Description of Recent Snow Models

 

Zong-Liang Yang

 

Department of Geological Sciences, The University of Texas at Austin, Austin, Texas, 78712 USA

 

To appear in Snow and Climate, E. Martin and R. Armstrong (editors), International Committee on Snow and Ice

 

Introduction

 

The presence of snow on the Earth's surface affects physical, chemical and biological processes over a wide range of spatial scales and has important societal effects. In addition, the timing of snowmelt and the subsequent fate of melt water play an important role in the hydrological cycle and water resources (Gray and Prowse, 1993). To accommodate various applications, there are a large number of snow models that have been designed over the past few decades. In mid-1980s, the World Meteorological Organization (WMO) conducted a survey of 11 snowmelt-runoff models that were built for forecasting snowmelt-induced runoff in watersheds (WMO, 1986). Since then, a great number of new snow models have appeared, both in the literature and in operation, largely due to increasing interest in climate change and assessing its impact.

 

A comprehensive snow model survey with 50 questions has been distributed via the internet to summarize the current status of snow models, and more than 40 responses have been received to date. These questions and responses have been documented on the World Wide Web (www.geo.utexas.edu/climate/Research/SNOWMIP/snowmip.htm). A concise summary of these responses is described in the following section, while the questions themselves are given at the end of this chapter.

 

Description of Recent Snow Models

 

The reader can find elsewhere a review of snow physics and a description of snowpack energy and mass budgets. This section presents the status of recent snow models and gives recommendations for future development.

 

Table 1 gives a list of the snow models and source references. Nearly half of the models were developed in the USA. The remainder came from 13 other countries. About half the models were designed for use in atmospheric models, including general circulation models (GCMs), regional climate models (RCMs) and numerical weather prediction models (NWPMs). A quarter of the models are used in snow process studies (e.g., Jordan, 1991) and the remainder for other purposes, such as operational runoff forecasting (e.g., Anderson, 1973), snow frozen-soil studies (e.g., Flerchinger and Saxton, 1989), avalanche forecasting (e.g., Brun et al., 1989), climate monitoring (Grody and Basist, 1996), erosion control (Tarboton and Luce, 1996), downscaling GCM output (Hewitt et al., 1997), and testing optimum complexity of snow model for GCMs (Loth et al., 1993). Some models have multiple purposes. For example, the model by Loth et al. (1993) is sufficiently sophisticated for snow process studies, but also computationally efficient enough for use in atmospheric models. Such a model may be an ideal tool to examine the optimum complexity of snow models for GCMs. Most of the models use precipitation, air temperature, wind speed, humidity, downward solar radiation and downward longwave radiation for snow surface energy and water budget computations. Three snowmelt-runoff models simply use precipitation and air temperature as input (Anderson, 1973; Schreider et al., 1997; Bergstrom, 1997), while one model also uses wind direction to determine snow redistribution due to blowing snow events (Liston and Sturm, 1998).

 

The models display a wide range of complexity in coding, from tens of lines (Grody and Basist, 1996) to tens of thousands of lines (Jordan, 1991). In terms of model structure, almost all the models focus on the snow processes in the vertical dimension only and most of the models use a single layer to represent the whole snow pack. There is little consensus regarding parameterization of snow albedo. Some models assume albedo is constant, or a function of snow age only, or a function of snow depth only, while some assume it depends on several parameters, including grain size and impurity. There are many other approaches that are largely empirical. Most of the models neglect the spectral and directional differences in solar radiation transfer, while others make partial or full allowance for these differences. Half of the models use fixed values for thermal parameters (heat capacity and conductivity), while the remainder assumes that these thermal parameters change with density. More than half of the models neglect the retention of snowmelt water and its percolation, but only ten percent of the models treat vapor transfer processes within snowpack. More than 50% of the models also treat frozen soil process underneath snowpack, and half of the models take account of aspects of the snow-vegetation interaction, but few incorporate sophisticated radiative transfer and aerodynamic processes within the canopy and realistic simulation of snow cover under forested floor. A quarter of the models incorporate effects of sub-grid-scale topography on distribution of precipitation, air temperature and snow depth, and about ten percent of the models use remote sensing data for input and validation.

 

Although detailed one-dimensional snow models exist (e.g., Anderson, 1976; Jordan, 1991), for computational reasons, atmospheric models use relatively simple snow models (Manabe, 1969; Dickinson et al.,1993; Verseghy, 1991; Loth et al., 1993; Lynch-Stieglitz, 1994; Marshall et al., 1994; Dai and Zeng, 1997; Sun et al., 1999). These GCM snow models, generally with one to five snow layers, are designed to resolve the diurnal and seasonal variations of surface snow processes such as surface temperature and heat fluxes, but simplify the treatment of the internal snow processes (e.g., the retention and transport of melt water, melting and freezing, diffusion of temperature and water vapor, and the extinction of solar radiation). Because GCMs have been shown to be highly sensitive to snow processes (e.g., Yeh et al., 1983; Cess et al., 1991), it is critical to have a one-dimensional snow model with adequate realism that is efficient enough for long-term climate integrations.

 

Features of process-level snowmelt models could be helpful for capturing sub-grid scale variability and improving snowmelt simulations. Consequently, an active line of research is being established which links physically based snowmelt models, geographical information system (GIS) analysis, remote sensing technology and assimilated data sets from mesoscale meteorological models in studies of small catchments and watersheds (e.g., Liston and Sturm, 1998; Davis et al., 1998). However, relatively speaking, what is most lacking is research and application of snow water and energy budget analyses at continental and global scales. A question that remains unresolved is the level of complexity required for snow models at those scales. A closely related question is how to relate a one-dimensional, vertical snowpack model to heterogeneous surfaces in each of the GCM land grids. Specifically, what is the optimum methodology to scale up when snow cover is patchy? The heterogeneous distribution of vegetation and topography adds more complexity to this problem. Hopefully, conclusions drawn from research at sub-resolution scales (e.g. local, catchment and watershed scales) will provide an important guide towards developing and improving snow process models at GCM scales.

 

 

Summary and Future Directions

 

The preliminary analysis from a recent survey of snow models indicates that there are many sophisticated snow models currently available which are appropriate at point or local scale, and that a wide range of models have been developed for application in small catchments and watersheds. These process-level snow models are useful in providing guidance towards improving GCM snow models. The future lines of research for snow modeling in climate models are:

 

Acknowledgements

 

I am indebted to Dr. R. C. Bales who suggested that I read the WMO-led snow model survey while designing my questionnaire. I also wish to thank Dr. R. E. Dickinson for his encouragement, and Dr. W. J. Shuttleworth for reading the early draft. All those who took time to complete the snow model questionnaire are gratefully acknowledged. This work was funded by the National Aeronautics and Space Administration (NASA) GCIP award NAG8-1520.


 

References

Albert, M.R. and Krajeski, G. 1998. ‘A Fast, Physically-Based Point Snow Melt Model for Use in Distributed Applications’, Hydrological Processes, 12 (11), 1809-1824.

Anderson, E. A. 1973. ‘National Weather Service River Forecast System -- Snow Accumulation and Ablation Model’, NOAA Technical Memorandum NWS HYDRO-17, US Dept. of Commerce, Silver Spring, MD, 217 pp.

Anderson, E.A. 1976. A Point Energy and Mass Balance Model of a Snow Cover.  Office of            Hydrology, National Weather Service.

Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C. 1989. ‘An energy and mass model of snow cover suitable for operational avalanche forecasting’, J. of Glaciol., 35, 121, 333-342.

Cess, R.D. and  32 Co-authors.  1991.  ‘Intercomparison  of  snow-feedback  as  produced  by general circulation models’,  Science, 253, 888-892.

Dai, Y.-J. and Zeng, Q.-C. 1997. ‘A Land Surface Model (IAP94) for Climate Studies, Part I: Formulation and Valiadation in off-line Experiments’, Advances in Atmospheric Sciences, 14, 433-460.

Desborough, C.E. and Pitman, A.J. 1998. ‘The BASE land surface model’, Global and Planetary Change, 19(1-4), 3-18..

Dickinson, R.E., Henderson-Sellers, A., and Kennedy, P.J.  1993. Biosphere  Atmosphere  Transfer  Scheme (BATS) Version 1e as Coupled to the NCAR Community  Climate Model. NCAR  Tech. Note, NCAR/TN-387+STR.

Douville, H., Royer, J.F., and Mahfouf, J.F. 1995. ‘A new snow parameterization for the Meteo-France climate model, Part I:  Validation in stand-alone experiments’,  Climate Dyn, 12, 21-35.

Essery, R. 1998. ‘Snow modelling in the Hadley Centre GCM’, Physics and Chemistry of the Earth, 23(5-6), 655-660.

Fernadez, A. 1998. ‘An energy balance model of seasonal snow evolution’, Physics and Chemistry of the Earth, 23(5-6), 661-666.

Flerchinger, G.N. and Saxton, K.E. 1989. ‘Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development’, Trans. of ASAE, 32(2), 565-571.

Gray, D.M. and Prowse, T.D. 1993. ‘Snow  and  floating ice’, in Maidment D.R. (ed.), Handbook  of Hydrology, McGraw-Hill, Inc., New York. pp. 7.1-7.58.

Grody, and Basist, 1996. IEEE Transactions on Geoscience and Remore Sensing, 34, 237-249.

Jin, J., Gao, X., Sorooshian, S., Yang, Z.-L., Bales, R. C., Dickinson, R. E., Sun, S. and Wu, G. 1999. ‘One-dimensional snow water and energy balance model for vegetated surfaces’, Hydrological Processes, 13, 2467-2482.

Johnsson, H.  and Lundin, L.-C. 1991. ‘Surface runoff and soil water percolation as affected by snow and soil frost’, Journal of Hydrology, 122, 141-159.

Jordan, R. 1991. A one-dimensional temperature model for a  snow cover. U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory, Special Report 91-16.

Kim, J. and Ek, M. 1995. J. Geophys. Res., 100 (D10), 20845-20854.

Kite, G.W. 1995. ‘The SLURP model’, Chapter 15 in Computer models of watershed hydrology, by V.P. Singh (ed.), Water Resources Publications, Colorado, USA, 521-562.

Koren, V., Duan, Q.-Y., Schaake, J., and Mitchell, K. 1999. ‘Validation of a snow-frozen ground parameterization of the Eta model’, AMS Conference, Dallas, paper J1.3.

Koster, R. and Suarez, M. 1996. ‘Energy and Water Balance Calculations in the Mosaic LSM’, NASA Tech. Memo. 104606, Vol. 9.

Lehning et al., 1998. ‘A network of automatic weather and snow stations and supplementary model calculations providing SNOWPACK information for avalanche warning’, ISSW 98 International Snow Science Workshop, Sunriver, Oregon.

Liston, G. E., and Sturm, M. 1998. ‘A snow transport model for complex terrain’, J. Glaciol., 44, 498-516.

Loth, B., Graf, H.-F., and Oberhuber, J.M. 1993. ‘Snow cover  model for  global  climate  simulations’, J.  Geophys.  Res., 98, 10451-10464.

Lynch-Stieglitz, M. 1994. ‘The development and  validation  of  a simple snow model for the GISS GCM’,  J. Clim., 7, 1842-1855.

Mabuchi, K.,Sato, Y., Kida, H., Saigusa, N., and Oikawa, T. 1997. ‘A Biosphere-Atmosphere Interaction Model (BAIM) and its primary verifications using grassland data’, Papers in Meteorology and Geophysics, 47, No. 3.

Manabe, S. 1969. ‘Climate and the ocean circulation I. The atmospheric circulation and the hydrology of the Earth’s surface’, Mon. Wea. Rev., 97, 739-774.

Marshall, S., Roads, J.O., and Glatzmaier, G. 1994. ‘Snow hydrology in a general circulation model’, J. Clim., 7, 1251-1269.

Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., Esch,  M., Giorgetta, M., Schlese, U., and Schulzweida, U. 1996. ‘The atmospheric circulation model ECHAM-4: Model description and simulation of present-day climate’, MPI-Rep.  218, MPI für Meteorologie, Hamburg, 90 pp.

Schreider, S.Yu., Whetton, P.H., Jakeman, A.J., Pittock, A.B. and Li, J. 1997. ‘Runoff Modelling for Snow-Affected Catchments in the Australian Alpine Region, Eastern Victoria’, Journal of Hydrology, 2, No. 1, 35-47.

Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997. ‘Performance of different soil model configurations in simulating ground surface temperature and surface fluxes’, Mon. Wea. Rev., 125, 216-261.

Stamnes, K.,Tsay, S.-C., Wiscombe, W., and Jayaweera, K. 1988. ‘Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media’, Applied Optics, 27(12), 2502-2509.

Sun, S.F., Jin, J.M., and Xue, Y. 1999. ‘A simple snow-atmosphere-soil transfer model’, J. Geophys. Res., 104, 19587-19597.

Tarboton, D. G. and Luce, C.H. 1996. ‘Utah Energy Balance Snow Accumulation and Melt Model (UEB)’, Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station.

Tokioka, T., Noda, A., Kitoh, A., Nikaidou, Y., Nakagawa, S., Motoi, T., Yukimoto, S., and Takata, K. 1995. ‘A transient CO2 experiment with the MRI CGCM --Quick Report--. J. Meteorl. Soc. Japan, 73, 817-826.

Xue, Y., P. J. Sellers, J.L. Kinter III, and J. Shukla, 1991. ‘A Simplified Biosphere Model for Global Climate Studies’, J. Climate, 4, 345-364.

Verseghy, D.L. 1991. ‘CLASS - A Canadian Land Surface Scheme  for GCMs. Part I: Soil Model’,  Int. J. Climatol., 11, 111-133.

Walland, D.J. and Simmonds, I. 1996. ‘Sub-grid-scale topography and the simulation of Northern Hemisphere snow cover’, International Journal of Climatology, 16, 961-982.

Wigmosta, M. S., Lettenmaier, D.P., and Vail, L.W. 1994. ‘A distributed hydrology-vegetation model for complex terrain’, Water Resources Research, 30(6), 1665-1679.

WMO, 1982. ‘Methods of Correction for Systematic Error in Point Precipitation Measurement for Operational Use’, Operational Hydrology Report No. 21, Geneva, 91 pp.

Yamazaki, T. 1995. ‘The influence of forests on atmospheric heating during the snowmelt season’, J. Appl. Meteor. 34, 511-519.

Yang, Z.-L., Dickinson, R.E., Robock, A., and Vinnikov, K.Ya. 1997. ‘Validation of the snow sub-model of the Biosphere-Atmosphere Transfer Scheme with Russian snow cover  and  meteorological observational data’, J. Clim., 10, 353-373.

Yeh, T.-C., Wetherald, R.T., and Manabe, S. 1983. ‘A model study of the  short-term  climate  and  hydrologic  effects of sudden snow-cover removal’,  Mon. Wea. Rev., 111, 1013-1024.


 

Table 1. List of snow models (in alphabetical order).

 

Model Name

Application

Retention &

percolation

Snow-vegetation

interaction

Sub-grid

topography

Reference

Australian

Downscaling GCM output

No

No

No

Hewitt et al. (1997, personal communication)

BAIM (Biosphere-Atmosphere Interaction Model)

GCM and RCM

Yes

Yes

No

Mabuchi et al. (1997)

BASE (Best Approximation of Surface Exchange)

GCM

No

Yes

No

Desborough and Pitman (1998)

BATS (Biosphere-Atmosphere Transfer Scheme)

GCM and RCM

No

Yes

No

Dickinson et al. (1993); Yang et al. (1997)

CLASS

GCM, RCM and NWPM

Yes

Yes

No

Verseghy (1991)

CROCUS

Understanding snow processes, operational avalanche forecasting in France, and for use in GCM

Yes

No

No

Brun et al. (1989)

DHSVM (Distributed Hydrology Soil Vegetation Model)

GCM

Yes

Yes

Yes

Wigmosta et al. (1994)

DARSSM (Division of Atmospheric Research Snow and Soil Model)

Understanding snow processes, forecasting runoff

No

Yes

No

Kowalczyk (1999)

ECHAM Snow Energy and Mass Budget Model

GCM and RCM

No

Yes

No

Roeckner et al. (1996)

ECHAM Multi-Layer Snow Model

Testing the optimal complexity of a snow cover model for GCM and RCM

Yes

Yes

No

Loth et al. (1993)

GFDL Snow Model

GCM

No

No

No

Manabe (1969)

Hadley Centre/UKMO GCM Land Surface Model

GCM and NWPM

No

Yes

No

Essery (1997)

HBV

Forecasting runoff and hydropower operation

No

Yes

Yes

Bergstrom (1997)

IAP94

GCM

Yes

Yes

No

Dai and Zeng (1997)

IHACRES Snow Model

Forecasting runoff, flow (real time), and estimating possible climate change impacts

No

No

Yes

Schreider et al. (1997)

INM (Snow Model of the Instituto Nacional de Meteorologia)

Forecasting runoff

Yes

No

No

Fernadez (1999)

ISBA (Interactions Soil- Biosphere-Atmosphere)

GCM and NWPM

No

Yes

No

Douville et al. (1995)

Layered Snow Model for Climate Study

Understanding snow processes, forecasting runoff and for use in GCM

Yes

Yes

No

Sun et al. (1999), Jin et al. (1999)

MAPS/RUC Soil-Vegetation-Snow Model

NWPM

No

No

No

Smirnova et al. (1997)

Mosaic

GCM

No

Yes

No

Koster and Suarez (1996)

MRI-CGCM Ground Hydrology Model

GCM

No

No

No

Tokioka et al. (1995)

Melbourne University Snow Model (MU-SNW)

NWPM and GCM

No

Yes

Yes

Walland and Simmonds (1996)

NCEP/OH/OSU CAPS

NWPM

Yes

Yes

No

Koren et al. (1999)

NWSRFS SNOW-97

Forecasting runoff

No

No

No

Anderson (1973)

RAMS Snow Model

Forecasting runoff

No

No

No

Lofgren (1997, personal communication)

RGM (Regional Geosystem Model)

Understanding snow processes, forecasting runoff, and risk assessment of snow hazards

Yes

No

Yes

Scherer (1997, personal communication)

SEMS (Snow Evolution Modeling System)

Understanding snow processes, NWPM and RCM

Yes

Yes

Yes

Liston and Sturm (1998)

SHAW (Simultaneous Heat and Water Model)

Understanding snow/frozen soil/surface energy balance processes

Retention: yes;

Percolation: no

Yes

No

Flerchinger and Saxton (1989)

SLURP

Forecasting runoff

No

Yes

Yes

Kite (1995)

SNAP (Snowmelt Numerical-Analytical Package)

Understanding snow processes and forecasting runoff

Yes

No

No

Albert and Krajeski (1998)

SNOWPACK

Understanding snow processes, forecasting runoff and avalanche warning

Yes

No

No

Lehning et al. (1998)

SNTHERM

Understanding snow processes and forecasting runoff

Yes

No

No

Jordan (1991)

SNTHERM (spatially distributed)

Understanding snow processes and forecasting runoff

Yes

Yes

Yes

Davis et al. (1998)

SNTHERM.ver4

Understanding snow processes

Yes

No

No

Stamnes et al. (1988)

SOIL

Understanding snow and frozen soil processes

Retention: yes;

Percolation: no

Yes

No

Johnsson and Lundin (1991)

Special Sensor Microwave Imager (satellite) Derived Snow Cover Model

Forecasting runoff, GCM, NWPM and climate monitoring

Yes

Yes

No

Grody and Basist (1996)

SPONSOR

GCM, RCM and NWPM

Yes

Yes

No

Shmakin (1997, person communication)

SPS (Soil-Plant-Snow)

Understanding snow processes, RCM and NWPM

No

No

No

Kim and Ek (1995)

SSiB (simplified Simple Biosphere Model)

GCM and NWPM

No

Yes

No

Xue et al. (1991)

TSCM1 (Tohoku Snow Cover Model with One-Layer)

Understanding snow processes and forecasting runoff

Yes

No

Yes

Yamazaki (1995)

TSCMM (Tohoku Snow Cover Model with Multi-Layer)

Understanding snow processes and forecasting runoff

Yes

No

No

Yamazaki (1997, personal communicatoion)

UEB (Utah Energy Balance Snow Accumulation and Melt Model)

Understanding snow processes and for runoff, erosion and water balance forecasting and modeling

Yes

No

Yes

Tarboton and Luce (1996)

 


 

Appendix Snow Model Questionnaire

 

1. Please write down the name (and abbreviation) of your snow model

   or land-surface model with snow component?

 

2. Name and address of model developer;

 

3. Name and address of model user;

 

4. Please indicate whether your model is developed for application

 

   in understanding snow processes,

   in a runoff forecasting model,

   in a weather forecasting model,

   in a global climate model (GCM),

   or other (please specify)?

 

5. The first year when the model was used;

 

6. One paragraph description of your model (e.g. abstract from report or

   paper);

 

7. Please specify any known application range or restrictions;

 

8. What are the development data needs;

 

9. What are the operational data needs?

 

10.Please indicate with an "x" for those meteorological variables used to

   DRIVE your snow model?

   precipitation                   :

   air temperature                 :

   wind speed                      :

   wind direction                  :

   humidity                        :

   downwelling shortwave radiation :

   downwelling longwave  radiation :

   cloud cover                     :

   surface pressure                :

 

11. List the state variables (e.g., snow temperature, snow water

    equivalent, etc) your snow model uses?

 

12. List the measurable/adjustable parameters (e.g., snow surface

    aerodynamic roughness, maximum albedo at visible wavelength, etc,

    excluding initial conditions) your snow model uses?

 

13. What are the output data?

 

14. What computer language does your model use?

 

15. How many subroutines (or functions) does your snow model have?

 

16. Number of lines of the snow code?

 

17. What is the recommended hardware?

 

18. How does your model determine the form of precipitation

    (i.e., snowfall and rainfall)?

    Please give the formulation.

 

19. Is your snow model one dimensional or multi-dimensional?

    Please specify.

 

20. If one dimensional, how many layers are there in your snow model?

    Please specify layering structure.

 

21. What is your snow model time step?

 

22. Does your model snow albedo allow its

    spectral differences    (visible vs. near-IR)?

    directional differences (direct  vs. diffuse)?

 

23. Is your model snow albedo a function of

      snow age

      grain size

      solar zenith angle

      pollution

      snow depth?

 

24. Does your snow model explicitly treat liquid water retention and

    percolation within the snowpack?

 

25. Does your snow model account for changes in the hydraulic and thermal

    properties of snow due to meltwater refreezing?

 

26. Is snow density in your snow model changing with time or fixed?

 

27. Is heat capacity and conductivity in your snow model changing with

    time or fixed?

 

28. Does your snow model simulate vapor transfer in the snowpack?

 

29. Does your snow model account for the heat transfer between the bottom

    of the snowpack and the underlying soil?

 

30. In snow energy balance, does your model consider heat convected by

    rain or falling snow?

 

31. Does your snow model include snow drifting and redistribution by wind

    (or avalanche)? If so, how?

 

32. How is areal snow distribution treated?

 

33. Does your snow model account for sub-grid (or sub-watershed) effects

    of topography? If so, how is temperature distributed?

   

           how is precipitation (spatial, elevation and corrections)

                  distributed?

           how is solar radiation distributed?

           how is wind distributed?

           how are other meteorological variables distributed?

 

34. Does your snow model consider snow-vegetation interaction?

 

35. Does the snow-vegetation interaction account for

    different vegetation types     (grass vs. forest),

    different vegetation heights   (short vs. tall),

    different vegetation densities (small vs. large LAI),

    different vegetation coverages (sparse vs. dense vegetation)?

 

36. Are snow interception, drip and melt on canopy surface allowed

    in your model?

 

37. How is the upper limit of the canopy interception determined?

 

38. In the presence of vegetation, how is snow surface albedo altered?

 

39. In the presence of vegetation, how is snow surface roughness altered?

 

40. In the presence of forest, does your snow model allow spatial

    variability of snow depth and water equivalent on forest floor?

 

41(a). How does your model deliver snowmelt to the soil system

       (e.g. affecting soil moisture)?

 

  (b). Once snowmelt is generated, how does your model relate it to

       runoff?

 

42. How is frozen soil treated in your model?

 

43. Has your snow model been tested with the field data?

 

    Is so, what data?

    what are their temporal and spatial scales?

 

44. Has your snow model been used together with remote sensing data as

    input?

 

    If so, how?

 

45. If your snow model is coupled with a numerical weather forecasting

    model or climate model, has the model snow product been compared

    with satellite data?

 

    If so, what satellite data were used?

 

46. Please list any other previous applications.

 

47. Please specify verification criteria, if any?

 

48. What are the model fitting procedures, if any?

 

49. What are future plans for using/improving the model?

 

50. Please provide references relevant to the model description and use.