SNAPS. Northern Periphery programme. SNAPS Work Package 5: Forecasting the snowpack structure. Prepared by. Issue / Revision: 1 / 0

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1 SNAPS Northern Periphery programme SNAPS Work Package 5: Forecasting the snowpack structure Prepared by Laura Rontu, FMI, Finland Richard Essery, University of Edinburgh, Scotland Bolli Palmason and Magni Hreinn Jonsson, IMO, Iceland Hedda Breien and Krister Kristensen, NGI, Norway Issue / Revision: 1 / 0

2 Document controlled by: Harpa Grímsdóttir Page i

3 Table of Contents 1 Application of Crocus snowpack model Results of the Crocus experiments and their validation Sensitivity of Crocus results to the choice of atmospheric forcing Crocus in Stryn, Norway Validation of Crocus against snow pit measurements at Kistufell study site, Iceland Comparing Crocus with high-resolution snowpack observations in Sodankylä, Finland Conclusions References Appendix A. Coordinates of SNAPS target points Finland Iceland Norway Sweden Appendix B. Observations and models applied during SNAPS Page ii

4 1 Application of Crocus snowpack model For the prediction of snowpack structure, a physically-based multi-layer snowpack model Crocus (Vionnet et al., 2012) within SURFEX (Masson et al., 2013) v.7.3 was applied in research (hindcast) mode at chosen locations. Crocus simulates the evolution of the snowpack providing as output several variables applicable for avalanche prediction, e.g. snow depth, layering of the snowpack, crystal form and size, snow hardness and wetness, density and temperature. Model results can further be interpreted for the avalanche risk by applying an expert system (MEPRA, Giraud, 1992). The model is started before the first snowfall, and it adds a new layer (up to 50) every time snowfall is detected. Crocus was chosen for SNAPS instead of the originally suggested Edinburgh snow model (Essery, 2013) because Crocus has been made available in a suitable form as part of the SURFEX system and because it is directly intended for avalanche risk forecasting. The weather parameters needed for driving Crocus within SURFEX are the following: near-surface wind speed and direction, temperature, humidity, surface pressure and elevation, downwelling, short-wave and long-wave radiation flux, snow and rain precipitation. In SNAPS, Crocus was driven by a variety of atmospheric input: SYNOP observations, NWP models from European Centre for Medium Range Weather Forecast (ECMWF, High Resolution Limited Aread model (HIRLAM, based on Unden et al., 2002, and HARMONIE-AROME (based on Seity et al., 2011, Benard et al., 2010). Figure 1 shows a map of the chosen target locations, where Crocus was applied for the winters , and autumn Coordinates of the locations are given in Appendix A. Figure 1. SNAPS target points in Iceland, Norway, Sweden, Finland, Scotland. Coordinates are given in Appendix A. Page 1

5 2 Results of the Crocus experiments and their validation 2.1 Sensitivity of Crocus results to the choice of atmospheric forcing HIRLAM (resolution 7 km/65 levels) and experimental HARMONIE (resolution 1 km/65 levels) lowest model level temperature, humidity, wind as well as the downward SW and LW radiation and (snow) precipitation at the surface were applied to drive Crocus for the autumn 2013 at Kistufell target point. Atmospheric forcing was based on the NWP forecasts with lead times of 3 hours (state variables) and 6 hours (accumulated radiation and precipitation) and linearly interpolated to each one-hour time step of the Crocus experiment. The mean elevation of the HIRLAM gridpoint was 351 metres, that of the HARMONIE 492 metres, no adaptation of the input variables was attempted towards the 600-metre elevation of the observation point at Kistufell. Figures 2 and 3 show the Crocus results based on HIRLAM and HARMONIE forcing, respectively. In this case the predicted snowpack structure, i.e. the vertical profiles of snow density, specific surface area (inversely related to optical grain size), temperature and specific liquid water content in the snowpack show more similarities than differences. The main difference between the experiments is related to the snow depth, whose maximum is 1.1 m for HIRLAM and 1.9 m for HARMONIE forcing. This can be compared to the snow depth observation at the nearby Seljalandhlid at the elevation of ca. 600 metres, indicating 2.7 m (Figure 4, lower right corner) at the snowpit location. The reason for the different snow depth predicted by Crocus is the different snow precipitation by the driving HIRLAM and HARMONIE three-dimensional models. The precipitation difference is indicated by the difference of the snowpack water equivalent (SWE), predicted by these models (Figure 4b). Figure 4a shows the corresponding SWE analyses (initial states of each forecast cycle) of HIRLAM and HARMONIE. Analysed SWE is the result of interpolation of snow depth observations available via the World Meteorological Organisation (WMO) Global Telecommunication System (GTS) to the model grid. It should be noted that these snow depth observations are done on lowland and the observation points are far apart and are, therefore, not representetative for the study point at Kistufell which is at 600 m elevation. Division of the SWE values by an assumed snow density of 250 kg/m 3 gives an estimate of the snow depth in metres. The operational HIRLAM forecast thus suggested a maximum 1.2 metres, the experimental HARMONIE about 2.7 metres. These snow depths were not used as forcing for Crocus, but the difference is related to the snowfall accumulated in the driving forecasts during the autumn. The much larger snowfall from the very fine resolution HARMONIE experiment, as compared to the coarser resolution operational HIRLAM, led to the much larger snow depth also in Crocus whose snow accumulation was driven by the host model snowfall. The HARMONIE result predicting a maximum snowdepth about 2.7 m is in correlation with the measured snowdepth from an automatic snow depth sensor that is close to the site (Figure 4). It is also interesting to note the large difference between the analysed and predicted SWE in HARMONIE while in HIRLAM the analysed and predicted snow depth were almost identical. The analysed snow depth was not used in the present experiments, but would show up in the snow maps by the NWP models. Different model resolutions, differences in the data assimilation methods and usage of observations in HIRLAM and HARMONIE may explain the differences between the analyses and between the analyses and forecasts. Page 2

6 Figure 2. Result of Crocus forecast driven by the operational HIRLAM forecast for October December 2013 at Kistufell target point. Page 3

7 Figure 3. Result of Crocus forecast driven by the experimental HARMONIE-AROME forecast for October December 2013 at Kistufell target point. Page 4

8 Figure 4. Analysed (a) and predicted (b) by operational HIRLAM and experimental HARMONIE snow water equivalent (SWE, kg/m2) at the gridpoint representing Kistufell between the 1st of October 2013 and 7th of January Estimate of snow depth can be obtained by division of SWE by an assumed snow density of 250 kg/m3. The measured snowdepth (cm) by Seljalandshlid automatic snow depth sensor is shown for the same period in the inserted figure in the lower right corner. 2.2 Crocus in Stryn, Norway During the SNAPS test period, a cycle of natural avalanche activity occurred between the 22 nd and the 25 th February A photo of post-avalanche situation 27 th February in Kringdalen is shown in Figure 5. The general avalanche hazard was temporarily estimated to degree 4, or "High" according to the European Avalanche Hazard Scale. The avalanches in this period were caused mainly by new snow accumulating in the starting zones and a weak interface between new and old snow. Weather observations for the period made at the NGI field station at 930 m a.s.l. in Grasdalen, Stryn, are shown in Figure 6. The corresponding snow profiles generated by a Crocus simulation with input from HIRLAM (also in Figure 6) show a fairly good correspondence with the observed snow heights and development. Page 5

9 Figure 5. A large slab avalanche in new snow in Kringdalen, north of the NGI field station. The avalanche probably released on the 25th of February and the photo was taken 2 days later. (Photo: Krister Kristensen) Page 6

10 Figure 6. Observed weather and snow at the NGI field station at 930 m a.s.l. in Grasdalen, Stryn, with corresponding snow profiles generated by the Crocus HIRLAM simulation. The period considered is February 19 to 28, At the time there was no possibility to make snow pack investigations in the field. However, the snow stratigraphy simulation from Crocus for the 25th of February 2012 (Figure 7) indeed seems to indicate that the new snow situation caused the avalanche, see the 75 cm thick top layer of new snow depicted in green. The somewhat limited experience with the Crocus program indicates that running the Crocus simulation in real time, driven by a higher resolution atmospheric model may be beneficial for avalanche forecasting and enable forecasters to assess the snow conditions in areas of interest that are not easily accessible during periods of avalanche activity. Very thin layers and weak interfaces between layers will still likely be difficult to model. Page 7

11 Figure 7. Snow stratigraphy simulation from Crocus for the 25th of February Validation of Crocus against snow pit measurements at Kistufell study site, Iceland Snow profiles for IMO s snow pit site at Kistufell were compared with Crocus analyses. The results from Crocus for Kistufell, with HIRLAM 7 km forcing, were compared to snow profiles obtained at Kistufell during the winter Eleven snow profiles were recorded this winter. One would not expect the results from the model and the snow profiles to be identical. Within a 7x7 km grid cell the snowpack may have a great spatial variability, both the total snow depth as well as the number of layers and crystal form. The results from the model should anyhow be similar to the snow profiles. Snow depth Automatic snow depth sensors are located at the snow pit site at Kistufell and at Seljalandsdalur about 3km away at similar elevation. The snowpack in the Crocus model is considerably shallower than the snowpack at these locations as seen in figure 8. Again, identical results are not expected, however, the locations of the snow sensors have been chosen because they are believed to be representative for the area. Figure 8 shows that the snow depth is similar at these two sites throughout the winter, although they differ a lot in the beginning of January. Better representation of the snow depth would be expected with HARMONIE forcing as seen in figure 4. Page 8

12 Figure 8. Snowdepth from the Crocus model, with HIRLAM 7km forcing, for the winter compared to measured snow depth at Kistufell and Seljalandsdalur. Layering Layers shown in the model are in most cases plausible but thin layers found in snow pits are often missing in the model. Ice layers that were found in three snow pits were not found in the model and the model did not show any ice layers at all for that winter. Ice layers are often very thin, only few mm, but they are important because weak layers tend to form above or below them. Layers in the model are therefore fewer and the snowpack in the model is generally simpler than observed in the snow pits. Crystal form Precipitation particles, fragmented particles, ice layers, facets and melt forms were observed in snow pits during the winter These crystal forms were also in the model results except that ice layers were not shown in the model as stated earlier. Layers with faceted crystals are one of the most common types of weak layers in Iceland. They were always shown in the model when they were found in snow pits. But they were often too thick and it is likely that they were present too often for this winter in the model. The formation of facets is driven by high temperature gradient in the snowpack. The temperature gradient is in average higher in thin snowpack than thick one. That is probably the reason why facets are frequently present in the model. Page 9

13 Hardness The snow hardness is in all cases underestimated in the model. The hardness is estimated in snow pits with a hand test that is somewhat a subjective test (McClung, 1993). However, the difference is too great to be explained by a systematic error in the hand test alone. On the other hand, the relative hardness between layers in the model is more in correspondence with results from the snow pits, which is most important in avalanche work. Temperature The difference in snow depth between the model and observation causes difference in the temperature within the snowpack. It takes longer time for fluctuation in air temperature to reach down through a thick snowpack than in thin one. Despite this, the temperature from Crocus was in many cases in accordance with observed temperature. However, there were also cases where the difference was noticeable and in some cases the difference in air temperature was significant. That indicates that in these cases the temperature was incorrect in the HIRLAM model. The most obvious difference between the model results and observation is the snow depth. The difference in snow depth is the most likely cause for differences in other factors as well, such as grain type and temperature. Despite that, the model results give indication on the snow profile although some details are missing such as thin layers. With more accurate snow depth that could be obtained by other forcing the results might be enhanced. The most obvious option is to use HARMONIE as forcing as it would in most cases, according to figure 4, give a greater snow depth than HIRLAM. Operational forecast with Crocus and systematic validation of the real-time results is planned at IMO the winter HARMONIE will be used as atmospheric forcing for the Crocus model. Page 10

14 2.4 Comparing Crocus with high-resolution snowpack observations in Sodankylä, Finland SNAPS trials of Crocus and HIRLAM for snow modelling in the winter of coincided with the third Nordic Snow Radar Experiment (NoSREx-III) at Sodankylä, during which detailed snow pit measurements of snow density, temperature and structure were made. Snow specific surface area was measured by several objective methods; from these measurements, an equivalent optical grain size can be retrieved. Figure 9 compares Crocus simulations with snow pit measurements on several dates. Although differences in detail are evident, the Crocus simulations capture the broad trends of increasing density, temperature and grain size of snow towards spring. Figure 9. Profiles of snow density, temperature and optical grain sizes at Sodankylä in simulated by Crocus (lines) and measured in snow pits (dots). Page 11

15 3 Conclusions Comparison of Crocus results with different types of in-situ observations was started during the project. The comparison allowed to suggest that running the Crocus simulation in real time, driven by a high resolution atmospheric model may be beneficial for avalanche forecasting and enable forecasters to assess the snow conditions in areas of interest that are not easily accessible during periods of avalanche activity. Although differences in detail are evident, the Crocus simulations capture the broad trends of increasing density, temperature and grain size of snow towards spring. Very thin layers and weak interfaces between layers will still likely be difficult to model. Most of the studies were conducted using the HIRLAM weather prediction model for atmospheric forcing of Crocus. A quick comparison indicates that the finer resolution HARMONIE model may give more accurate results. A large part of the Crocus results obtained during SNAPS still remains to be analysed and compared with observations. Operational forecast with Crocus and systematic validation of the real-time results is planned to be started at IMO during the year 2014 and is ongoing in Met Norway (outside the SNAPS project). Page 12

16 4 References Bénard, P., J. Vivoda, J. Mašek, P. Smolíková, K. Yessad, C. Smith, R. Brožková, and J.-F. Geleyn, 2010: Dynamical kernel of the Aladin-NH spectral limited-area model: Revised formulation and sensitivity experiments. Quart. J. Roy. Meteor. Soc., 136, Essery (2013). Snowpack modelling and data assimilation. Proceedings of ECMWF-WWRP/THORPEX Workshop on polar prediction, Reading, June Giraud G., MEPRA: an expert system for avalanche risk forecasting, Proceedings of the International Snow Science Workshop, 4-8 October 1992, Breckenridge, Colorado, USA, p Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, , doi: /gmd , McClung, D., P. Schaerer, The Avalanche Handbook. Seattle, Washington, USA. Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, V. Masson, 2011: The AROME-France Convective-Scale Operational Model. Mon. Wea. Rev., 139, doi: Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J. And co-authors The HIRLAM-5 scientific documentation. Available online at: Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, , doi: /gmd , Page 13

17 Appendix A. Coordinates of SNAPS target points Finland Sodankylä N, E FMI Arctic research centre, road weather station Yllästunturi , Road with avalanche risk Luosto , Ski centre, location of weather radar Vt4 Saariselkä , Road with wind drift risk, road weather station Vt21 Ropinsalmi , Road with wind drift risk, road weather station Vt5 Kairala , Road with wind drift risk, road weather station Iceland Kirkjubolshlid (Datum: WGS84): , Height: 600 m.s.l Sudavikurhlid (Datum: WGS84): , Height: 550 m.s.l Steingrimsfjardarheidi (Datum: WGS84): Height: 420 m.s.l western , middle , eastern , Page 14

18 Kistufell , Height: 600 m.s.l Norway Tromso Fv91 Breivikeidet , Sunnmore FV 63 Geiranger-Eidsdal , Sunnmore RV , Sweden Ritsem road (Datum: WGS84): , Height: 1200 m.s.l Abisko , Scotland A93 at Glenshee , close to the Cairnwell summit AWS) A9 at Drumochter , Page 15

19 Appendix B. Observations and models applied during SNAPS Observations Category Usage Comments Remote sensing optical Snow extent/fraction maps Maps in snaps-project.eu Remote sensing SAR Wet snow mapping Maps in snaps-project.eu Remote sensing passive microwave Snow Water Equivalent maps SYNOP snow depth Input to NWP model data assimilation, validation SYNOP weather observations SM4 snow sensor: snow depth and temperature profile Road weather station measurements Road weather web cameras Statistical study on weather v.s. avalanches, input to Crocus Forecast, validation Forecast, input to road weather model, validation Forecast, validation Maps in snaps-project.eu Set up during SNAPS in Westfjords and Norway Observations at snowsense.is Models Operational HIRLAM Operational HARMONIE Weather, snow maps, atmospheric forcing for Crocus, road weather model Weather, snow maps, atmospheric forcing for Crocus, road weather model HIRLAM RCR run in FMI Run separately in IMO and FMI Crocus (Meteo France) Snowpack structure Driven by observations or NWP forecasts; research runs and validation in Edinburgh, IMO Road weather model Drifting snow algorithm by Skúli Þórðarson Road conditions, including drifting snow Drifting snow maps for Iceland Driven by observations and NWP forecasts, operational in FMI, includes drifting snow algorithm Maps produced in IMO Page 16