1. Please write down the name (and abbreviation) of your snow model or land-surface model with snow component?

BATS (Biosphere-Atmosphere Transfer Scheme)

2. Name and address of model developer;

Robert E. Dickinson
School of Earth and Atmospheric Sciences  
Georgia Institute of Technology
Atlanta, GA  30332-0340
Phone:  404 894-3991  
Fax:    404 894-5638

3. Name and address of model user;

Zong-Liang Yang
University of Arizona Land-surface modeling group
National Center for Atmospheric Research (CCM1, CCM2, CCM3 and RegCM2)

BATS is a public model available on the WWW server (http://www.atmo.arizona.edu/bats/batsmain.html) which has been distributed to over 100 users. It is currently being used on numerous projects.

4. Please indicate whether your model is developed for application

   in understanding snow processes,
   in a runoff forecasting model,	
   in a weather forecasting model,      X
   in a global climate model (GCM),     X
   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);

In brief, BATS has a single vegetation canopy layer overlying a three-layer soil model. Treatment of snow in BATS satisfies all water and energy balance constraints. Precipitation is deposited as snow when the surface air temperature lies below 2.2 degree C. Snow and soil are lumped together for computing surface and subsurface temperatures based on an analytical ``force-restore'' approach. The BATS snow model simulates explicitly only the snow-surface processes. There is no explicit distinction between subsurface snow versus soil temperature, i.e., a subsurface temperature refers, in principle, to a subsurface snow temperature after more than a few centimeters of liquid equivalent snow have accumulated. Water incident on the snow surface is assumed to go directly into the soil. The melting at the bottom of the snowpack due to heat conducted from the ground (ground melt) is also implicitly neglected unless this heat reaches the top snow surface. The model simulates the snow aging and its impact on surface snow density and albedo. The latent heat of fusion in the surface energy balance is considered. Snowmelt is computed from the energy balance and during melt surface temperature remain at 0 degree C. The melt water is immediately removed from the snowpack. The fractional areal coverage of snow is parameterized as a function of snow depth and surface roughness.

7. Please specify any known application range or restrictions;

The model is developed to study land surface-climate interaction at GCM grid scales, so many detailed features of the surface processes are neglected.

8. What are the development data needs;

Detailed measurements of snow depth, water equivalent, albedo and temperature both at point and on the large scales.

9. What are the operational data needs?

Incoming solar and longwave radiation or cloud cover, precipitation, wind speed, air temperature and relative humidity for upper boundary.

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                  : 
   humidity                        : X
   downwelling shortwave radiation : X
   downwelling longwave  radiation : X
   cloud cover                     :
   surface pressure                : X

11. List the state variables (e.g., snow temperature, snow water equivalent, etc) your snow model uses?

state variables in conservation equations are: snow surface temperature, deep snow temperature, snow water equivalent, snow age.

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 aerodynamic roughness for momentum, heat and moisture.
    height of instrument readings
    maximum albedo at visible wavelength
    maximum albedo at near infrared wavelength
    fresh snow density,
    maximum snow density.

13. What are the output data?

    Snow surface temperature
    Deep snow temperature
    Snow depth
    Snow water equivalent
    Snow cover fraction
    Snow sublimation
    Snow surface albedo

14. What computer language does your model use?


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

7 (some routines are just for snow, some routines are also for other processes)

16. Number of lines of the snow code?


17. What is the recommended hardware?

PC or UNIX computers

18. How does your model determine the form of precipitation (i.e., snowfall and rainfall)? Please give the formulation.

The model defaults to rain when the air temperature exceeds 2.2 C. Otherwise, precipitation is snowfall.

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.

One layer.

21. What is your snow model time step?

5 minutes to 3 hours, mostly 20-30 minutes.

22. Does your model snow albedo allow its

    spectral differences    (visible vs. near-IR)? Yes.
    directional differences (direct  vs. diffuse)? Yes.

23. Is your model snow albedo a function of

      snow age                 X
      grain size               X (implicit)
      solar zenith angle       X
      pollution                X
      snow depth?              X

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?

Accumulation: 0nly if accomplished in precipitation data stream.

32. How is areal snow distribution treated?

Through areal snow cover fraction.

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),            Yes
 different vegetation heights   (short vs. tall),              Yes
 different vegetation densities (small vs. large LAI),         Yes
 different vegetation coverages (sparse vs. dense vegetation)? Yes

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?

0.1 mm x (LAI+SAI)* FVEG
where LAI: Leaf area index
      SAI: Stem area index
      FVEG: frational vegetation cover fraction

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

GCM grid box surface albedo depends on snow surface albedo (computed) and vegetation-snow masking area.

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

GCM grid box surface neutral coefficient depends on snow surface roughness (prescribed) and vegetation-snow masking area.

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)?

Snowmelt is added to the throughfall (i.e., rainfall after the canopy interception) for the water input to the surface soil layer.

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

The surface runoff is a function of the net surface water input (i.e. the above water input minus soil surface evaporation) and the average soil moisture condition. When the surface temperature is below freezing, the surface runoff is enhanced.

42. How is frozen soil treated in your model?

It is included in a very conceptual way: i.e. (1) when computing surface runoff (see 41b), (2) when computing the soil thermal properties (increasing the thermal heat capacity), (3) when computing surface evaporation and the soil moisture transport within the soil column.

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

    If so, what data?
    what are their temporal and spatial scales?

Yes. BATS snow model has been tested with the snow data (six Russian stations, Valdai station, the Mammoth Mountain, BOREAS site). Temporal: 6 years for the six Russian stations (3-hourly interval), about 19 years for Valdaii (three-hourly interval, interpolated to 30 minutes), 2 years for the Mammoth Mountain (20 minutes), 3 years for BOREAS site (15 minutes). Spatial: stational.

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?

Yes, with the NESDIS data and the data from Robinson et al. (1993).

46. Please list any other previous applications.

In off-line mode: BATS has been used in all phases of PILPS runs. In on-line mode: BATS is linked with the NCAR regional climate model (MM4) and with the NCAR CCM1, CCM2 and CCM3

47. Please specify verification criteria, if any?

In off-line mode: the performance is evaluated by comparing against whatever data available, such as observed soil moisture, snow mass, albedo, streamflow, latent and sensible fluxes. In on-line mode: the performance is evaluated by comparing against the satellite data of snow cover, and climatology of precipitation, surface air temperature and runoff.

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

If results from above comparisons do not match, (manual) adjustments are made to some of the BATS parameters.

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

Subgrid-scale variability of topography; snow-vegetation masking; multi-layer snow column model; frozen soil dynamics

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

There are over 80 publications from using BATS. The most recent papers about BATS snow model and its evaluation using observed data collected from Former Soviet Union are

Yang, Z.-L., R.E. Dickinson, A. Robock and K.Y. Vinnikov, 1997: Validation of the snow sub-model of the Biosphere-Atmosphere Transfer Scheme with Russian snow cover and meteorological observational data, Journal of Climate, 10, 353-373, 1997.

Yang, Z.-L., R.E. Dickinson, W.J. Shuttleworth and M. Shaikh, 1998: Treatment of Soil, Vegetation and Snow in Land-surface Models: A Test of the Biosphere-Atmosphere Transfer Scheme with the HAPEX-MOBILHY, ABRACOS, and Russian Data, invited to a BAHC Special Issue of Journal of Hydrology,, 212-213, 109-127. [Abstract]

The latter paper also compared the snow outputs from a version of NCAR GCM with satellite observations.

Other related papers are:

Jin, J.M., X. Gao, S. Sorooshian, Z.-L. Yang, R.C. Bales, R.E. Dickinson, S.-F. Sun and G.-X. Wu, 1999: One-dimensional snow water and energy balance model for vegetated surfaces, Hydrological Processes, 13, Issue 14-15, 2467-2482, 1999. [Abstract]

Jin, J.M., X. Gao, Z.-L. Yang, R.C. Bales, S. Sorooshian, R.E. Dickinson, S.-F. Sun and G.-X. Wu, 1999: Comparative analyses of physically based snowmelt models for climate simulations. Journal of Climate, 12, 2643-2657, 1999. [Abstract]

Schlosser, C.A., A. Slater, A. Robock, A.J. Pitman, K.Y. Vinnikov, A. Henderson-Sellers, N.A. Speranskaya, K. Mitchell, A. Boone, H. Braden, P. Cox, P. DeRosney, C.E. Desborough, Y.J. Dai, Q. Duan, J. Entin, P. Etchevers, N. Gedney, Y.M. Gusev, F. Habets, J. Kim, E. Kowalczyk, O. Nasanova, J. Noilhan, J. Polcher, A. Shmakin, T. Smirnova, D. Verseghy, P. Wetzel, Y. Xue and Z.-L. Yang, 1999: Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS Phase 2(d), Mon. Wea. Rev. , (in press).

Yang, Z.-L., R.E. Dickinson, A.N. Hahmann, G.-Y. Niu, M. Shaikh, X. Gao, R.C. Bales, S. Sorooshian and J.M. Jin, 1999: Simulation of snow mass and extent in global climate models, Hydrological Processes, 13, Issue 12-13, 2097-2113, 1999. [Abstract]

Yang, Z.-L., G.Y. Niu and R.E. Dickinson, 1999: Comparing snow simulations from NCAR LSM and BATS using PILPS 2d data, Preprints, 14th Conference on Hydrology, American Meteorological Society Meeting, Dallas, TX, USA, January 1999, pp. 316-319. [Abstract]

Yang, Z.-L. and G.Y. Niu, 2000: Snow-climate interaction in NCAR CCM3, Preprints, 15th Conference on Hydrology, American Meteorological Society Meeting, Long Beach, CA, USA, January 2000. [Abstract]

Yang, Z.-L., Description of recent snow models, Book Chapter, in Snow and Climate, E. Martin and R. Armstrong (editors), International Committee on Snow and Ice, 2004 (in press).

A detailed list can be found from http://www.atmo.arizona.edu/bats/batsrefs.html.
-- Last updated Fri Oct 8 12:47:54 MST 1999 by Zong-Liang Yang.
For questions and comments, please contact Zong-Liang Yang