A NETWORK OF AUTOMATIC WEATHER AND SNOW STATIONS AND SUPPLEMENTARY MODEL CALCULATIONS PROVIDING SNOWPACK INFORMATION FOR AVALANCHE WARNING
Michael Lehning, Perry Bartelt, Bob Brown, Tom Russi, Urs St6ckli and Martin Zimmerli Swiss Federal Institute for Snow and Avalanche Research, Davos
ABSTRACT: The Swiss Federal Institute for Snow and Avalanche Research (SLF) began to construct a network of high Alpine automatic weather and snow measurement stations in the Summer of 1996. Presently 35 stations are in operation and another 25 stations will be on-line before the Summer of 1999.The stations measure wind, air temperature, relative humidity, snow depth, surface temperature, ground (soil) temperature, reflected short wave radiation and three temperatures within the snowpack. The measurements are transferred hourly to the SLF in Davos and the data are used to drive a finite-element based physical snowpack model. The model runs every hour and provides supplementary information regarding the state of the snowpack at the sites of the automatic stations.
New snow amounts, settling rates, possible surface hoar formation, temperature and density profiles as well as the metamorphic development of the snowpack are all predicted by the model.
The model is connected to a relational data base which stores the measurements as well as the model results. New visualization tools are available which allow a fast, easy and comprehensive access to the stored data. The model has been tested in quasi-operational mode during the Winter of 1997 / 98. The calculation is reliable in terms of the energy budget and the mass balance. The description of snow metamorphism is currently being improved. The model will be fully operational in the Winter of1998/99and will be used by local, regional and national avalanche forecasters.
KEYWORDS: remote weather and snow measurements, snowcover simulation, snow cover structure, snow metamorphism, mass balance, energy balance
1. INTRODUCTION
Avalanche warning with an ever increasing spatial and temporal resolution is required because of the increasing use of high Alpine areas by Winter tourists. Therefore, the Swiss avalanche warning service in Davos is expanding its services to provide a daily forecast of avalanche danger on a national scale and several regional avalanche danger forecasts (Russi et aI., this issue). The national forecast is now issued in the evening and is valid for the next day. The regional forecasts are issued in the early morning.
These forecasts can only be made based on an adequate knowledge of the snowpack status and the weather throughout Switzerland.
Traditionally, the Swiss Avalanche Warning Service has observers who provide weather and snow cover information. This observer network (Russi et aI., this issue) suffers from the limitation of a small temporal and unrepresentative spatial distribution since they measure only once or twice per day and since
they are typically located in Alpine villages below 2000 m a.s.1.
To overcome these limitations, automatic measuring stations have been built throughout the Swiss Alps. These stations provide weather and snow cover information. The direct measurements at the stations are supplemented by model calculations. Certain important parameters such as new snow amount, settling rates, surface hoar and especially the internal structure of the snowpack can not be measured by these stations. The model calculations are needed to fill this gap. The model results together with the measurement data also serve as input parameters for local and regional avalanche forecast models which are currently under development at the SLF.
The French model chain SAFRAN-CROCUS- MEPRA (Durand et aI., 1993; Brun et aI., 1989;
Brun et aI., 1992; Giraud, 1992) is used in a similar yet conceptually different way for the avalanche warning service in France. It calculates "representative" snow cover characteristics for geometrical pyramids representing a region in the French Alps. With
Corresponding author address: Michael Lehning, Swiss Federal Institute for Snow and Avalanche Research (SLF), Fluelastrasse 11, CH- 7260 Davos Dort, phone: +41 81 417 0158 fax: +41 81 417 0110 e-mail:
lehning@slf.ch.
Figure 1a. Example of IMIS wind station on a mountain crest. Visible are the solar panel, the logger-transmission box and some sensors.
this idealized approach, a realistic evaluation against measurements and observations is difficult on an operational basis. The known small scale and local variabilty of the snowpack is not taken into account. However, since the model calculations include different height ranges and expositions, an assessment of the general state and variability of the snowcover on a regional basis might be possible.
Our concept is to model the real local snowpack at many distinct sites in the Swiss Alps in order to gain an impression. of variability of the snowpack status. With this approach, the question still re":lains,.
representative this local snowcover slmulat.lon IS.
However the advantage of this approach IS that the calculation can be directly verified.
This paper gives an overview of how information is retrieved operationally from the measuren:ent stations, supplemented by model
and finally used by the avalanche warning service.
2. IMIS STATIONS
In the Summer of 1996; the Swiss Federal Institute for Snow and Avalanche Research (SLF) started to set up a network of high Alpine automatic weather and snow stations (IMIS) in cooperation with the Swiss mountain cantons.
During the Winter of 1996/97, the first 20 stations were already in operation. Since Winter 1997/98, 40 stations are operational and another 30 stations are planned to be built until the Summer of 1999. This network provides crucial additional information to the observer measu rements.
2.1 Station Characteristics
An automatic IMIS station consists of a wind station situated on top of a mountain peak (Fig.
1a) and a snow station close to the wind station but on a rather flat site which is not directly exposed to the wind (Fig. 1b). The snow stations are located at altitudes of typical avalanche starting zones, i.e. between 1600 and 2700 m a.s.1. Measured parameters include wind, air temperature, relative humidity, snow depth, surface temperature, soil temperature, reflected short wave radiation and three temperatures within the snow cover. The meteorological parameters are measured at the wind- and snow stations. The snow parameters are measured only at the snow stations. The stations operate autonomously using a battery and a solar panel. Therefore the sensors can not be heated or ventilated. Some measurement errors and data gaps occur because of this limitation.
Experience gathered during the last two winters shows, however, that the operation of the stations remains stable and reliable for most of the Winter season.
The concept of high Alpine weather and snow station is not totally new. In cooperation with the Swiss Meteorological Institute, the SLF also operates 11 additional automatic weather and snow stations (ENET, Fig. 2), which are typically located in the vicinity of high Alpine ski resorts in order to have access to power supply. This concept allows to have ventilated and heated sensors. The disadvantage is that only few such stations are available and that their location can not be chosen according to the needs of the avalanche forecasters.
. German: Interkantonales Mess- und Informations- System
Figure 1b. Example of IMIS snow station. Visible are among others the wind sensor on top and the ultrasonic snow depth sensor and the humidity / temperature sensor on the beam.
At the IMIS stations, a data logger stores the measurement values in thirty minute intervals.
The transmission to the SLF takes place every hour via a radio signal and the telephone network. At the SLF, the data is centrally stored in a data base and from there immediately distributed to the local avalanche forecasters.
2.2 Example Situation December20 1997 The avalanche forecasters in Davos also have an immediate access to the data. Time series displays of all parameters for a specific station are possible. More important is, however, the overview for a specific area or for the entire Swiss Alps. An example for such a representation is Fig. 2. The snow depths at all at this time operational IMIS stations are displayed together with the data from the observer network and the ENET stations. The figure is taken from the operational data base browser which is used by the avalanche warning service to access information on our central
relational data base. This example situation will further be examined in section 3.7.
3. SNOWPACK MODEL CALCULATIONS The snowcover at each station is simulated with a new numerical model, SNOWPACK, developed at the SLF (Lehning et aI., 1998). The goal of the model is to provide avalanche forecasters with additional information. A full, Le.
mathematical, description of the model is beyond the scope of this text and will be published elsewhere (Bartelt and Lehning, 1998). Here, we give a short description of the most practically relevant features.
3.1 Model Summary: Snow
In SNOWPACK, snow is modeled as a three- phase water and air) porous medium, charactenzed by the volumetric contents of each phase and the microstructural parameters sphericity and dendricity, (Brun et aI., 1992), grain radius, bond radius and a grain type marker. These parameters are considered primary microstructural parameters because temperature gradient (TG) and equi-temperature (ET) metamorphism routines determine the rates of change of these parameters over time.
Additional microstructural parameters, such as the coordination number or the bond neck length are termed secondary since they are calculated the volumetric contents and primary microstructural parameters. The metamorphism code is now under intense development. Wet and new snow metamorphism is presently not accounted for.
3.2 Model Summary: Numerical Solution
The model solves the instationary heat transfer and creep/settlement equations using a Lagrangian finite element method. At the snowcover surface, either the measured surface temp.e.rature is prescribed (Dirichlet boundary condition - DBC) or the meteorological parameters are used to determine a surface heat flux containing sensible and latent heat exchanges and a net longwave radiation contribution (Neumann boundary condition - NBC). For measured now surface temperatures below 0 °C the DBC is used. As soon as the snow surface temperature reaches 0 °C the model switches to the NBC. Shortwave radiation is modeled as a volumetric heat source. The intensity of the absorbed shortwave radiation
All Available Snow Depth (cm) Measurements in the Early Morning of December 20 1997
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Figure 2. Snow heights as measured by all available IMIS stations (bold italics), ENET stations (bold) and the on 20, 1997, 06:00am throughout the Swiss Alps. The four circled IMIS stations will further be investigatedInsection 3.7.
decreases exponentially with distance below the surface.
Phase changes between the ice and water components are also accounted for as volumetric heat sinks (melting) and sources (refreezing). Meltwater is transported via a - simple threshold procedure.
The conservation of mass, momentum and energy is strictly enforced in SNOW PACK at all times.
3.3 Model Summary: Microstructural Viscosity and Thermal Conductivity
The settling of the snow, Le. the height changes of the elements, is governed by a volumetric constitutive relation. For non-dendritic snow, the relation is linked to the microstructural parameters. It includes processes such as pressure sintering within the bonds and initial settling caused by surface tension. The development is based on the model presented by Mahajan and Brown (1993). Distinguishing
between a linear and a non-linear range, a relationship results where the viscosity, TI, is proportional to:
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Comparison between Modeled and Measured Water Equivalent I Total Precipitation at Weissfluhjoch
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3.4 Determination and Treatment of Snowfall
The model can be run in a mode with prescribed precipitation rates. This mode is necessary to run the model as a forecast which will be done in the near future.
One special feature of the model is the determination of snowfall. It does not use an independent estimation of the precipitation rate".
It determines the amount of new snow from the measured snow depth and the model-estimated settling rate: The measured and corrected (Lehning et aI., 1998) snow depth is compared to the model-predicted snow depth for the model case of no snow fall. The difference is taken as new snow depth. New elements are added on top of the snowcover and the height is thus re- adjusted to the measured snow depth. This is only permitted, however, if the threshold conditions for snow fall such as high relative
Figure 3. Evaluation of model mass balance against measurements on the Weissfluhjoch experimental site.
The model total snow water equivalent (which is almost identical to the model accumulated new snow water equivalent) is compared against the manual accumulated new snow water equivalent measurements, the manual total snow water equivalent measurements and standard precipitation gauge measurements. Note that at the end of the period investigated the model underestimates the mass lost from ablation.
humidity and low shortwave radiation are met.
This procedure ensures that for snow fall conditions there is no difference between modeled and measured snow depths.
However, this snow fall estimation method only yields a correct description of the snowpack mass balance if a very good estimation of the snowpack settling rate and especially the new snow density is possible. We use the formulation of Lehning et al. (1998) as initial density estimation. Fig. 3 shows an evaluation of the snow cover mass balance at our experimental site Weissfluhjoch for the Winter 1996/97. The estimated amounts of new snow from the model as well as the modeled total water equivalent have been compared against manual measurements of new snow depth and density (daily) and total water equivalent (twice per month) and against the automatic measurements from the standard precipitation gauge as used by the Swiss Weather Service.
Seasonal Evolution of Density at Station Piz Kesch
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3.5 Operational Use
Our strategy is to have a an operational version of the model available at all times while working on the improvement of a new version.
Figure 4. Time evolution of density and snow depth for the station Piz Kesch during Winter 1997/98.
Because of the link between viscosity and snow microstructure the layered structure of the snowpack is preserved. The x-axis is in days after November 91997.
The gauges are especially equipped for high Thereby we receive valuable feedback from out Alpine snow precipitation. Without any parameter users, Le. the avalanche forecasters. A simple fitting, the agreement between the model version of the model has been used operationally estimates and the manual measurements is very already in Winter 1997/98. The operational good, while the automatic precipitation version is connected to the central relational data measurements typically underestimate the base of the institute. Every hour, as the new .:. precipitation by 30 %. We conclude that our measurements at the 1MIS stations provide method to combine controlled measured updated boundary conditions, the model snowpack heights with our density estimation calculates the snowpack status at the stations plus the modeled snowpack settling gives and writes the results back into the data base.
reliable Winter snow precipitation rates with a Parameters stored are new snow depths and high temporal (30 minutes) and spatial resolution water equivalents, formation of surface hoar, at high Alpine sites. Such an information is surface run-off and a reduced set of profile desperately needed by hydrologists, information including temperature, density and meteorologists and avalanche specialists but grain type. This information is accessed and could not be obtained using conventional visualized by the avalanche forecasters at our methods until now. institute. A smaller set of parameters is also transmitted together with the measured data (2.1) from the stations to approximately 120 local avalanche specialists, who are responsible for the safety of road, railway, residence area and ski area protection.
3.6 Seasonal Snowpack Sample Calculation
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Figure 5. Time series of snow depth and evaluation of settling rates at station Piz Kesch. The x-axis is in days after November 9,1997.
the Alps. The three stations Simplon, Vallascia and Piz Kesch in the southern, central and south-eastern part of the Swiss Alps, respectively, received between 36 and 48 cm of new snow within the 24 hours before the morning of December 20 (Fig. 7). This translates into a considerable increase of the snow depth by approximately 30 %. Also given are the increase in snow depth and new snow amounts for the three day period: The station Simplon had received more than 90 cm of new snow. The comparison between the snow depth difference (SDD in Fig. 7) and the modeled new snow depth (MNSD in Fig. 7) shows how much the snowpack settled. This comparison reveals that the settling rates were small, especially for the 24 hour period when the snowfall activity was high. We have already seen (Fig. 5, day 10) that the model may underestimated the settling during this period. However, it can be assumed that the rather cold temperatures during the snowfall event (not shown) have prevented an effective settling of the snow cover. In conclusion, the southerly (blocked) flow impinging on the Alps brought a lot of new snow to the southern, central and south-eastern regions of the Swiss Alps. During the snowfall event the settling and thus stabilization of the snowcover was small.
The following information is additionally available from the model calculations and the measurements: a) this considerable snowfall occurred after a similar event a week before (Fig. 5, days 33 / 34), b) a longer period of small
The snowfall event we are looking at is the increase in snow depth between days 40 and 42 in Fig. 5.
3.7 Example Situation December20 1997 Fig. 4 uses Piz Kesch station in the Grisons as an example to illustrate what information SNOWPACK can deliver and how it is visualized.
The plot is a copy of a color original which displays the density distribution at the station for the Winter season 1997 / 98. The layering of the snow cover is visible in the density distribution.
Similarly to the density plot, the temperature distribution, the water content, the microstructural parameters and many other quantities can be displayed.
Another way to look at the snowcover development is the display of time series of important quantities. Fig. 5 shows the comparison between measured and modeled snow depth again for the station Piz Kesch.
Between day 10 and 20 the settling rate is underpredicted, while between day 80 and 105 the predicted settling rate is too high and the model introduces new snow on two occasions to correct for the difference between modeled and measured snow depth (3.4). In general, however, the settling rates are predicted well, considering the fact that the microstructure- dependent viscosity (3.3) has not yet been calibrated against measured data. When the metamorphic state of the snowpack is not modeled correctly, the settling rate is also wrong.
Note that for other episodes of new snow settling and also for the ablation period the predicted settling rate is correct.
The arguments presented above are also valid for an evaluation of the modeled temperatures as presented in Fig. 6 again for Piz Kesch. Fig. 6 shows the comparison between modeled and measured temperature 50 cm above ground in the snow cover. The general agreement is good, especially the onset of isothermal conditions is predicted very well. This fact is very important for the avalanche warning service. At the beginning of the simulation the predicted temperatures have a higher variation and are lower than the measured temperatures.
An incomplete description of the metamorphism and how it is linked to the thermal conductivity is the most likely reason for this disagreement.
Turning back to our case study in section 2.2, Fig. 7 presents for four selected IMIS stations the snow depth development before the morning of December 20. The reference station with no snowfall is Schilthorn north of the main crest of
with crucial knowledge from a) the weather forecast, b) the observed avalanche activity and c) own observations to judge the overall snow cover stability and its variability. As a result, a forecast of high avalanche danger had been issued for those parts in the central and south- eastern Alps which received the highest amounts of new snow before and including Saturday December 20.
4. CONCLUSIONS AND OUTLOOK
The Swiss avalanche warning service wants to improve the spatial and temporal resolution of its avalanche forecasts and integrates remote automatic weather and snow measurements with model calculations to provide an objective basis for the judgment of the snow cover status. This integration is accompanied by a central relational data base management and user-friendly visualization tools.
The new model developed has a very modular structure. Its Lagrangian finite element layout provides an ideal framework to model the
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snowfall activity was observed before December 12 (Fig. 5, days 10 to 32) and c) the model indicated possible surface hoar formation during this period (not shown).
For the presented case, the avalanche warning service had combined this information
Snow Depth Development for the Past 24 and 72 Hours in the Early Morning of December 20 1997
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Figure 7. Total snow depth increase and new snow depths for the 24 hour period and the 72 hour period before December20,1997,6:00am.
layered snow cover with its settling in time, growth through snow fall, erosion through wind (not implemented yet) and ablation through melting. The necessary improvement of the metamorphism formulations is emphasized. First attempts to link the thermal conductivity and the viscosity to the microstructural parameters are encouraging: the energy and mass balance of the snow cover can be modeled satisfactorily and the deduced precipitation rates are more reliable than standard precipitation gauge measurements.
With an improvement of the metamorphism formulations, we should be able to describe the development of weak layers in the snowpack.
The model formulation is such that development steps and improvements can easily be implemented. The final goal will be a quantitative description of snow cover stability on different slopes. The research and development plans include a semi-quantitative description of snow transport by wind in combination with a prognostic use based on the numerical weather forecast: The operational Swiss weather forecast model will provide the raw input parameters for SNOWPACK. Those input parameters will be adapted by a downscaling procedure to the local conditions.
ACKNOWLEDGEMENTS
We thank Walter Ammann for initiating and supporting this work. Bernhard Brabec, Roland Meister, Thomas Stucki, Charles Fierz and Pramod Satyawali are thanked for valuable discussions.
REFERENCES
Adams, E.E., Sato, A., 1993: Model for effective conductivity of a dry snow cover composed of uniform ice spheres, Ann.
Glaciol., 18, 300-304.
Bartelt, P., Lehning, M., 1998: 1D - Simulation of the seasonal snowpack with finite elements, Interner Bericht SLF 719, in preparation.
Brun, E, David, P., Sudul, M., Brugnot, G., 1992:
A numerical model to simulate snowcover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13-22.
Brun, E., Martin, E., Simon, V., Gendre, C., Coleou, C., 1989: An energy and mass model of snow cover suitable for
operational avalanche forecasting, J.
Glaciol., 35,333-342.
Durand, Y., Brun, E, Merin-dol, L., Guyomarc'h, G., Lesaffre, B., Martin, E, 1993: A meteorological estimation of relevant parameters for snow models, Ann.
Glaciol., 18, 65-71.
Giraud, G., 1992: MEPRA: an expert system for avalanche risk forecasting, Proc. of int.
snow science workshop, Breckenridge Colorado, 97-106.
Jordan, R., 1991: A one-dimensional temperature model for a snow cover, CRREL Special Report, 91-16.
Lehning, M., Bartelt, P., Brown, B., 1998:
Operational Use of a Snowpack Model for the Avalanche Warning Service in Switzerland: Model Development and First Experiences, Proceedings of the NGI Anniversary Conference, Norwegian Geotechnical Institute May 1998, 169 - 174.
Mahajan, P., Brown, R.L., 1993: A microstructure-based constitutive law for snow, Ann. Glaciol., 18, 287-294.
Russi, T., Ammann, W., Brabec, 8., Lehning, M., Meister, R., 1998: Avalanche Warning Switzerland 2000, Proc. of int. snow science workshop (ISSW '98), Sunriver, Oregon, this issue.
