SIMULATING SNOW CONDITIONS IN SKI RESORTS WITH THE PHYSICALLY BASEDSNOWPACK MODELS AMUNDSEN, CROCUS, AND SNOWPACK/ALPINE3D

Florian Hanzer1,2,∗, Carlo Maria Carmagnola3, Pirmin Philipp Ebner4, Mathias Bavay4, Matthieu Lafaysse3,Michael Lehning4,5, Ulrich Strasser1, Hugues Francois6, Samuel Morin3

1Department of Geography, University of Innsbruck, Austria2Wegener Center for Climate and Global Change, University of Graz, Austria

3Univ. Grenoble Alpes, Universite de Toulouse, Meteo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France4WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

5School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland6Univ. Grenoble Alpes, Irstea, LESSEM, Grenoble, France

ABSTRACT: Physically based snowpack models are commonly used for the simulation of the snow cover inmountain regions in a high level of detail and process representation. More recently, those models have beenadapted to simulate natural and technical snow in ski resorts. Adaptation has been necessary to represent thespecific properties of technical snow and snow management practices. This includes the physical descriptionof the snowmaking and grooming processes under consideration of the ski resort infrastructure (snow gunlocations and efficiency, water availability, pumping capacity, etc.), but also the associated socioeconomic de-cisions (when and where to produce snow and to groom). In the frame of the H2020 project PROSNOW, thesnowpack models AMUNDSEN, Crocus, and SNOWPACK/Alpine3D will be applied in eight pilot ski resortsacross the European Alps for forecasting snow conditions in time scales from days to several months ahead.In our contribution, we present an overview and comparison of these three models including the individualapproaches and recent developments for the simulation of natural snow conditions, snowmaking and groom-ing. We show how individual ski resorts, their infrastructure, and management practices are represented inthe models, as well as the approaches for their operational application.

Keywords: snow management, ski resorts, snowpack modeling

1. INTRODUCTION

Most ski resorts in the Alps nowadays are equippedwith snowmaking facilities in order to ensure snowreliability during the winter and increase the lengthof the ski season. While it is increasingly com-mon to employ monitoring techniques such as GPS-equipped grooming devices tracking the snow depthon the slopes, usually information about the futureevolution of the snowpack is lacking. Given the ap-propriate initial conditions and meteorological fore-casts, this information can be provided by numeri-cal snowpack models accounting for snow manage-ment practices, i. e., the physical descriptions of thesnowmaking and grooming processes and the as-sociated socioeconomic decisions. In the past fewyears much progress has been made in this re-gard, most notably by the studies by Hanzer et al.(2014) and Spandre et al. (2016, 2017) who inte-grated snow management into the physically basedsnowpack models AMUNDSEN (Strasser, 2008)and Crocus (Vionnet et al., 2012). Recently, snow

∗Corresponding author address:Florian HanzerDepartment of Geography, University of InnsbruckInnrain 52f, 6020 Innsbruck, Austriaemail: florian.hanzer@uibk.ac.at

management practices have also been integratedinto the SNOWPACK (Bartelt and Lehning, 2002)and Alpine3D (Lehning et al., 2006) models.

These models are currently being applied in theframe of the H2020 PROSNOW project (Morin et al.,2018), where a demonstrator of a meteorologicaland snow prediction system for time scales rangingbetween several days and several months ahead isbeing developed. For eight pilot ski resorts acrossthe Alps, state-of-the-art meteorological and climateforecasts will be used to feed AMUNDSEN, Cro-cus and SNOWPACK/Alpine3D and deliver infor-mation of the future snowpack evolution dependingon both the meteorological forecasts and possiblesnow management strategies.

As the three snowpack models have differentbackgrounds and original intended purposes (e. g.,hydrology vs. avalanche forecasting), they differ intheir design and internals (e. g., in terms of onedi-mensional vs. spatially distributed application, rep-resentation of snowpack layering and microstruc-ture, regionalization of meteorological variables).Here, after a short general introduction to the threemodels, we present an overview and comparison ofthe current state of integration of snow managementand the available parameters allowing to adapt themodels to resort-specific management practices. Fi-

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nally, we describe the operational workflow of calcu-lating the snowpack forecasts within PROSNOW interms of the spatial clustering of ski resorts and thescenarios of management configurations that will beshared by the models.

2. MODELS

In the following, we describe the approaches forintegrating snow management in the AMUNDSEN,Crocus, and SNOWPACK/Alpine3D models and therelated model parameters. While a more generalmodel description would be out of scope for thispaper, an overview of the most important modelspecifics including a list of references for further in-formation can be found in Table 1.

2.1. Snow production

The production of snow in ski resorts is influencedby several factors, most importantly (i) snow de-mand (i. e., if there is a need for producing snowat a given location within the resort), (ii) adequateambient conditions (cold and dry enough air allow-ing to produce snow, low wind speeds for avoidingblowing snow losses), and (iii) ski resort infrastruc-ture and availability of resources (e. g., number andefficiency of snow guns, water availability, pumpingcapacity). In the following we describe the generalworkflow and implementation of snowmaking in thethree snowpack models and how these factors areaccounted for by discussing the snow production re-lated parameters in the models. These parameters,listed in Table 2, allow to adjust the simulation ofsnow production according to the infrastructure andsnowmaking practices of individual ski resorts. Aflowchart of the core snow production procedure asdescribed below is shown in Figure 1. For AMUND-SEN and Crocus, the described functionality is –with the exception of some new developments –mostly identical to the more detailed presentationsin Hanzer et al. (2014) and Spandre et al. (2016,2017).

• Four parameters determine the calculation of(i), snow demand, i. e., when and where inthe ski resort snow should be produced: theproduction period (PP), production time (PT),production threshold (CT), and snow threshold(ST).

PP defines the period (in the form of a startand end date) in which snowmaking is gener-ally possible. Similarly, PT defines the period(in the form of a start and end time) within eachday of PP in which snowmaking is possible.Usually, several subperiods during the seasoncorresponding to different management strate-gies will need to be considered. Commonly,

the season is divided into a base-layer snow-making period prior to the opening of the re-sort where snow is produced whenever possi-ble depending on the ambient conditions and areinforcement snowmaking period afterwards,where snow is produced more selectively de-pending on demand and only during timeswhen no skiers are on the slopes.

In addition to the current date and time, the de-cision if snow should be produced is also in-fluenced by the amount of snow already pro-duced and by the current snow conditions onthe slopes. In the models this is accounted forby the production threshold (CT) and the snowthreshold (ST) parameters.

CT allows to specify a certain target waterconsumption volume which should be met be-fore production is stopped, whereas ST allowsto specify that production should be stoppedwhenever the snow depth on the slopes (i. e.,the combination of natural and machine madesnow) exceeds a certain value. Again, bothof these values can vary depending on PP.Commonly, during the base-layer snowmakingperiod a certain production threshold is setdepending on the available water resources,while during the reinforcement period the snowthreshold is used to produce snow only whensnow depth on the slopes is below a criticalthreshold.

• With regard to (ii), ambient conditions, botha wet-bulb temperature threshold (TT) and awind speed threshold (WT) can be set in orderto account for conditions where snowmakingis inefficient (marginal temperatures, too highwind speeds) or not possible at all. If at leastone these thresholds is exceeded for a givenlocation, no snow is produced there.

• Finally, if all conditions according to (i) and (ii)are fulfilled, the amount of snow that is actu-ally produced is determined according to (iii),the ski resort infrastructure and available re-sources.

The production rate PR (m3 h−1), i. e., theamount of water that can be converted intosnow for a given snow gun and time step, isassumed to be a linear function of the wet-bulbtemperature Tw (◦C) at the snow gun location:

PR = aTw + b (1)

Total snow production volumes for the entireski resort are calculated differently in the threemodels. In AMUNDSEN and Alpine3D, beingset up as fully spatially distributed models, thenumber of snow guns per ski slope needs to

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Table 1: Overview of the structure and input data of the AMUNDSEN, Crocus, and SNOWPACK/Alpine3D snowpack models. Meteoro-logical variables are: air temperature (T), total/solid/liquid precipitation (P/Ps /Pr ), relative humidity (RH), shortwave/longwave radiation(Rs /Rl ), and wind speed (WS).

AMUNDSEN Crocus SNOWPACK/Alpine3D

Key reference(s) Strasser (2008) Vionnet et al. (2012)Bartelt and Lehning (2002);Lehning et al. (2006)

Spatial scale Distributed Point scalePoint scale (SNOWPACK) /Distributed (Alpine3D)

Vertical snowpack discretiza-tion

2–4 bulk layers Multi-layer Multi-layer

Temporal resolution 1–3 h 1 h 30 min–24 hMeteorological input data T, P, RH, Rs , WS T, Ps , Pr , RH, Rs , Rl , WS T, P, RH, Rs , Rl , WS

Meteorological preprocessing Built-in SAFRAN (Durand et al., 1993)MeteoIO (Bavay and Egger,2014)

be supplied using the NG parameter. Eachslope is then divided into segments of equalarea according to the number of snow guns.The model snow guns are placed in the centerof each segment and produce snow accordingto the meteorological conditions at these spe-cific locations. In Crocus on the other hand,the snowmaking module is designed to be ap-plied at the point scale: snow production vol-umes are calculated according to the meteo-rological conditions at the simulation point andthen scaled according to the snow spreadingsurface parameter (SS), which defines the sur-face area covered a snow gun.

In AMUNDSEN and Alpine3D, additional snow-making infrastructure specifics can be incor-porated using the water availability (WA), refillrate (RR), and water flow threshold (FT) pa-rameters. WA defines the total water availabil-ity for snowmaking at the start of the season,which is then reduced during the season ac-cording to the snow guns’ water consumptionand increased according to RR in each timestep. If WA = 0, snow production is stopped.The water flow threshold (FT) parameter on theother hand allows to specify that the total waterthroughput for the entire resort is limited (as de-termined by the pumping and piping infrastruc-ture) – if simulated potential production ratesexceed this value, production for each snowgun is limited accordingly.

In practice, parts of the water volumes exiting thesnow guns according to Equation (1) do not reachthe ground of the ski slopes in the form of snow dueto both thermodynamic (evaporation and sublima-tion) and mechanical (wind-driven redistribution) ef-fects. While these water losses can be significanteven under ideal conditions (Spandre et al., 2017),simulating them using physical formulations is chal-lenging except for simple estimations of the lossesdue to thermodynamic effects such as applied inHanzer et al. (2014). Hence, all three models cur-rently assume a fixed water loss ratio as defined by

the WL parameter.The density of freshly produced technical snow,

ρmm, is modeled as a function of the wet-bulb tem-perature Tw (◦C) in SNOWPACK/Alpine3D:

ρmm = 1.7261T2w + 37.484Tw + 605.05. (2)

In AMUNDSEN and Crocus, ρmm is a fixed param-eter value. Similar parameters are implemented inCrocus and SNOWPACK/Alpine3D for the specificsurface area (SSAmm) and sphericity (Smm).

2.2. Grooming

While in practice grooming in ski resorts is per-formed both in order to redistribute and to compactsnow on the slopes, the former is not accounted forby the models, i. e., no explicit transport of snow onthe slopes due to grooming takes place. The under-lying assumptions are that freshly produced techni-cal snow is immediately distributed evenly over therespective slope surface area, and that the entiresnow that is transported downwards due to skiersand wind is later moved back to its original locationby the groomers at least daily. The effects of groom-ing on snow properties (most importantly density)are however explicitly accounted for. Several param-eters, listed in Table 3, allow to adjust the scheduleand impacts of grooming in the individual models asdescribed below.

Similar to the simulation of snow production,the period and timing of grooming is controlledby the grooming period (GP) and grooming time(GT) parameters, and grooming is only performedwhere SWE (Crocus) or snow depth (SNOW-PACK/Alpine3D) is above a certain threshold (GH).

In Crocus, the densification of the snowpackdue to the weight of the groomer is calculated byapplying a static stress of 5 kPa to the topmost50 kg m−2) of snow, then linearly decreasing to 0 kPaat 150 kg m−2 of snow. Additional effects due to thetiller mounted to the groomer are applied to the partsof the snowpack specified by the PD parameter (the

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Table 2: Adjustable snow production related parameters as implemented in the three models. The last three columns indicate whetherthe respective parameter is implemented in AMUNDSEN (A), Crocus (C), or SNOWPACK/Alpine3D (S).

Parameter Symbol Unit Function of Description A C S

Snow demand

Production period PPDaterange

Start and end date for snowmaking × × ×

Production time PTTimerange

PPDaily start and end time for snowmaking during theproduction period

× × ×

Production threshold CT kg m−2 PPWater consumption threshold (SWE equivalent) forstopping production

× × –

Snow threshold ST cm PP Snow depth threshold for stopping production × × ×Ambient conditions

Temperature thresh-old

TT ◦C Snow guntype

Wet-bulb temperature threshold for snowmaking × × ×Wind threshold WT m s−1 Wind speed threshold for snowmaking × × ×

Ski resort infrastructure and available resources

Number of snow guns NG Slope Number of snow guns for each ski slope × – ×Snow spreading sur-face

SS m2 Surface area covered by a snow gun – × –

Production rate PR m3 h−1 Snow guntype

Water flow rate for a single snow gun × × ×Water availability WA m3 Total water volume available for snowmaking × – ×Refill rate RR m3 h−1 Water refill rate × – ×Water flow threshold FT m3 h−1 Maximum total water flow × – ×

Snow properties

Water losses WLFraction of water lost due to thermodynamic andmechanical effects

× × ×Density ρmm kg m−3 Density of machine-made snow × × –1

SSA SSAmm m2 kg−1 Specific surface area of machine-made snow – × ×Sphericity Smm % Sphericity of machine-made snow – × ×

1Density is parameterized according to Equation (2)

topmost 35 kg m−2 by default): densification is pa-rameterized as

ρgroomed = max{ρav,

2ρav + 3ρt

5

}, (3)

where ρav is the weighted average density of im-pacted layers before grooming and ρt is the targetdensity that should eventually be reached by groom-ing (Spandre et al., 2016). Sphericity and SSA arealtered analogously using the respective target val-ues St and SSAt .

In SNOWPACK/Alpine3D, densification of the top-most layers of the snowpack as specified by the PDparameter is calculated as

ρgroomed = 12.152(448.78 − ρ)0.5 + 0.9963ρ− 35.41.(4)

In AMUNDSEN, the bulk snowpack density is al-tered by adapting the parameters of the snow densi-fication parameterization during grooming hours asdescribed in Hanzer et al. (2014).

3. OPERATIONAL WORKFLOW

3.1. Spatial clustering

As in PROSNOW three different models are appliedfor a range of ski resorts operationally, it is neces-sary to agree on a common understanding of theway ski resorts are represented geographically, bothfor technical reasons and for providing information tothe stakeholders in a unified way. While this issue isstill part of ongoing discussions at the current stageof the project, one currently proposed approach isthat each ski resort is divided into a number of so-called ski resort reference units (SRUs), similar tothe hydrological response units (HRUs) commonlyused in hydrological modeling.

The delineation of SRUs for a given ski resort isleft open to the modeling groups within PROSNOWand stakeholders and can be based on character-istics such as terrain elevation, slope, aspect, thepresence or absence of snow guns, or productionpriorities. The SRUs as such constitute the elemen-tary elements that will be processed by the PROS-NOW platform and the basis on which model out-puts will be presented to the users, however theyare not necessarily equivalent to the smallest model

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Table 3: Adjustable grooming related parameters as implemented in the three models. The last three columns indicate whether therespective parameter is implemented in AMUNDSEN (A), Crocus (C), or SNOWPACK/Alpine3D (S).

Parameter Symbol Unit Description A C S

Grooming period GPDaterange

Start and end date for grooming × × ×

Grooming time GTTimerange

Daily start and end time for grooming × × ×

Grooming threshold GH kg m−2 /cm

Minimum SWE (Crocus) or snow depth (SNOWPACK/Alpine3D)required for grooming

– × ×Penetration depth PD kg m−2 Part of the snowpack affected by grooming – × ×Target density ρt kg m−3 Target density that could be reached by grooming – × ×Target SSA SSAt m2 kg−1 Target specific surface area that could be reached by grooming – × –Target sphericity St % Target sphericity that could be reached by grooming – × –

Figure 1: Flowchart of the snow production procedure containingthe parameters shared by all three models.

units for which the simulations are performed. Forexample, simulations could still be performed fullyspatially distributed on a high resolution grid andonly be aggregated to the coarser SRU scale in apost-processing step. This approach will be pursuedin the AMUNDSEN and Alpine3D simulations, whilethe Crocus simulations will directly be performed onthe actual SRU scale.

The total number of SRUs for an average ski re-sort will typically range between several tens and afew hundreds. Figure 2 exemplarily shows a possi-ble discretization of a ski resort into SRUs.

3.2. Model configurations

While the formulation of snow management prac-tices in the models generally allows to adequately

Figure 2: Example of the discretization of a ski resort into SRUsbased on the topographic characteristics of the slopes and thepresence/absence of snow guns.

simulate real practices (as demonstrated previouslyby Hanzer et al. (2014) for an Austrian ski resortand Spandre et al. (2016, 2017) for French ski re-sorts), in practice the parameters listed in Table 2are not constant but vary both in space and time,as the decision when and where to produce snow ismade on a day-by-day basis by the snow productionteams in the ski resorts. In order to be able to assistthe users in making these decisions, the operationalforecasting system developed in PROSNOW will in-clude configurations of different snow managementstrategies based on the current snow conditions andthe meteorological forecasts. Allowing the users tochange the model parameters interactively is how-ever not foreseen both due to the computationaldemands and the increasing complexity of such asystem. Rather, a predefined set of configurationswhich should be representative of the most impor-tant management choices will be prepared.

The choice of proposed configurations is listed in

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Table 4: Default values (to be adapted for individual ski resorts) for the snow management configurations as used in the operationalworkflow of PROSNOW. The subscripts b and r indicate the base-layer and reinforcement periods, respectively.

Parameter Value Source Combinations

PPPPb = 01 Nov–15 DecPPr = 16 Dec–31 Mar

Hanzer et al. (2014); Spandre et al. (2016) 1

PTPTb = 00:00–24:00PTr = 18:00–08:00

Spandre et al. (2016) 1

PR 1. PRfan = −4.83Tw + 3.942. PRlance = −3.94Tw − 4.23

Hanzer et al. (2014) 2

TT

1. TTfan = −2 ◦CTTlance = −4 ◦C

2. TTfan = −4 ◦CTTlance = −6 ◦C

2

WT WT = 4.2 m s−1 Spandre et al. (2016) 1

CT

1. CTb = 150 kg m−2

CTr = ∞2. CTb = 250 kg m−2

CTr = ∞2

STSTb = ∞STr = 60 cm

Hanzer et al. (2014) 1

IS

1. IS = (0, 0)2. IS = (1, 0)3. IS = (0, 1)4. IS = (1, 0)

4

Figure 3: The 34 model configurations as defined in Table 4.

Table 4 and contains a range of strategic variablesas presented in Table 2 as well as one tactical vari-able, the inhibition switch (IS). The strategic vari-ables concern the snow management choices overthe entire season, whereas IS allows to define a setof rules that guide the daily operational choices inthe next few days. The selection of configurationslisted in Table 4 can be summarized as follows (theindividual parameter values, however, can of coursebe adapted for individual ski resorts):

• The base-layer production period (PPb ) is setto the period from 1 November to 15 De-cember in order for the resorts to be able toopen in time for the Christmas holidays. Dur-ing this period production is possible duringthe entire day (PTb = 00:00–24:00). Simula-tions are performed for two production thresh-olds (CTb ) after which production should bestopped, namely 150 and 250 kg m−2, respec-

tively (corresponding to snow depths of 30and 50 cm assuming an average density of500 kg m−3). No snow threshold (STb ) is set,i. e., these snow amounts are produced regard-less of the natural snow accumulation duringthis period.

• The reinforcement snowmaking period (PPr ) isset to the period 16 December until 31 March.Here, snow is only produced during nighttime(PTr = 18:00–08:00) and only if the total snowdepth on the slopes is below STr = 60 cm.

• Separate simulations are performed assumingthe ski resort being equipped with fan gunsand lance guns, respectively, using the genericparameterizations described in Hanzer et al.(2014). For each of these snow gun types(corresponding to different production rates forgiven ambient conditions) again two scenar-ios are considered when production should betriggered: for fan guns, wet-bulb temperaturethresholds (TT) of −2 ◦C and −4 ◦C are consid-ered, while for lance guns the thresholds areset to −4 ◦C and −6 ◦C.

• An additional variable is introduced which aimsat accounting for the short-term managementdecisions: the inhibition switch (IS) allows tostop snow production on a daily basis withinthe next two days. IS is defined as a tu-ple (ISd+1, ISd+2), where a value of 1 for ISd+1

or ISd+2 indicates that production should bestopped from 18:00 today until 18:00 tomorrowor 18:00 tomorrow until 18:00 on the day aftertomorrow. I. e., IS = (0, 0) corresponds to “nor-mal” production according to the settings de-fined earlier, whereas IS = (1, 1) indicates that

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all production should be ceased for the next twodays.

Combining these settings (two scenarios each forPR, TT, and CTb , and four scenarios for IS) amountsto 32 separate simulations. In addition, one modelrun considering untreated natural snow only andone run considering groomed natural snow withoutsnowmaking is included as well, amounting to a totalof 34 combinations as shown in Figure 3.

4. RESULTS

Figure 4: Temporal evolution of snowpack and snow productionvariables as simulated by Crocus for an SRU at 2100 m a.s.l. inthe Les Saisies ski resort and the configurations 1, 2, 15, 31, 11,and 27.

While work on the models and the configurationoptions is still in progress, Figures 4 to 6 showpreliminary results obtained using Crocus, AMUND-SEN, and SNOWPACK/Alpine3D, and demonstratethe influence of the various strategic configurationson the simulated snowpack evolution.

Figure 4 shows the temporal evolution of severalvariables as simulated by Crocus for an SRU at2100 m a.s.l. in the Les Saisies ski resort. Resultsare shown for the configurations 1 (natural snowonly), 2 (groomed natural snow), 15 (lance guns,TT = −6 ◦C, CTb = 150 kg m−2), 31 (lance guns,TT = −6 ◦C, CTb = 250 kg m−2), 11 (lance guns,TT = −4 ◦C, CTb = 150 kg m−2), and 27 (lance guns,TT = −4 ◦C, CTb = 250 kg m−2).

Figure 5 shows the spatial distribution of SWE assimulated by AMUNDSEN for 16 November 2017

Figure 5: SWE for 16 November 2017 23:00 as simulated withAMUNDSEN (10 m resolution) for the Colfosco ski resort assum-ing the configurations 3 (top, i. e., fan guns with TT = −2 ◦C) and15 (bottom, i. e., lance guns with TT = −6 ◦C) and start of produc-tion on 14 November. Off-slope areas show the simulated naturalsnowpack.

23:00 in the Colfosco ski resort. Results wereobtained by running the model in 10 m resolutionwithout snow production (i. e., configuration 1) un-til 13 November while switching to configurations 3(left plot, i. e., fan guns with TT = −2 ◦C) and 15 (rightplot, i. e., lance guns with TT = −6 ◦C) for the period14–16 November.

Figure 6 shows the snow depth for a sector inthe Lenzerheide ski resort as simulated with SNOW-PACK/Alpine3D for 24 December 2011 after startingsnow production in early December (left), and for1 April 2012 assuming no snow production in thelast two months (right).

5. CONCLUSIONS AND OUTLOOK

We have presented the current state of integrationof snow management practices in the AMUNDSEN,Crocus, and SNOWPACK/Alpine3D models, as wellas the approach for the spatial discretization of skiresorts and the selection of management configura-tions for the operational workflow within the PROS-NOW project. First simulation results obtained usingthese configurations have been presented for se-

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Figure 6: Simulated snow height with SNOWPACK/Alpine3D (5 mresolution) for a sector in the Lenzerheide ski resort assuming dif-ferent snow height conditions for each slope section. The lowestpoint is at 1500 m a.s.l. and the highest at 2250 m a.s.l. Left:snow height on 24 December 2011 after starting the machine-made snow production in December. Right: snow height on1 April 2012 without machine-made snow production for the lasttwo months. Off-slope areas show the simulated natural snow-pack.

lected ski resorts. Future work will focus on com-pleting the setup of the models for all PROSNOWpilot ski resorts including the assimilation of localsnow production data (i. e., forcing the models withprescribed water consumption volumes) and snowdepth measurements. Performance of the modelsdriven by both observed meteorological data anddownscaled hindcast data will be evaluated for his-torical conditions using both in-situ and remotelysensed (satellite-derived snow cover maps) obser-vation data prior to running the models with forecastdata. First evaluations of Crocus snowpack simu-lations (natural snow only) driven by meteorologi-cal forecasts in the context of PROSNOW are pre-sented in Carmagnola et al. (2018).

ACKNOWLEDGEMENTS

We thank our project partners and pilot ski resortsfor many constructive discussions which are con-tributing to shape model implementations that arepractical from a user and stakeholder perspective.

This project has received funding from the Euro-pean Union’s Horizon 2020 research and innovationprogramme under grant agreement No 730203.

REFERENCES

Bartelt, P. and Lehning, M. (2002). A physical SNOWPACK modelfor the Swiss avalanche warning. Cold Regions Science andTechnology, 35(3):123–145.

Bavay, M. and Egger, T. (2014). MeteoIO 2.4.2: A preprocessinglibrary for meteorological data. Geoscientific Model Develop-ment, 7(6):3135–3151.

Carmagnola, C. M., Morin, S., Lafaysse, M., Vernay, M., Francois,H., Eckert, N., Batte, L., Soubeyroux, J.-M., and Viel, C.(2018). Combination of climatological information and me-teorological forecast for seamless prediction of Alpine snowconditions. In ISSW 2018 Innsbruck Proceedings.

Durand, Y., Brun, E., Merindol, L., Guyomarc’h, G., Lesaffre, B.,and Martin, E. (1993). A meteorological estimation of relevantparameters for snow models. Annals of Glaciology, 18:65–71.

Hanzer, F., Marke, T., and Strasser, U. (2014). Distributed, explicitmodeling of technical snow production for a ski area in theSchladming region (Austrian Alps). Cold Regions Science andTechnology, 108:113–124.

Lehning, M., Volksch, I., Gustafsson, D., Nguyen, T. A., Stahli,M., and Zappa, M. (2006). ALPINE3D: A detailed model ofmountain surface processes and its application to snow hy-drology. Hydrological Processes, 20(10):2111–2128.

Morin, S., Dubois, G., and the PROSNOW Consortium (2018).PROSNOW – Provision of a prediction system allowing formanagement and optimization of snow in Alpine ski resorts.In ISSW 2018 Innsbruck Proceedings.

Spandre, P., Francois, H., Thibert, E., Morin, S., and George-Marcelpoil, E. (2017). Determination of snowmaking efficiencyon a ski slope from observations and modelling of snowmak-ing events and seasonal snow accumulation. The Cryosphere,11(2):891–909.

Spandre, P., Morin, S., Lafaysse, M., Lejeune, Y., Francois, H.,and George-Marcelpoil, E. (2016). Integration of snow man-agement processes into a detailed snowpack model. Cold Re-gions Science and Technology, 125:48–64.

Strasser, U. (2008). Modelling of the mountain snow cover inthe Berchtesgaden National Park. Technical report, Berchtes-gaden National Park, Berchtesgaden.

Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Moigne,P. L., Martin, E., and Willemet, J. M. (2012). The detailedsnowpack scheme Crocus and its implementation in SURFEXv7.2. Geoscientific Model Development, 5(3):773–791.

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