses atmospheric composition using air quality models during aircraft campaigns in Europe and Asia, focusing on carbonaceous aerosols and trace gases. While carbon monoxide is well modeled, other pollutants have moderate to weak agreement with observations. Wind speed modeling is reliable for identifying pollution plumes, where models tend to overestimate concentrations. This highlights challenges in accurately modeling aerosol and trace gas composition, particularly in cities.
Adrien Deroubaix, Marco Vountas, Benjamin Gaubert, Maria Dolores Andrés Hernández, Stephan Borrmann, Guy Brasseur, Bruna Holanda, Yugo Kanaya, Katharina Kaiser, Flora Kluge, Ovid Oktavian Krüger, Inga Labuhn, Michael Lichtenstern, Klaus Pfeilsticker, Mira Pöhlker, Hans Schlager, Johannes Schneider, Guillaume Siour, Basudev Swain, Paolo Tuccella, Kameswara S. Vinjamuri, Mihalis Vrekoussis, Benjamin Weyland, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2024-521, https://doi.org/10.5194/egusphere-2024-521, 2024
Preprint archived
Short summary
Short summary
This study explores the proportional relationships between carbonaceous aerosols (black and organic carbon) and trace gases using airborne measurements from two campaigns in Europe and East Asia. Differences between regions were found, but air quality models struggled to reproduce them accurately. We show that these proportional relationships can help to constrain models and can be used to infer aerosol concentrations from satellite observations of trace gases, especially in urban areas.
Adrien Deroubaix, Laurent Menut, Cyrille Flamant, Peter Knippertz, Andreas H. Fink, Anneke Batenburg, Joel Brito, Cyrielle Denjean, Cheikh Dione, Régis Dupuy, Valerian Hahn, Norbert Kalthoff, Fabienne Lohou, Alfons Schwarzenboeck, Guillaume Siour, Paolo Tuccella, and Christiane Voigt
Atmos. Chem. Phys., 22, 3251–3273, https://doi.org/10.5194/acp-22-3251-2022, https://doi.org/10.5194/acp-22-3251-2022, 2022
Short summary
Short summary
During the summer monsoon in West Africa, pollutants emitted in urbanized areas modify cloud cover and precipitation patterns. We analyze these patterns with the WRF-CHIMERE model, integrating the effects of aerosols on meteorology, based on the numerous observations provided by the Dynamics-Aerosol-Climate-Interactions campaign. This study adds evidence to recent findings that increased pollution levels in West Africa delay the breakup time of low-level clouds and reduce precipitation.
Laurent Menut, Bertrand Bessagnet, Régis Briant, Arineh Cholakian, Florian Couvidat, Sylvain Mailler, Romain Pennel, Guillaume Siour, Paolo Tuccella, Solène Turquety, and Myrto Valari
Geosci. Model Dev., 14, 6781–6811, https://doi.org/10.5194/gmd-14-6781-2021, https://doi.org/10.5194/gmd-14-6781-2021, 2021
Short summary
Short summary
The CHIMERE chemistry-transport model is presented in its new version, V2020r1. Many changes are proposed compared to the previous version. These include online modeling, new parameterizations for aerosols, new emissions schemes, a new parameter file format, the subgrid-scale variability of urban concentrations and new transport schemes.
Vincenzo Mazzarella, Rossella Ferretti, Errico Picciotti, and Frank Silvio Marzano
Nat. Hazards Earth Syst. Sci., 21, 2849–2865, https://doi.org/10.5194/nhess-21-2849-2021, https://doi.org/10.5194/nhess-21-2849-2021, 2021
Short summary
Short summary
Forecasting precipitation over the Mediterranean basin is still a challenge. In this context, data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of the precipitation forecast. For the first time, the ability of a cycling 4D-Var to reproduce a heavy rain event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study.
Cited articles
Alberton, M.: Water Governance in Italy: From Fragmentation to Coherence
Through Coordination Attempts, 355–368, Springer International
Publishing, Cham, https://doi.org/10.1007/978-3-030-69075-5_15, 2021. a
Appiotti, F., Krželj, M., Russo, A., Ferretti, M., Bastianini, M., and
Marincioni, F.: A multidisciplinary study on the effects of climate change in
the northern Adriatic Sea and the Marche region (central Italy),
Reg. Enviro. Change, 14, 2007–2024,
https://doi.org/10.1007/s10113-013-0451-5, 2014. a
Barlage, M., Chen, F., Tewari, M., Ikeda, K., Gochis, D., Dudhia, J.,
Rasmussen, R., Livneh, B., Ek, M., and Mitchell, K.: Noah land surface model
modifications to improve snowpack prediction in the Colorado Rocky Mountains,
J. Geophys. Res.-Atmos., 115, D22,
https://doi.org/10.1029/2009JD013470, 2010. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, 2005. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche
warning: Part I: numerical model, Cold Reg. Sci. Technol., 35,
123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a, b
Bebi, P., Kulakowski, D., and Rixen, C.: Snow avalanche disturbances in forest
ecosystems – State of research and implications for management,
Forest Ecol. Manage., 257, 1883–1892,
https://doi.org/10.1016/j.foreco.2009.01.050, 2009. a
Belda, M., Holtanová, E., Halenka, T., and Kalvova, J.: Climate classification
revisited: From Köppen to Trewartha, Clim. Res., 59, 1–13,
https://doi.org/10.3354/cr01204, 2014. a
Bellaire, S. and Jamieson, B.: Forecasting the formation of critical snow
layers using a coupled snow cover and weather model,
Cold Reg. Sci. Technol., 94, 37–44,
https://doi.org/10.1016/j.coldregions.2013.06.007, 2013. a
Bellaire, S., Jamieson, J. B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011. a, b
Bellaire, S., Jamieson, J. B., and Fierz, C.: Corrigendum to ”Forcing the snow-cover model SNOWPACK with forecasted weather data” published in The Cryosphere, 5, 1115–1125, 2011, The Cryosphere, 7, 511–513, https://doi.org/10.5194/tc-7-511-2013, 2013. a, b
Bellaire, S., van Herwijnen, A., Mitterer, C., and Schweizer, J.: On
forecasting wet-snow avalanche activity using simulated snow cover data, Cold
Reg. Sci. Technol., 144, 28–38,
https://doi.org/10.1016/j.coldregions.2017.09.013, 2017. a
Brunetti, M., Maugeri, M., and Nanni, T.: Variations of temperature and
precipitation in Italy from 1866 to 1995,
Theor. Appl. Climatol., 65, 165–174, https://doi.org/10.1007/s007040070041, 2000. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model
with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation
and Sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a, b
Chen, F., Barlage, M., Tewari, M., Rasmussen, R., Jin, J., Lettenmaier, D.,
Livneh, B., Lin, C., Miguez-Macho, G., Niu, G.-Y., Wen, L., and Yang, Z.-L.:
Modeling seasonal snowpack evolution in the complex terrain and forested
Colorado Headwaters region: A model intercomparison study,
J. Geophys. Res.-Atmos., 119, 13795–13819,
https://doi.org/10.1002/2014JD022167, 2014. a
Chiambretti, I. and Sofia, S.: Winter 2016–2017 snowfall and avalanche
emergency management in Italy (Central Apennines) – A review, in: Proceedings
of the International Snow Science Workshop, Innsbruck, Austria, 7–12, http://arc.lib.montana.edu/snow-science/item/2793 (last access: 5 February 2023), 2018. a
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W.-C., Wang, C.-B., and
Bernardini, S.: The COVID-19 pandemic,
Crc. Cr. Rev. Cl. Lab. Sc., 57, 365–388, https://doi.org/10.1080/10408363.2020.1783198, 2020. a
Doms, G. and Schättler, U.: A description of the nonhydrostatic regional
model LM, Part I: Dynamics and Numerics, Deutscher Wetterdienst, Offenbach, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_6.00_I, 2002. a
Erfani, A., Mailhot, J., Gravel, S., Desgagné, M., King, P., Sills, D.,
McLennan, N., and Jacob, D.: The high resolution limited area version of the
Global Environmental Multiscale model and its potential operational
applications, 11th Conference on Mesoscale Processes, Session 1M, Mesoscale Model Development & Data Assimilation, Albuquerque, 2005. a
Fazzini, M., Cordeschi, M., Carabella, C., Paglia, G., Esposito, G., and
Miccadei, E.: Snow Avalanche Assessment in Mass Movement-Prone Areas: Results
from Climate Extremization in Relationship with Environmental Risk Reduction
in the Prati di Tivo Area (Gran Sasso Massif, Central Italy), Land, 10, 1176, https://doi.org/10.3390/land10111176, 2021. a
Frigo, B., Bartelt, P., Chiaia, B., Chiambretti, I., and Maggioni, M.: A
Reverse Dynamical Investigation of the Catastrophic Wood-Snow Avalanche of 18
January 2017 at Rigopiano, Gran Sasso National Park, Italy,
Int. J. Disast. Risk. Sc., 12, 40–55, 2021. a
Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, 2015. a
Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, 2018. a
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014. a
Hall, A.: The Role of Surface Albedo Feedback in Climate, J. Climate,
17, 1550–1568, https://doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2,
2004. a
Horton, S. and Haegeli, P.: Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting, The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, 2022. a
Horton, S. and Jamieson, B.: Modelling hazardous surface hoar layers across
western Canada with a coupled weather and snow cover model, Cold Reg. Sci. Technol., 128, 22–31,
https://doi.org/10.1016/j.coldregions.2016.05.002, 2016. a
Horton, S., Schirmer, M., and Jamieson, B.: Meteorological, elevation, and slope effects on surface hoar formation, The Cryosphere, 9, 1523–1533, https://doi.org/10.5194/tc-9-1523-2015, 2015. a
Hou, Y., Huang, X., and Zhao, L.: Point-to-Surface Upscaling Algorithms for
Snow Depth Ground Observations, Remote Sens., 14, 4840, https://doi.org/10.3390/rs14194840,
2022. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13, https://doi.org/10.1029/2008JD009944, 2008. a
Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M.,
Barlage, M., Dudhia, J., Miller, K., Arsenault, K., Grubišić, V., Thompson,
G., and Guttman, E.: Simulation of seasonal snowfall over Colorado,
Atmos. Res., 97, 462–477,
https://doi.org/10.1016/j.atmosres.2010.04.010, 2010. a
ISPRA: Valori climatici normali di temperature e precipitazione in Italia,
Stato dell’ambiente 55/2014,
http://www.scia.isprambiente.it/wwwrootscia/Documentazione/rapporto_Valori_normali_def.pdf (last access: 2 February 2023),
2015. a
Italian Civil Protection Department and CIMA Research Foundation: The Dewetra
Platform: A Multi-perspective Architecture for Risk Management during
Emergencies, in: Information Systems for Crisis Response and Management in
Mediterranean Countries, edited by: Hanachi, C., Bénaben, F., and Charoy,
F., 165–177, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-11818-5_15, 2014. a
Köppen, W.: Grundriss der klimakunde, Walter de Gruyter GmbH & Co KG,
1931. a
Koren, V., Schaake, J., Mitchell, K., Duan, Q.-Y., Chen, F., and Baker, J. M.:
A parameterization of snowpack and frozen ground intended for NCEP weather
and climate models, J. Geophys. Res.-Atmos., 104,
19569–19585, https://doi.org/10.1029/1999JD900232, 1999. a
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P., Redelsperger, J. L., Richard, E., and Vilà-Guerau de Arellano, J.: The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations, Ann. Geophys., 16, 90–109, https://doi.org/10.1007/s00585-997-0090-6, 1998. a
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model
for the Swiss avalanche warning: Part III: meteorological forcing, thin layer
formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184,
https://doi.org/10.1016/S0165-232X(02)00072-1, 2002a. a, b
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical
SNOWPACK model for the Swiss avalanche warning: Part II. Snow
microstructure, Cold Reg. Sci. Technol., 35, 147–167,
https://doi.org/10.1016/S0165-232X(02)00073-3, 2002b. a, b
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli, M., and
Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its
application to snow hydrology, Hydrol. Process., 20, 2111–2128,
https://doi.org/10.1002/hyp.6204, 2006. a, b
Lena, B., Antenucci, F., and Mariani, L.: Space and time evolution of the
Abruzzo precipitation, Ital. J. Agrometeorol., 17, 5–20, 2012. a
Libertino, A., Ganora, D., and Claps, P.: Technical note: Space–time analysis of rainfall extremes in Italy: clues from a reconciled dataset, Hydrol. Earth Syst. Sci., 22, 2705–2715, https://doi.org/10.5194/hess-22-2705-2018, 2018. a
Livneh, B., Xia, Y., Mitchell, K. E., Ek, M. B., and Lettenmaier, D. P.: Noah
LSM Snow Model Diagnostics and Enhancements, J. Hydrometeorol., 11,
721–738, https://doi.org/10.1175/2009JHM1174.1, 2010. a
Longobardi, A. and Villani, P.: Trend analysis of annual and seasonal rainfall
time series in the Mediterranean area, Int. J. Climatol.,
30, 1538–1546, https://doi.org/10.1002/joc.2001, 2010. a
Luijting, H., Vikhamar-Schuler, D., Aspelien, T., Bakketun, Å., and Homleid, M.: Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway, The Cryosphere, 12, 2123–2145, https://doi.org/10.5194/tc-12-2123-2018, 2018. a, b
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018a. a
Lussana, C., Tveito, O. E., and Uboldi, F.: Three-dimensional spatial
interpolation of 2 m temperature over Norway, Q. J. Roy. Meteor. Soc., 144, 344–364,
https://doi.org/10.1002/qj.3208, 2018b. a
Mailhot, J., Bélair, S., Lefaivre, L., Bilodeau, B., Desgagné, M., Girard,
C., Glazer, A., Leduc, A., Méthot, A., Patoine, A., Plante, A., Rahill, A.,
Robinson, T., Talbot, D., Tremblay, A., Vaillancourt, P., Zadra, A., and
Qaddouri, A.: The 15‐km version of the Canadian regional forecast system,
Atmos. Ocean, 44, 133–149, https://doi.org/10.3137/ao.440202, 2006. a
Marsh, C. B., Pomeroy, J. W., Spiteri, R. J., and Wheater, H. S.: A Finite
Volume Blowing Snow Model for Use With Variable Resolution Meshes, Water Resour. Res., 56, e2019WR025307,
https://doi.org/10.1029/2019WR025307,
2020a. a
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview, Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020, 2020b. a
Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., and
Böttcher, K.: An optical reflectance model-based method for fractional snow
cover mapping applicable to continental scale, Remote Sens. Environ.,
123, 508–521, https://doi.org/10.1016/j.rse.2012.04.010, 2012. a
Milbrandt, J. A., Bélair, S., Faucher, M., Vallée, M., Carrera, M. L., and
Glazer, A.: The Pan-Canadian High Resolution (2.5 km) Deterministic
Prediction System, Weather Forecast., 31, 1791–1816,
https://doi.org/10.1175/WAF-D-16-0035.1, 2016. a
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics:
Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6,
https://doi.org/10.3389/feart.2018.00197, 2018. a
Müller, M., Homleid, M., Ivarsson, K.-I., Koltzow, M. A. O., Lindskog, M.,
Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjorge, D., Dahlgren,
P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.:
AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction
Model, Weather Forecast., 32, 609–627,
https://doi.org/10.1175/WAF-D-16-0099.1, 2017. a
Nurmi, P.: Recommendations on the verification of local weather forecasts, ECMWF Technical Memoranda, 430, 19 pp., https://doi.org/10.21957/y1z1thg5l, 2003. a
Pavan, V., Tomozeiu, R., Cacciamani, C., and Di Lorenzo, M.: Daily
precipitation observations over Emilia-Romagna: mean values and extremes,
Int. J. Climatol., 28, 2065–2079,
https://doi.org/10.1002/joc.1694, 2008. a
Pavelsky, T. M., Kapnick, S., and Hall, A.: Accumulation and melt dynamics of
snowpack from a multiresolution regional climate model in the central Sierra
Nevada, California, J. Geophys. Res.-Atmos., 116, D16, https://doi.org/10.1029/2010JD015479, 2011. a, b
Petriccione, B. and Bricca, A.: Thirty years of ecological research at the Gran
Sasso d’Italia LTER site: Climate change in action, Nature Conserv.,
34, 9–39, https://doi.org/10.3897/natureconservation.34.30218, 2019. a
Piacentini, T., Calista, M., Crescenti, U., Miccadei, E., and Sciarra, N.:
Seismically Induced Snow Avalanches: The Central Italy Case, Front. Earth Sci., 8, 507, https://doi.org/10.3389/feart.2020.599611, 2020. a
Pinna, M.: Contributo alla classificazione del clima d'Italia, Rivista
Geografica Italiana, 77, 129–152, 1970. a
Quéno, L., Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Dumont, M., and Karbou, F.: Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts, The Cryosphere, 10, 1571–1589, https://doi.org/10.5194/tc-10-1571-2016, 2016. a, b, c
Raparelli, E: edrap/WRF2A3D: WRF-Alpine3D offline coupling script version 0.2.0-alpha, Zenodo [code], https://doi.org/10.5281/zenodo.7543614, 2023. a
Raparelli, E. and Tuccella, P.: WRF-Noah/Alpine3D simulations for 2018–2021 snow seasons in Italian Central Apennines, Zenodo [data set], https://doi.org/10.5281/zenodo.7591394, 2023. a
Rapisarda, A. and Pranzo, A. M. R.: Mapping the avalanche risk: from survey to cartographic production. The avalanche bulletin of the Meteomont Service of the Alpine Troops Command, Proc. Int. Cartogr. Assoc., 4, 92, https://doi.org/10.5194/ica-proc-4-92-2021, 2021. a
Romano, E. and Preziosi, E.: Precipitation pattern analysis in the Tiber River
basin (central Italy) using standardized indices, Int. J. Climatol., 33, 1781–1792, https://doi.org/10.1002/joc.3549, 2013. a
Romeo, V. and Massimiliano, F.: La neve in Appennino. Prime analisi su 30 anni
di dati meteonivologici, Neve e Valanghe, 63,
https://issuu.com/aineva7/docs/nv63 (last access: 2 February 2023), 2008. a
Rossi, G.: Institutional Framework of Water Governance, 83–100, Springer
International Publishing, Cham, https://doi.org/10.1007/978-3-030-36460-1_4, 2020. a
Schirmer, M. and Jamieson, B.: Verification of analysed and forecasted winter precipitation in complex terrain, The Cryosphere, 9, 587–601, https://doi.org/10.5194/tc-9-587-2015, 2015. a, b, c
Scorzini, A. R. and Leopardi, M.: Precipitation and temperature trends over
central Italy (Abruzzo Region): 1951–2012, Theor. Appl. Climatol., 135, 959–977,
https://doi.org/10.1007/978-3-030-36460-1_4, 2019. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F.,
Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model,
Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-231, in review, 2021.
a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of
the advanced research WRF model version 3, National Center for Atmospheric
Research: Boulder, CO, USA, p. 145, https://doi.org/10.5065/D68S4MVH,
2008. a, b
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme.
Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Vanat, L.: International Report on Snow & Mountain Tourism,
https://www.vanat.ch/RM-world-report-2020.pdf (last access: 2 February 2023), 2020. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b
Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity, Y., and
Bazile, E.: Numerical Weather Forecasts at Kilometer Scale in the French
Alps: Evaluation and Application for Snowpack Modeling, J. Hydrometeorol., 17, 2591–2614, https://doi.org/10.1175/JHM-D-15-0241.1, 2016. a
Vionnet, V., Martin, E., Masson, V., Lac, C., Naaim Bouvet, F., and Guyomarc'h,
G.: High-Resolution Large Eddy Simulation of Snow Accumulation in Alpine
Terrain, J. Geophys. Res.-Atmos., 122, 11005–11021,
https://doi.org/10.1002/2017JD026947, 2017. a
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021. a
Wang, Z., Zeng, X., and Decker, M.: Improving snow processes in the Noah land
model, J. Geophys. Res.-Atmos., 115, D20,
https://doi.org/10.1029/2009JD013761, 2010. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a
Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced...