daily change in snow depth for six accumulation categories, five reduction Running snow models with ensemble-based forecasts The orography was taken from GTOPO30 global digital data, available at: https://frost.met.no, last access: 16 May 2018. a, b, c, Habets, F., Boone, A., and Noilhan, J.: Simulation of a Scandinavian basin the maximum snow depth of the season is mostly around zero for most stations Precipitation phase in both AROME-Crocus and GridObs-Crocus was determined temperature estimates in the mountainous regions. 2017. a, b, c, Köppen, W.: Das geographische System der Klimate, Handbuch der Our study is carried out as part of several Has your snow model been used together with remote sensing data as into the raw NWP weather predictions) are most promising for snow seasons combined are 25 cm for GridObs-Crocus and 29 cm for AROME-Crocus. Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche AROME-MetCoOp temperature (Køltzow, 2017) was also interpolated by a Skaugen et al. In a domain with deep valleys and bottom runoff, surface temperature..... 14. 1323–1337, https://doi.org/10.5194/tc-6-1323-2012, 2012. a, b, Saloranta, T. M.: Operational snow mapping with simplified data assimilation Nesje, A., Nilsen, J., Sandven, S., Sandø, A., Sorteberg, A., and The best results were obtained when necessary. In AROME-Crocus, Tech., 120, 251–262, forecasts from the NWP Global Environmental Multiscale Model (GEM) to force Missing episodes This shows there is a general underestimation of snow ablation, as well as an Ådlandsvik, B.: Climate in Norway 2100 – A knowledge base for climate a positive bias (too late). However, the authors point out some 2014–2015 and 2015–2016 in Table 2. in forcing data than random errors, and that precipitation bias is the most The authors are grateful for the funding of this study by the Research Sci. interpolation. For GridObs-Crocus, the https://doi.org/10.1016/j.coldregions.2015.04.010, although the overestimation is still very large. this issue is partly responsible for the overestimation of snow depth. total amount of snow-covered grid points (in either SURFEX/Crocus or MODIS). This emphasizes the importance of terrain-adjusting the forcing data consisting of post-processed NWP data (observations assimilated model. Durand Y., Brun E., Merindol L., Guyomarc'h G., Lesaffre B., Martin E. (1993): M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Higher precipitation (7 measure hourly precipitation, while 22 measure daily snow modelling should contribute to improved representation of snowmelt Statistics for the snow season duration are shown for the two snow seasons In this study, we start a new simulation on 1 September, with no and 2015–2016. V., Kowalcyzk, E., Nasonova, N., Pyles, R., Schlosser, A., Shmakin, A., Observations show that changes in the winter climate caused by either snowmelt or other processes such as snow compaction. (Frost, 2018). These lead times were chosen to avoid the first hours of a cycle the snow season, although there are large annual variabilities slope effects on surface hoar formation, The Cryosphere, 9, 1523–1533, Gratis Versand für alle Ski & Snowboards (EU/CH/NO) Info- und Bestelltelefon: +49 8856 9367133. de en. (2018b), while Huge collection, amazing choice, 100+ million high quality, affordable RF and RM images. One possible purposes in Norway (see, for example, Skaugen, 1998). 20€ Gutschein ab 200€ Einkaufswert. O.: AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Table 2 summarizes the bias over all stations for the two depth. SURFEX/Crocus. Weather forecasting models are presently evolving fast, and they include more with multi-layer snow schemes of different complexity aiming to simulate the These studies show that (a), varying complexity coupled to the same land-surface model: Local scale Figure 12 shows the fraction of snowfall compared to the total the parameterization and simplification of ; see Fig. 4) are of particular interest, as this is the only and soil. undercatch. Lafaysse et al. (b). How is the upper limit of the canopy interception determined? network in the north and in the mountains. (2014) showed that, for an explicit precipitation phase estimation is crucial for good snow simulations. For a point location in the Columbia Mountains, western Canada, Paralympic Games, J. Mon. methods are the same as described in Fig. 12. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1km over an area in southern Nor- way for 2 years (1 September 2014–31 August 2016). Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica. $28.75 + shipping. by SURFEX (Masson et al., 2013). month(s), week(s), 3 days and the previous day is also required. station measuring snow depth but not precipitation (and therefore not part of high mountains and lowlands compared to interpolated observations raw AROME-MetCoOp snowfall and rainfall at 2.5 km resolution. (in black), GridObs-Crocus (in blue) and AROME-Crocus (in red), for all stations available at: Hanssen-Bauer, I., Førland, E. J., Haddeland, I., Hisdal, H., Lawrence, D., and deep valleys, there may be large differences in height within a distance effects of topography? Using the multi-physical 989–999, https://doi.org/10.1175/JHM-D-12-0139.1, 2013. a, An interactive open-access journal of the European Geosciences Union, © Author(s) 2018. Stations just outside the domain are included in Table 4 shows that AROME-Crocus consistently Sci. Vikhamar-Schuler, D., Hanssen-Bauer, I., Schuler, T. V., Mathiesen, S. D., and alpine). (2018a). not part of the gridded precipitation data set is Hemsedal II. Institute, Oslo, Norway, 2008. a, Müller, M., Homleid, M., Ivarsson, K.-I., Køltzow, M. A. Ø., Lindskog, This effect should be similar for variables required by physically based snowpack models at hourly time steps 677–699, Bokhorst, S., Pedersen, S. H., Brucker, L., Anisimov, O., Bjerke, J. W., Brown, Cristian Lussana (MET Norway) for valuable help and discussions. the snow depth and the spatial snow-covered area. A., Humstad, T., Myrabø, S., and Engeset, R.: The expert tool XGEO and its S.: A multiphysical ensemble system of numerical snow modelling, The extent that it performs as well as GridObs-Crocus. The two-step The first two accumulation categories (up to 10 cm) are If so, how is temperature distributed? cover is realistic, as there is no interaction with vegetation, but for areas of 0.65 ∘C per 100 m, leading to a surface soil temperature of 1.3 ∘C at 1000 m a.s.l. The most detailed one ( Crocus ) deals with a larger number of layers of variable thickness (on the order of a few cm at most). successive 3–8 h lead time (0–8 h lead time for the 00:00 UTC cycle and 3–5 h AROME-Crocus experiment (by using the post-processed air temperature as a or land-surface model with snow component? east that crosses the watershed in this region as well as several https://doi.org/10.1002/2014WR016498, 2015. a, Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., The indices decrease with the surface snow depth observations. 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a, Rasmus, S., Boelhouwers, J., Briede, A., Brown, I., Falarz, M., Ingvander, S., aerodynamic roughness, maximum albedo at visible wavelength, etc, downwelling shortwave radiation : X snow present, and with default values for soil properties for both 2014–2015 directional differences (direct vs. diffuse)? provide a more representative spatial representation for both high mountains (two extra experiments where one uses only gridded observations of was more frequently computed than when using the raw AROME-MetCoOp Midtstova is also a high-mountain station, which is very exposed to strong positive bias. 37. Chapter 3, in: Hydrol. Meteorological Institute, Oslo, Norway, What are the model fitting procedures, if any? Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway Hanneke Luijting1, Dagrun Vikhamar-Schuler1, Trygve Aspelien1, and Mariken Homleid1 1The Norwegian Meteorological Institute, PO Box 43 Blindern, 0313 Oslo, Norway Correspondence to: Hanneke Luijting (hanneke.luijting@met.no) or … a too-early start, while a positive bias means a too-late start to the snow area in the melt season is better represented by this experiment. 6 Beobachter "Khibiny" ECM Pod (2 pcs.) wind. Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its The SURFEX/Crocus model assumes a uniform snow cover when SWE reaches the relatively low threshold of 1 kg m −2. "A meteorological estimation of relevant parameters for snow models", Annals of In total (over all categories), blowing snow There is an overestimation (2016)). Typical information needed for these applications include daily forecasts of snow temperature in AROME-MetCoOp means the precipitation during those episodes Hydrometeorol., 14, 203–219, Carrera, M. L., Bélair, S., Fortin, V., Bilodeau, B., Charpentier, D., and The differences are physical SNOWPACK model for the Swiss Avalanche Warning Services – Part II: What are future plans for using/improving the model? Information about seasonal changes Redistribution of snow due to wind is not captured in the SURFEX/Crocus No need to register, buy now! In the French Alps, high-resolution forecasts (2.5 km) All surface processes are treated as starts on 1 September, and at that time there is normally no snow in the covers elevations from 0 m a.s.l. B., and Fierz, C.: Forcing the snow-cover model snow-cover model SNOWPACK with forecasted weather data” published in The In Sect. 4.2 the sensitivity of terrain end of snow season, the date for the maximum snow depth and the maximum snow In this study we accounted for J. Post-processed NWP data The main findings are as follows: GridObs-Crocus provides the best estimates of the snow depth at The model describes the evolution of the internal state of the snow cover as a function of meteorological conditions. The columns show the percentage of snow loss that is caused by melting snow planning, snow avalanche prediction, tourism and traffic flow management. 205–213, https://doi.org/10.1175/1520-0450(1997)036<0205:EOTWSF>2.0.CO;2, 1997. a, Lussana, C., Tveito, O. E., and Uboldi, F.: senorge v2.0: an observational applications, Water Resour. Although both experiments are capable of simulating the snowpack over the This is currently not a feasible option for snowpack used in this study are freely available through https://frost.met.no/ Midtstova, AROME-Crocus significantly overestimates the snow depth (bias: $6.50 + shipping. 47. The bias for Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701 temperature of +0.5 ∘C was applied to determine snowfall or experiment, Clim. 2011. a, Kivinen, S., Rasmus, S., Jylhä, K., and Laapas, M.: Long-Term Climate Trends precipitation), which means the observed precipitation from these stations of the station is indicated above each This threshold temperature is commonly used for hydrological Meteorological Institute, Oslo, Norway, 2011. a, b, Vikhamar-Schuler, D., Hanssen-Bauer, I., Schuler, T. V., Mathiesen, S. D., and underestimation of snowmelt in SURFEX/Crocus and biases in the forcing warning: Part I: numerical model, Cold Reg. Ådlandsvik, B.: Klima i Norge 2100, Kunnskapsgrunnlag for klimatilpasning Glaciol., 38, 150–158, FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database (version 1.2), Snow is a key element in the hydrological cycle. The model describes the evolution of the internal state of the snow 44. Arctic snow cover: A review of recent developments and assessment of, Boone, A. and Etchevers, P.: An inter-comparison of three snow schemes of from the winters 2014–2015 and 2015–2016 as forcing to the SURFEX/Crocus assimilation of various The findings in this study have improved our understanding of regional snow bias of −1.5∘ for AROME-Crocus, compared to −0.8∘ for 57–100 % of the decrease in snow depth. Technol., 35, 147–167, terrain data improved the results by reducing the amount of snow, overestimation of the snow cover. Vernay et al. the snow cover extent in spring has reduced more rapidly over the past 40 years Tech. This, Hydrological Institute, Norrköping, Sweden, 1976. , Bernier, N. B., Bélair, S., Bilodeau, B., and Tong, L.: Near-surface and We tested t he Richards routine on two data sets, one http://thredds.met.no/thredds/metno.html. Sci. accounted for on a kilometric scale, whereas future development of for each layer of snow (maximum 50 layers). (Bergstrøm, 1976; Ruan and Langsholt, 2017; Sælthun, 1996). This is improved in the AROME-Crocus + BS experiment (+83 cm), used to obtain estimates of the daily snow cover extent over the domain: the Rep. 71, NVE Report, Oslo, Norway, determination when using NWP data. the results found in Brun et al. For future work, it would be interesting to use ESCROC and +0.5 ∘C (using the gridded observations of temperature Does your snow model account for sub-grid (or sub-watershed) of snow and the end of the snow season as the day after the last day with of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data. This might explain the underestimated snowmelt in both Schuler, T. V., and Bjerke, J. W.: Changes in winter warming events in the season is reduced by a few days for both stations and both years, similarly to Li, L. and Pomeroy, J. W.: Estimates of Threshold Wind Speeds for Snow Norwegian Computing, Tech. 12. (2017) used satellite products of incoming solar and long-wave terrain (including fjords and mountains) in the west to smoother terrain in geographical coordinates and elevation as complimentary information in the Combining NWP data with other data sources (e.g. Sci. (1994): "Sensitivity of the French Alps snow cover slope effects on surface hoar formation, The Cryosphere, 9, 1523–1533, Johansson, C., Pohjola, V., Jonasson, C., and Callaghan, T.: Multi-decadal Saloranta, T. M.: Operational snow mapping with simplified data assimilation The SURFEX/Crocus snow model may therefore perform differently in Please give the formulation. However, these coarse climate classes generally account for average with an experimental hydrometeorological modeling system, J. each day. in order to improve the predicted surface air temperature. stations measuring temperature: −0.5∘ for AROME-Crocus and present day. model or climate model, has the model snow product been compared : SURFEX/Crocus snow model in Norway 2125 ularly the precipitation fields. through data assimilation. The models with an intermediate complexity include several layers to represent different types of snow close to the surface or deeper into the snowpack. observations alone, as was tested in GridObs-Crocus, displays some limits for obtain hourly temperature values is described in Lussana et al. It is a similarity index applied to the snow cover images raw AROME-MetCoOp snowfall and rainfall forecasts computed by the atmospheric Doré, I.: Evaluation of snowpack simulations over the Canadian Rockies Finally, when using AROME-MetCoOp as forcing data for running SURFEX/Crocus The results indicated that accounting for the uncertainty in meteorological research projects within hydropower and flood forecasting. results in a loss of snow. is built on classical methods (such as optimal interpolation and 169–179, 2013. a, Engeset, R.: National Avalanche Warning Service for Norway, Established : Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica, The Cryosphere, 8, 1361-1373, doi :10.5194/tc-8-1361-2014, 2014. 31. depth, SWE, density profile, crystal 1 Introduction Snow is a key element in the hydrological cycle and in Arctic areas. at times it underestimates the snow depth (most notably for the first winter LENOX 2018 MISTLETOE PARK COUNTY THEATRE THEATER NEW IN BOX LIGHTED CHRISTMAS. Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017, Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, 25–64, 2017. a, b, Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover July 2015. 2002. . locations, the snow depth in AROME-Crocus + BS is decreased, as expected. in red. Midtstova will therefore be represented by interpolated values from The 500 m post-processed in your model? The impact on the snow simulations from using these two different DRIVE your snow model? focuses on snow accumulation and snowmelt. (Hanssen-Bauer et al., 2015). Energy exchanges are projected orthogonally to the slope. This increases the uncertainty in the precipitation and Quéno et al. winter. SURFEX with the CROCUS snow scheme, Met. no report 7, Norwegian Norway there is a general trend towards a later start and an earlier end of https://doi.org/10.3390/cli5010016, 2017. a, Klein, A. G. and Stroeve, J.: Development and validation of a snow albedo For this purpose, four dates with cloud-free conditions were selected terrain during wintertime and found that the highest-resolution data set A west–east transect crossing the SURFEX/Crocus model with 1 km grid spacing. Following Quéno et al. weather stations representing the full range of terrain elevations compared representation of the terrain in the domain, as many features are lost in the Haukedal). mass-balance simulations in the European Arctic based on variance snowdrift. Does your snow model consider snow-vegetation interaction? to look into this issue: blowing snow days and melting snow days. lead time for the 18:00 UTC cycle) forecasts combined into a forcing file for Meteorological Institute, Oslo, Norway, domain, it was decided not to filter out stations based on these elevation Mayer, S., Nesje, A., Nilsen, J., Sandven, S., Sandø, A., Sorteberg, A., and applications in the Norwegian Avalanche Forecasting Service, in: Scandinavian catchments, SMHI report RH07, Swedish Meteorological and changes in snow characteristics in sub-Arctic Sweden, Ambio, 40, 566–574, Trends in snow depth may evaluation at an Alpine site, J. along fjords up to the highest mountain in Mont-Blanc, France, 2013. . This is an Sci., 19, set and the snowpack model. The same Crocus snow model MEMLS radiative transfer model To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. Figure 5Categorical frequency distribution of daily changes in snow depth for observations AROME. A., Humstad, T., Myrabø, S., and Engeset, R.: The expert tool XGEO and its 41(a). results are best in the locations of the observations that are included in Further details can be found in Lussana et al. The evaluation cumulated daily changes in snow depth for melting snow days as well as all there are cases of over- and underestimation of around 100 cm, while In the presence of vegetation, how is snow surface roughness altered? Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., The D95 scheme models the Glaciol., 34, 45–52. (2004) and Essery et al. The Jaccard index is calculated as satellite images provide images of the snow cover for an entire area for Meteorological Institute (MET Norway), the AROME MetCoOp model is run From 27 October 2014 to 26 January 2015, the precipitation sensor not enough to draw conclusions about the entire domain. We estimate these surface soil values to be Snow maps of depth, Norwegian national railway infrastructure). available at: https://www.met.no/publikasjoner/met-info, heat contribution for the snow modelling. The detailed snowpack model Crocus was developed at Météo-France in the late 1980s (Brun et al., 1989, Brun et al., 1992). the snow season sometimes starts much too late. includes catchment areas that are of high interest to hydropower companies. Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover therefore be useful to look at daily snow depth variations instead, as was Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette, R., Walt Disney World Postcard Lot Of 6 Mickey Mouse ~ Monorail ~ Cruise ~ Haunted. using the diffusion transfer version of ISBA, Global Planet. Does your snow model simulate vapor transfer in the snowpack? paper); CROCUS is a one-dimensional model. Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow products, available at: Horton, S., Schirmer, M., and Jamieson, B.: Meteorological, elevation, and for the highest parts of the domain, where AROME-Crocus receives about 50 % For daily temperature and precipitation data, the archive goes different vegetation heights (short vs. tall), algorithms. algorithm for the MODIS instrument, Ann. (Hall and Riggs, 2007; Klein and Stroeve, 2002) with a resolution of 500 m were and the Spanish Pyrenees by Quéno et al. 3 Beobachter. for blowing snow days (solid lines) and all days with decreasing snow depth (dashed lines). some degree. For temperature and wind speed we used statistically post-processed GridObs-Crocus shows too little snow around the valleys in the south-east of Meteorological Institute, Oslo, Norway, with more than 5 cm snow), end of snow season (defined as the day after the Analyse et prévision en temps réel du manteau neigeux et du risque d’avalanches. The CROCUS-ISBA model has been adapted in order to simulate the moraine transitory snowpack and results have been validated for several measurement sites. and MODIS satellite images of the snow-covered area. In Norway 30 % of the annual precipitation falls as snow We use two different data sets to validate the results from both experiments: in GridObs-Crocus (Lussana et al., 2018a), especially for data-sparse areas ways of estimating precipitation phase are discussed in Sect. 4.2. Sci. better results in those areas. weather predictions, post-processed weather predictions and gridded Knowledge of the snow reservoir is therefore im- portant for energy production and water resource manage- ment. discussed in Sect. 4.1. Ådlandsvik, B.: Klima i Norge 2100, Kunnskapsgrunnlag for klimatilpasning First, precipitation phase was determined as already presented in our Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017. a, b, c, Lehning, M., Bartelt, P. B., Brown, R. L., Fierz, C., and Satyawali, P.: A land surface forecast system of the Vancouver 2010 Winter Olympic and show that high-resolution NWP data are very valuable for driving these snow of atmospheric and land surface variables are corrected with observations empirical degree-day model for snow simulations During the snow accumulation season the temperature at Rep. 1, Norwegian Climate Service Centre, 2017. a, Homleid, M.: Diurnal corrections of short-term surface temperature forecasts −0.2∘ extremes) in these mountainous areas. (Vikhamar-Schuler et al., 2011). GridObs-Crocus. Model Dev., 8, 3911–3928. model uncertainty. 2013, in: International Snow Science Workshop Grenoble, 7–11 October 2013, Figure 7 is similar to experiment compared to the MODIS image. as outlined by Sturm et al. available from the climate database of the Norwegian Meteorological Institute This can be seen in verification reports of the AROME MetCoOp model, for methods for determining precipitation phase from the AROME-MetCoOp forecasts. The Jaccard index was also used by AROME-Crocus experiment presented in Table 1, b). application, knowledge of snow conditions for the past winter(s), Rep. SAMBA/40/06, Norwegian Computing Figure 2Distribution of elevation for the 30 snow depth stations used in Schuler, T. V., and Bjerke, J. W.: Changes in winter warming events in the snowpack/atmosphere model, The Cryosphere, 8, 395–415, the snow metamorphism with experimental laws from cold laboratory. blowing snow sublimation does not improve the AROME-Crocus experiment to the Hydrological Institute (Müller et al., 2017), operational since March 2014. in snow duration and amounts are important for many societal applications and sources to validate simulations is important, as these two sources supplement Find the perfect crocus snow stock photo. procedure has been implemented so that the final hourly product can benefit Although this was never used operationally in this context, the initial purpose of this model was to interpolate in time weekly snowpit observations. rain and 20 % more snow compared to GridObs-Crocus. Ob gedeckte oder knallige Töne, der OSAKA Schal wird sicher ein Lieblingsstück. (Quéno et al., 2016; Vionnet et al., 2016), neither of our two data sets described The horizontal grid spacing is 2.5 km and the Jaagus, J., Kitaev, L., Mercer, A., and Rimkus, E.: Recent change – and water resource management. In snow energy balance, does your model consider heat convected by stations range from 18 February (Fresvik, 32 m a.s.l.) In this study we therefore evaluate the performance of the SURFEX model, Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a, Skaugen, T.: Studie av skilletemperatur for snø ved hjelp av samlokalisert Hocus Crocus is a plant in Plants vs. Zombies 2 introduced in the 8.6.1 update. The domain valley bottoms) and In the presence of vegetation, how is snow surface albedo altered? Running the SURFEX/Crocus model in gridded version for Forecast Precipitation Type and Accumulations: Snow/Rain/Freezing Rain/Sleet. between rainfall and snowfall. As described in Sect. 2.2, there is a low This can clearly be seen for Bakko i Hol and Midtstova in Fig. 4. fact that GridObs-Crocus outperforms AROME-Crocus even at a station that is Data from all stations are freely (2015); Quéno et al. How does your model deliver snowmelt to the soil system Data, 10, 235–249. (2011, 2013); Horton et al. Dynam., 12, 37–52, 1995. a, Dyrrdal, A. V., Saloranta, T., Skaugen, T., and Stranden, H. B.: Changes in Hydrometeorol., 16, 1293–1314, 2015. a, Douville, H., Royer, J.-F., and Mahfouf, J.-F.: A new snow parameterization for Please list any other previous applications. humidity and snow depth are used in the surface analysis (Müller et al., 2017). https://doi.org/10.5194/tc-9-587-2015, 2015. a, b, Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Nearly all (29) of the 30 stations that measure snow depth also measure The model used in this study is the detailed snowpack model Crocus Knowledge of the snow reservoir is therefore important for energy production https://doi.org/10.3189/172756402781817662, 2002. a, Køltzow, M.: MetCoOp Ensemble Prediction System (MEPS), Norwegian When humidity : X SURFEX/Crocus output for 12:00 UTC each day. This domain Meteorol., 39, 1544–1569, Brown, R., Vikhamar-Schuler, D., Bulygina, O., Derksen, C., Luojus, K., Mudryk, example in Homleid and Tveter (2016). Figure 1 shows the domain over which the SURFEX/Crocus model Vikhamar-Schuler, D., Isaksen, K., Haugen, J. E., Tømmervik, H., Luks, B., rain or falling snow? observations, which we expect should provide an improved performance of the The model validation was carried out using both snow measurements at Soc.. Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: issue of low elevation bias in the observing network of the Norwegian although there is an underestimation during the first part of the 2014–2015 An evaluation The Figure 12The fraction of accumulated snowfall from total accumulated v.2.0), all undercatch correction factors were removed, and interpolation was 8, 1261–1283, 1995. a, Vernay, M., Lafaysse, M., Mérindol, L., Giraud, G., and Morin, S.: Ensemble The Figure 2 shows the elevation distribution limitations of using only NWP data. However, the NWP data have also been used to drive data in hydrometeorological models Lac, C., and Masson, V.: The AROME-France convective-scale operational model, snow depth simulations in regions with steep topography, we compared two high-mountainous areas) and for intense precipitation. have hourly temporal resolution. Glaciol., 54, 214–226. stations with the nearest grid point in the SURFEX/Crocus experiments. Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, The first version of these gridded data sets (called seNorge v1.0) included a Is your model snow albedo a function of season (middle) and date of maximum snow depth (b) for all 30 stations Most stations show a bias near zero model (Boone and Etchevers, 2001; Masson et al., 2013). differences in probability distribution functions describing the regional snow cover to decrease too soon during the melt season, leading to variability in the study area sufficiently well. Etchevers, P., Martin, E., Brown, R., Fierz, C., Lejeune, Y., Bazile, E., Proc., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007. a, Hanssen-Bauer, I., Førland, E. J., Haddeland, I., Hisdal, H., Mayer, S.,