1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?
Snow Evolution Modeling System (SEMS)
2. Name and address of model developer;
Dr. Glen E. Liston
Department of Atmospheric Science
Colorado State University
Fort Collins, CO 80523-1371
Internet: liston@iceberg.atmos.colostate.edu
Voice: (970) 491-7473
FAX: (970) 491-8449
3. Name and address of model user;
Dr. Glen E. Liston
Department of Atmospheric Science
Colorado State University
Fort Collins, CO 80523-1371
Internet: liston@iceberg.atmos.colostate.edu
Voice: (970) 491-7473
FAX: (970) 491-8449
4. Please indicate whether your model is developed for application
in understanding snow processes, X
in a runoff forecasting model,
in a weather forecasting model, X
in a global climate model (GCM),
or other (please specify)? X
regional climate model
5. The first year when the model was used;
1990
6. One paragraph description of your model (e.g. abstract from report or paper);
This model is a system of coupled sub-models which simulate the time evolution of 3-dimensional snow-depth distributions. The blowing and drifting snow submodel includes an accounting of the relevant snow transport mechanisms, such as saltation and turbulent suspension, surface shear stress modifications in the presence of saltation, sublimation of the blowing and drifting snow, wind field adjustments for topography, and snow-vegetation interactions are included through a vegetation snow-holding capacity. When driven with observed or modeled atmospheric forcing, this submodel describes how the winter snow cover accumulates and is redistributed by the interactions of wind and topography. In addition, it provides an accounting of the snow-pack losses due to the sublimation of the wind-transported snow. The energy-balance melt submodel is available to simulate snow-melt related processes. Variations of the modeling system are being incorporated into the Regional Atmospheric Modeling System (RAMS) for weather and climate simulation purposes.
7. Please specify any known application range or restrictions;
none.
8. What are the development data needs;
atmospheric forcing variables (see 10 below), vegetation, and topography data.
9. What are the operational data needs?
atmospheric forcing variables (see 10 below), vegetation, and topography data.
10. Please indicate with an "x" for those meteorological variables used to
DRIVE your snow model?
precipitation : X
air temperature : X
wind speed : X
wind direction : X
humidity : X
downwelling shortwave radiation : X (if not provided, then computed)
downwelling longwave radiation : X (if not provided, then computed)
cloud cover : X (if not provided, then computed)
surface pressure : X (if not provided, then computed)
11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?
snow temperature
snow water equivalent
snow density
surface friction velocity
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?
snow surface roughness
snow albedo
threshold shear velocity
13. What are the output data?
time evolution of 3-dimensional snow distribution
wind flow field in complex terrain
14. What computer language does your model use?
FORTRAN
15. How many subroutines (or functions) does your snow model have?
about 55
16. Number of lines of the snow code?
about 4000
17. What is the recommended hardware?
The model is generally run on UNIX workstations.
18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.
A temperature criterion determines whether liquid precipitation is converted to snow.
19. Is your snow model one dimensional or multi-dimensional? Please specify.
One-dimensional energy balances, 3-dimensional snow distributions, that evolve in time.
20. If one dimensional, how many layers are there in your snow model? Please specify layering structure.
Multi-layers, layers defined depending on application; usually identified by a continuous precipitation event.
21. What is your snow model time step?
Determined by individual application; typically 30 minutes to a day.
22. Does your model snow albedo allow its
spectral differences (visible vs. near-IR)? No.
directional differences (direct vs. diffuse)? Yes.
23. Is your model snow albedo a function of
snow age X
grain size X
solar zenith angle X
pollution
snow depth?
24. Does your snow model explicitly treat liquid water retention and percolation within the snowpack?
Yes.
25. Does your snow model account for changes in the hydraulic and thermal properties of snow due to meltwater refreezing?
Yes.
26. Is snow density in your snow model changing with time or fixed?
Changing with time.
27. Is heat capacity and conductivity in your snow model changing with time or fixed?
Changing with time.
28. Does your snow model simulate vapor transfer in the snowpack?
No.
29. Does your snow model account for the heat transfer between the bottom of the snowpack and the underlying soil?
No.
30. In snow energy balance, does your model consider heat convected by rain or falling snow?
No.
31. Does your snow model include snow drifting and redistribution by wind (or avalanche)? If so, how?
Yes.
32. How is areal snow distribution treated?
It is computed as part of the model integration.
33. Does your snow model account for sub-grid (or sub-watershed) effects of topography?
Depends on the application. The approach and complexity of computing the various distributions listed below also depends on the application; and can range from solving simple empirical functions to solving the full Navier-Stokes equations.
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?
Yes.
35. Does the snow-vegetation interaction account for
different vegetation types (grass vs. forest),
YES.
different vegetation heights (short vs. tall),
YES.
different vegetation densities (small vs. large LAI),
No.
different vegetation coverages (sparse vs. dense vegetation)?
No.
36. Are snow interception, drip and melt on canopy surface allowed in your model?
Yes.
37. How is the upper limit of the canopy interception determined?
Through a pre-defined snow-holding capacity.
38. In the presence of vegetation, how is snow surface albedo altered?
Depends on the type of vegetation.
39. In the presence of vegetation, how is snow surface roughness altered?
Depends on the type of vegetation.
40. In the presence of forest, does your snow model allow spatial variability of snow depth and water equivalent on forest floor?
No.
41(a). How does your model deliver snowmelt to the soil system (e.g. affecting soil moisture)?
Directly input into the upper soil layers.
(b). Once snowmelt is generated, how does your model relate it to runoff?
This is handled by the soil model.
42. How is frozen soil treated in your model?
It is not.
43. Has your snow model been tested with the field data?
Yes.
If so, what data? (areas)
Mostly Arctic Alaska data sets collected by Sturm and Liston.
what are their temporal and spatial scales?
Widely ranging from point to ~200km, and from hourly to annual cycle.
44. Has your snow model been used together with remote sensing data as input?
No.
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?
No.
46. Please list any other previous applications.
47. Please specify verification criteria, if any?
Depends on application.
48. What are the model fitting procedures, if any?
49. What are future plans for using/improving the model?
Continued development, validation, and application.
50. Please provide references relevant to the model description and use.
Liston, G. E., and M. Sturm, 1997: A snow transport model for complex terrain. J. Glaciol., submitted.
Liston, G. E., 1995: Local advection of momentum, heat, and moisture during the melt of patchy snow covers. J. Applied Meteorol., 34 (7), 1705-1715.
Liston, G. E., and D. K. Hall, 1995: An energy balance model of lake ice evolution. J. Glaciol., 41 (138), 373-382.
Sturm, M., J. Holmgren, and G. E. Liston, 1995: A seasonal snow cover classification system for local to global applications. J. Climate, 8 (5), 1261-1283.
Liston, G. E., and M. Sturm, 1996: Modeling the seasonal evolution of non-uniform Arctic snow covers in regional atmospheric models. Modeling the Arctic System: A Workshop Report on the State of Modeling in the Arctic System Science Program, ARCSS Modeling Workshop, January 15-16, Boulder, Colorado.
Liston, G. E., 1995: Parameterizing the influence of subgrid-scale snow distributions during snow melt in regional atmospheric models. Summary Report and Proceedings, International GEWEX Workshop on Cold-Season/Region Hydrometeorology, May 22-26, Banff, Alberta, Canada, 208.
Liston, G. E., R. A. Pielke, T. G. F. Kittel, L. Lu, J. H. Copeland, 1996: Formulating the regional atmospheric modeling system (RAMS) for use in regional climate studies. Third Annual Central Plains Experimental Range (CPER) LTER Symposium, January 11, Fort Collins, Colorado.
Liston, G.E., 1998: Example applications of a 3-d snow-transport model (SnowTran-3D), EOS, Transactions, American Geophysical Union, Fall Meeting, Supplement, Vol. 79, No. 45, page F269.