• Arduini, G., G. Balsamo, E. Dutra, J. J. Day, I. Sandu, S. Boussetta, and T. Haiden, 2019: Impact of a multi-layer snow scheme on near-surface weather forecasts. J. Adv. Model. Earth Syst., 11, 46874710, https://doi.org/10.1029/2019MS001725.

    • Search Google Scholar
    • Export Citation
  • Armstrong, R. L., and J. D. Ives, Eds., 1976: Avalanche release and snow characteristics, San Juan Mountains, Colorado. Bureau of Reclamation Occasional Paper 19, 256 pp., https://snowstudies.org/wp-content/uploads/2020/03/OP19-AVALANCHE-RELEASE-AND-SNOW-CHARACTERISTICS-Reduced.pdf.

  • Arnould, G., I. Dombrowski-Etchevers, I. Gouttevin, and Y. Seity, 2021: Améliorer la prévision de température en montagne par des descentes d’échelle. Meteorologie, 115, 3744, https://doi.org/10.37053/lameteorologie-2021-0091.

    • Search Google Scholar
    • Export Citation
  • Becken, S., 2010: The importance of climate and weather for tourism. Land Environment and People (LEaP), 23 pp., https://researcharchive.lincoln.ac.nz/bitstream/handle/10182/2920/weather_literature_review.pdf;jsessionid=54F8CFE60F33FE0FEA9A9732C81A6487?sequence=1.

  • Bellaire, S., and B. Jamieson, 2013: Forecasting the formation of critical snow layers using a coupled snow cover and weather model. Cold Reg. Sci. Technol., 94, 3744, https://doi.org/10.1016/j.coldregions.2013.06.007.

    • Search Google Scholar
    • Export Citation
  • Bellaire, S., A. van Herwijnen, C. Mitterer, and J. Schweizer, 2017: On forecasting wet-snow avalanche activity using simulated snow cover data. Cold Reg. Sci. Technol., 144, 2838, https://doi.org/10.1016/j.coldregions.2017.09.013.

    • Search Google Scholar
    • Export Citation
  • Boone, A., and P. Etchevers, 2001: An intercomparison of three snow schemes of varying complexity coupled to the same land surface model: Local-scale evaluation at an alpine site. J. Hydrometeor., 2, 374394, https://doi.org/10.1175/1525-7541(2001)002<0374:AIOTSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boone, A., J.-C. Calvet, and J. Noilhan, 1999: Inclusion of a third soil layer in a land surface scheme using the force–restore method. J. Appl. Meteor., 38, 16111630, https://doi.org/10.1175/1520-0450(1999)038<1611:IOATSL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brousseau, P., Y. Seity, D. Ricard, and J. Léger, 2016: Improvement of the forecast of convective activity from the AROME-France system. Quart. J. Roy. Meteor. Soc., 142, 22312243, https://doi.org/10.1002/qj.2822.

    • Search Google Scholar
    • Export Citation
  • Brun, E., E. Martin, V. Simon, C. Gendre, and C. Coleou, 1989: An energy and mass model of snow cover suitable for operational avalanche forecasting. J. Glaciol., 35, 333342, https://doi.org/10.1017/S0022143000009254.

    • Search Google Scholar
    • Export Citation
  • Brun, E., P. David, M. Sudul, and G. Brunot, 1992: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. J. Glaciol., 38, 1322, https://doi.org/10.1017/S0022143000009552.

    • Search Google Scholar
    • Export Citation
  • Bubnová, R., G. Hello, P. Bénard, and J.-F. Geleyn, 1995: Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/Aladin NWP system. Mon. Wea. Rev., 123, 515535, https://doi.org/10.1175/1520-0493(1995)123<0515:IOTFEE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Caillaud, C., S. Somot, A. Alias, I. Bernard-Bouissières, Q. Fumière, O. Laurantin, Y. Seity, and V. Ducrocq, 2021: Modelling Mediterranean heavy precipitation events at climate scale: An object-oriented evaluation of the CNRM-AROME convection-permitting regional climate model. Climate Dyn., 56, 17171752, https://doi.org/10.1007/s00382-020-05558-y.

    • Search Google Scholar
    • Export Citation
  • Calaf, M., and I. Stiperski, 2019: Dependence of near-surface similarity scaling on scalewise anisotropy of atmospheric boundary layer turbulence. Geophysical Research Abstracts, Vol. 21, Abstract EGU2019-12357, https://meetingorganizer.copernicus.org/EGU2019/EGU2019-12357.pdf.

  • Cao, B., S. Gruber, and T. Zhang, 2017: REDCAPP (v1. 0): Parameterizing valley inversions in air temperature data downscaled from reanalyses. Geosci. Model Dev., 10, 29052923, https://doi.org/10.5194/gmd-10-2905-2017.

    • Search Google Scholar
    • Export Citation
  • Comola, F., J. F. Kok, J. Gaume, E. Paterna, and M. Lehning, 2017: Fragmentation of wind-blown snow crystals. Geophys. Res. Lett., 44, 41954203, https://doi.org/10.1002/2017GL073039.

    • Search Google Scholar
    • Export Citation
  • Day, J. J., G. Arduini, I. Sandu, L. Magnusson, A. Beljaars, G. Balsamo, M. Rodwell, and D. Richardson, 2020: Measuring the impact of a new snow model using surface energy budget process relationships. J. Adv. Model. Earth Syst., 12, e2020MS002144, https://doi.org/10.1029/2020MS002144.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., A. Boone, C. Delire, and J. Noilhan, 2011: Local evaluation of the interaction between soil biosphere atmosphere soil multilayer diffusion scheme using four pedotransfer functions. J. Geophys. Res., 116, D20126, https://doi.org/10.1029/2011JD016002.

    • Search Google Scholar
    • Export Citation
  • De Wekker, S. F. J., M. Kossmann, J. C. Knievel, L. Giovannini, E. D. Gutmann, and D. Zardi, 2018: Meteorological applications benefiting from an improved understanding of atmospheric exchange processes over mountains. Atmosphere, 9, 371, https://doi.org/10.3390/atmos9100371.

    • Search Google Scholar
    • Export Citation
  • Domine, F., G. Picard, S. Morin, M. Barrere, J.-B. Madore, and A. Langlois, 2019: Major issues in simulating some arctic snowpack properties using current detailed snow physics models: Consequences for the thermal regime and water budget of permafrost. J. Adv. Model. Earth Syst., 11, 3444, https://doi.org/10.1029/2018MS001445.

    • Search Google Scholar
    • Export Citation
  • Douville, H., J.-F. Royer, and J.-F. Mahfouf, 1995: A new snow parameterization for the Météo-France climate model. Climate Dyn., 12, 2135, https://doi.org/10.1007/BF00208760.

    • Search Google Scholar
    • Export Citation
  • Dozier, J., and S. G. Warren, 1982: Effect of viewing angle on the infrared brightness temperature of snow. Water Resour. Res., 18, 14241434, https://doi.org/10.1029/WR018i005p01424.

    • Search Google Scholar
    • Export Citation
  • Dujardin, J., and M. Lehning, 2022: Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning. Quart. J. Roy. Meteor. Soc., 148, 13681388, https://doi.org/10.1002/qj.4265.

    • Search Google Scholar
    • Export Citation
  • Durand, Y., G. Giraud, E. Brun, L. Mérindol, and E. Martin, 1999: A computer-based system simulating snowpack structures as a tool for regional avalanche forecasting. J. Glaciol., 45, 469484, https://doi.org/10.1017/S0022143000001337.

    • Search Google Scholar
    • Export Citation
  • Dutra, E., I. Sandu, G. Balsamo, A. Beljaars, H. Freville, E. Vignon, and E. Brun, 2015: Understanding the ECMWF winter surface temperature biases over Antarctica. ECMWF Tech. Memo. 762, 16 pp., https://www.ecmwf.int/node/15262.

  • ECMWF, 2016: IFS documentation—Cy43r1, operational implementation 22 Nov 2016, Part IV: Physical processes. ECMWF Tech. Rep., 223 pp., https://www.ecmwf.int/sites/default/files/elibrary/2016/17117-part-iv-physical-processes.pdf#subsection.3.10.3.

  • Fierz, C., and Coauthors, 2009: The international classification for seasonal snow on the ground. IHP-VII Tech. Doc. 83, 90 pp., https://unesdoc.unesco.org/ark:/48223/pf0000186462.

  • Geleyn, J.-F., 1988: Interpolation of wind, temperature and humidity values from model levels to the height of measurement. Tellus, 40A, 347351, https://doi.org/10.3402/tellusa.v40i4.11805.

    • Search Google Scholar
    • Export Citation
  • Goger, B., M. W. Rotach, A. Gohm, I. Stiperski, O. Fuhrer, and G. De Morsier, 2019: A new horizontal length scale for a three-dimensional turbulence parameterization in mesoscale atmospheric modeling over highly complex terrain. J. Appl. Meteor. Climatol., 58, 20872102, https://doi.org/10.1175/JAMC-D-18-0328.1.

    • Search Google Scholar
    • Export Citation
  • Gouttevin, I., G. Krinner, P. Ciais, J. Polcher, and C. Legout, 2012: Multi-scale validation of a new soil freezing scheme for a land-surface model with physically-based hydrology. Cryosphere, 6, 407430, https://doi.org/10.5194/tc-6-407-2012.

    • Search Google Scholar
    • Export Citation
  • Guyomarc’h, G., and Coauthors, 2019: A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.). Earth Syst. Sci. Data, 11, 5769, https://doi.org/10.5194/essd-11-57-2019.

    • Search Google Scholar
    • Export Citation
  • Haiden, T., I. Sandu, G. Balsamo, G. Arduini, and A. Beljaars, 2018: Addressing biases in near-surface forecasts. ECMWF Newsletter, No. 157, ECMWF, Reading, United Kingdom, 40, https://www.ecmwf.int/en/newsletter/157/meteorology/addressing-biases-near-surface-forecasts.

  • Harpold, A. A., M. L. Kaplan, P. Z. Klos, T. Link, J. P. McNamara, S. Rajagopal, R. Schumer, and C. M. Steele, 2017: Rain or snow: Hydrologic processes, observations, prediction, and research needs. Hydrol. Earth Syst. Sci., 21 (1), 122, https://doi.org/10.5194/hess-21-1-2017.

    • Search Google Scholar
    • Export Citation
  • Helbig, N., R. Mott, A. Van Herwijnen, A. Winstral, and T. Jonas, 2017: Parameterizing surface wind speed over complex topography. J. Geophys. Res. Atmos., 122, 651667, https://doi.org/10.1002/2016JD025593.

    • Search Google Scholar
    • Export Citation
  • Hock, R., 1999: A distributed temperature-index ice- and snowmelt model including potential direct solar radiation. J. Glaciol., 45, 101111, https://doi.org/10.3189/S0022143000003087.

    • Search Google Scholar
    • Export Citation
  • Jörg-Hess, S., N. Griessinger, and M. Zappa, 2015: Probabilistic forecasts of snow water equivalent and runoff in mountainous areas. J. Hydrometeor., 16, 21692186, https://doi.org/10.1175/JHM-D-14-0193.1.

    • Search Google Scholar
    • Export Citation
  • Kienzle, S. W., 2008: A new temperature based method to separate rain and snow. Hydrol. Processes, 22, 50675085, https://doi.org/10.1002/hyp.7131.

    • Search Google Scholar
    • Export Citation
  • Krinner, G., C. Genthon, Z.-X. Li, and P. Le Van, 1997: Studies of the Antarctic climate with a stretched-grid general circulation model. J. Geophys. Res., 102, 13 73113 745, https://doi.org/10.1029/96JD03356.

    • Search Google Scholar
    • Export Citation
  • Krinner, G., and Coauthors, 2018: ESM-SnowMIP: Assessing snow models and quantifying snow-related climate feedbacks. Geosci. Model Dev., 11, 50275049, https://doi.org/10.5194/gmd-11-5027-2018.

    • Search Google Scholar
    • Export Citation
  • Lac, C., and Coauthors, 2018: Overview of the MESO-NH model version 5.4 and its applications. Geosci. Model Dev., 11, 19291969, https://doi.org/10.5194/gmd-11-1929-2018.

    • Search Google Scholar
    • Export Citation
  • Lafaysse, M., B. Cluzet, M. Dumont, Y. Lejeune, V. Vionnet, and S. Morin, 2017: A multiphysical ensemble system of numerical snow modelling. Cryosphere, 11, 11731198, https://doi.org/10.5194/tc-11-1173-2017.

    • Search Google Scholar
    • Export Citation
  • Lapo, K. E., L. M. Hinkelman, M. S. Raleigh, and J. D. Lundquist, 2015: Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance. Water Resour. Res., 51, 16491670, https://doi.org/10.1002/2014WR016259.

    • Search Google Scholar
    • Export Citation
  • Laurent, J.-P., J.-M. Cohard, R. Biron, F. Delbart, S. Aubert, and P. Choler, 2014: FLUXALP: Un projet de développement d’une station de mesures éco-climatiques au col du Lautaret, Hautes-Alpes, France. Proc. XXVIIème Colloque de l’Association Internationale de Climatologie, Dijon, France.

    • Search Google Scholar
    • Export Citation
  • Libois, Q., 2014: Evolution des propriétés physiques de neige de surface sur le plateau Antarctique. Observations et modélisation du transfert radiatif et du métamorphisme. Ph.D. thesis, Université de Grenoble, 280 pp., https://theses.hal.science/tel-01232294/document.

  • Louis, J.-F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor., 17, 187202, https://doi.org/10.1007/BF00117978.

    • Search Google Scholar
    • Export Citation
  • Lundquist, J., M. Hughes, E. Gutmann, and S. Kapnick, 2019: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks. Bull. Amer. Meteor. Soc., 100, 24732490, https://doi.org/10.1175/BAMS-D-19-0001.1.

    • Search Google Scholar
    • Export Citation
  • Marks, D., A. Winstral, M. Reba, J. Pomeroy, and M. Kumar, 2013: An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin. Adv. Water Resour., 55, 98110, https://doi.org/10.1016/j.advwatres.2012.11.012.

    • Search Google Scholar
    • Export Citation
  • Martin, E., and Y. Lejeune, 1998: Turbulent fluxes above the snow surface. Ann. Glaciol., 26, 179183, https://doi.org/10.1017/S0260305500014774.

    • Search Google Scholar
    • Export Citation
  • Marzeion, B., and Coauthors, 2020: Partitioning the uncertainty of ensemble projections of global glacier mass change. Earth’s Future, 8, e2019EF001470, https://doi.org/10.1029/2019EF001470.

    • Search Google Scholar
    • Export Citation
  • Mascart, P., J. Noilhan, and H. Giordani, 1995: A modified parameterization of flux-profile relationships in the surface layer using different roughness length values for heat and momentum. Bound.-Layer Meteor., 72, 331344, https://doi.org/10.1007/BF00708998.

    • Search Google Scholar
    • Export Citation
  • Masson, V., and Coauthors, 2013: The SURFEXv7. 2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev., 6, 929960, https://doi.org/10.5194/gmd-6-929-2013.

    • Search Google Scholar
    • Export Citation
  • Monteiro, D., C. Caillaud, R. Samacoïts, M. Lafaysse, and S. Morin, 2022: Potential and limitations of convection-permitting CNRM-AROME climate modelling in the French Alps. Int. J. Climatol., 42, 71627185, https://doi.org/10.1002/joc.7637.

    • Search Google Scholar
    • Export Citation
  • Morin, S., and Coauthors, 2020: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future. Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910.

    • Search Google Scholar
    • Export Citation
  • Naaim-Bouvet, F., and Coauthors, 2013: Lac Blanc Pass: A natural wind-tunnel for studying drifting snow at 2700ma.s.l. Proc. Int. Snow Science Workshop 2013, Grenoble Chamonix Mont-Blanc, France, ISSW, 13321339, https://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_O4-30.pdf.

  • Noilhan, J., and J.-F. Mahfouf, 1996: The ISBA land surface parameterisation scheme. Global Planet. Change, 13, 145159, https://doi.org/10.1016/0921-8181(95)00043-7.

    • Search Google Scholar
    • Export Citation
  • Pichelli, E., and Coauthors, 2021: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution Part 2: Historical and future simulations of precipitation. Climate Dyn., 56, 35813602, https://doi.org/10.1007/s00382-021-05657-4.

    • Search Google Scholar
    • Export Citation
  • Quéno, L., V. Vionnet, I. Dombrowski-Etchevers, M. Lafaysse, M. Dumont, and F. Karbou, 2016: Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts. Cryosphere, 10, 15711589, https://doi.org/10.5194/tc-10-1571-2016.

    • Search Google Scholar
    • Export Citation
  • Quéno, L., F. Karbou, V. Vionnet, and I. Dombrowski-Etchevers, 2020: Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain. Hydrol. Earth Syst. Sci., 24, 20832104, https://doi.org/10.5194/hess-24-2083-2020.

    • Search Google Scholar
    • Export Citation
  • Raleigh, M. S., 2013: Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates. Ph.D. thesis, University of Washington, 189 pp.

  • Raleigh, M. S., B. Livneh, K. Lapo, and J. D. Lundquist, 2016: How does availability of meteorological forcing data impact physically based snowpack simulations? J. Hydrometeor., 17, 99120, https://doi.org/10.1175/JHM-D-14-0235.1.

    • Search Google Scholar
    • Export Citation
  • Réveillet, M., and Coauthors, 2018: Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin glacier (French Alps). Cryosphere, 12, 13671386, https://doi.org/10.5194/tc-12-1367-2018.

    • Search Google Scholar
    • Export Citation
  • Rontu, L., C. Wastl, and S. Niemelä, 2016: Influence of the details of topography on weather forecast—Evaluation of HARMONIE experiments in the Sochi Olympics domain over the Caucasian mountains. Front. Earth Sci., 4, 13, https://doi.org/10.3389/feart.2016.00013.

    • Search Google Scholar
    • Export Citation
  • Sabatier, T., A. Paci, C. Lac, G. Canut, Y. Largeron, and V. Masson, 2020: Semi-idealized simulations of wintertime flows and pollutant transport in an alpine valley: Origins of local circulations (part I). Quart. J. Roy. Meteor. Soc., 146, 807826, https://doi.org/10.1002/qj.3727.

    • Search Google Scholar
    • Export Citation
  • Schirmer, M., and B. Jamieson, 2015: Verification of analysed and forecasted winter precipitation in complex terrain. Cryosphere, 9, 587601, https://doi.org/10.5194/tc-9-587-2015.

    • Search Google Scholar
    • Export Citation
  • Schlögl, S., M. Lehning, K. Nishimura, H. Huwald, N. J. Cullen, and R. Mott, 2017: How do stability corrections perform in the stable boundary layer over snow? Bound.-Layer Meteor., 165, 161180, https://doi.org/10.1007/s10546-017-0262-1.

    • Search Google Scholar
    • Export Citation
  • Schweizer, J., J. Bruce Jamieson, and M. Schneebeli, 2003: Snow avalanche formation. Rev. Geophys., 41, 1016, https://doi.org/10.1029/2002RG000123.

    • Search Google Scholar
    • Export Citation
  • Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976991, https://doi.org/10.1175/2010MWR3425.1.

    • Search Google Scholar
    • Export Citation
  • Senkova, A. V., L. Rontu, and H. Savijärvi, 2007: Parametrization of orographic effects on surface radiation in HIRLAM. Tellus, 59A, 279291, https://doi.org/10.1111/j.1600-0870.2007.00235.x.

    • Search Google Scholar
    • Export Citation
  • Serafin, S., and Coauthors, 2020: Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press, 42 pp., https://www.uibk.ac.at/iup/buch_pdfs/10.1520399106-003-1.pdf.

  • Sicart, J. E., J. C. Espinoza, L. Quéno, and M. Medina, 2016: Radiative properties of clouds over a tropical Bolivian glacier: Seasonal variations and relationship with regional atmospheric circulation. Int. J. Climatol., 36, 31163128, https://doi.org/10.1002/joc.4540.

    • Search Google Scholar
    • Export Citation
  • Slater, A. G., and Coauthors, 2001: The representation of snow in land surface schemes: Results from PILPS 2 (d). J. Hydrometeor., 2, 725, https://doi.org/10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Slater, A. G., D. M. Lawrence, and C. D. Koven, 2017: Process-level model evaluation: A snow and heat transfer metric. Cryosphere, 11, 989996, https://doi.org/10.5194/tc-11-989-2017.

    • Search Google Scholar
    • Export Citation
  • Spandre, P., H. François, E. George-Marcelpoil, and S. Morin, 2016a: Panel based assessment of snow management operations in French ski resorts. J. Outdoor Recreation Tourism, 16, 2436, https://doi.org/10.1016/j.jort.2016.09.002.

    • Search Google Scholar
    • Export Citation
  • Spandre, P., S. Morin, M. Lafaysse, Y. Lejeune, H. François, and E. George-Marcelpoil, 2016b: Integration of snow management processes into a detailed snowpack model. Cold Reg. Sci. Technol., 125, 4864, https://doi.org/10.1016/j.coldregions.2016.01.002.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 2012: An Introduction to Boundary Layer Meteorology. Atmospheric and Oceanographic Sciences Library, Vol. 13. Springer, 670 pp.

  • Torma, C., F. Giorgi, and E. Coppola, 2015: Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps. J. Geophys. Res. Atmos., 120, 39573972, https://doi.org/10.1002/2014JD022781.

    • Search Google Scholar
    • Export Citation
  • Verseghy, D. L., 1991: CLASS—A Canadian land surface scheme for GCMS. I. Soil model. Int. J. Climatol., 11, 111133, https://doi.org/10.1002/joc.3370110202.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., E. Brun, S. Morin, A. Boone, S. Faroux, P. Le Moigne, E. Martin, and J. Willemet, 2012: The detailed snowpack scheme crocus and its implementation in SURFEX v7. 2. Geosci. Model Dev., 5, 773791, https://doi.org/10.5194/gmd-5-773-2012.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., G. Guyomarc’h, F. N. Bouvet, E. Martin, Y. Durand, H. Bellot, C. Bel, and P. Puglièse, 2013: Occurrence of blowing snow events at an alpine site over a 10-year period: Observations and modelling. Adv. Water Resour., 55, 5363, https://doi.org/10.1016/j.advwatres.2012.05.004.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., I. Dombrowski-Etchevers, M. Lafaysse, L. Quéno, Y. Seity, and E. Bazile, 2016: Numerical weather forecasts at kilometer scale in the French Alps: Evaluation and application for snowpack modeling. J. Hydrometeor., 17, 25912614, https://doi.org/10.1175/JHM-D-15-0241.1.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., and Coauthors, 2019: Sub-kilometer precipitation datasets for snowpack and glacier modeling in alpine terrain. Front. Earth Sci., 7, 182, https://doi.org/10.3389/feart.2019.00182.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., V. Fortin, E. Gaborit, G. Roy, M. Abrahamowicz, N. Gasset, and J. W. Pomeroy, 2020: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins. Hydrol. Earth Syst. Sci., 24, 21412165, https://doi.org/10.5194/hess-24-2141-2020.

    • Search Google Scholar
    • Export Citation
  • Vionnet, V., C. B. Marsh, B. Menounos, S. Gascoin, N. E. Wayand, J. Shea, K. Mukherjee, and J. W. Pomeroy, 2021: Multi-scale snowdrift-permitting modelling of mountain snowpack. Cryosphere, 15, 743769, https://doi.org/10.5194/tc-15-743-2021.

    • Search Google Scholar
    • Export Citation
  • Winstral, A., T. Jonas, and N. Helbig, 2017: Statistical downscaling of gridded wind speed data using local topography. J. Hydrometeor., 18, 335348, https://doi.org/10.1175/JHM-D-16-0054.1.

    • Search Google Scholar
    • Export Citation
  • Winstral, A., J. Magnusson, M. Schirmer, and T. Jonas, 2019: The bias-detecting ensemble: A new and efficient technique for dynamically incorporating observations into physics-based, multilayer snow models. Water Resour. Res., 55, 613631, https://doi.org/10.1029/2018WR024521.

    • Search Google Scholar
    • Export Citation
  • Yen, Y.-C., K. Cheng, and S. Fukusako, 1991: A review of intrinsic thermophysical properties of snow, ice, sea ice, and frost. North. Eng., 24, 5374.

    • Search Google Scholar
    • Export Citation
  • Zamo, M., L. Bel, O. Mestre, and J. Stein, 2016: Improved gridded wind speed forecasts by statistical postprocessing of numerical models with block regression. Wea. Forecasting, 31, 19291945, https://doi.org/10.1175/WAF-D-16-0052.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 351 351 30
Full Text Views 173 173 10
PDF Downloads 166 166 15

To the Origin of a Wintertime Screen-Level Temperature Bias at High Altitude in a Kilometric NWP Model

Isabelle GouttevinaUniversité Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France

Search for other papers by Isabelle Gouttevin in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-1801-684X
,
Vincent VionnetbEnvironmental Numerical Prediction Research, Environment and Climate Change Canada, Dorval, Quebec, Canada

Search for other papers by Vincent Vionnet in
Current site
Google Scholar
PubMed
Close
,
Yann SeitycCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Yann Seity in
Current site
Google Scholar
PubMed
Close
,
Aaron BoonecCNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

Search for other papers by Aaron Boone in
Current site
Google Scholar
PubMed
Close
,
Matthieu LafaysseaUniversité Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France

Search for other papers by Matthieu Lafaysse in
Current site
Google Scholar
PubMed
Close
,
Yannick DeliotaUniversité Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France

Search for other papers by Yannick Deliot in
Current site
Google Scholar
PubMed
Close
, and
Hugo MerzisenaUniversité Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France

Search for other papers by Hugo Merzisen in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high-altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high-altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme, and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67% of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high-altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Isabelle Gouttevin, isabelle.gouttevin@meteo.fr

Abstract

High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high-altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high-altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme, and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67% of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high-altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Isabelle Gouttevin, isabelle.gouttevin@meteo.fr

Supplementary Materials

    • Supplemental Materials (PDF 544 KB)
Save