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

     Land-surface model with snow component:

     The NCEP/OH/OSU CAPS Model

      (specifically, the NCEP/OH extended land-surface model of
       Oregon State University, known as CAPS)

2. Name and address of model developer;

     Kenneth Mitchell (Kenneth.Mitchell@noaa.gov) (Ph: 301-763-8161, x7225)
     Environmental Modeling Center
     National Centers for Environmental Prediction
     W/NP2, Room 204
     4700 Silver Hill Road
     Stop 9910
     Washington DC 20233-9910

     Co-developers (entire land-surface model):

     NCEP: Fei Chen (fchen@sun1.wwb.noaa.gov)
     NCEP: Hua-Lu Pan (wd23ph@sun1.wwb.noaa.gov)
     OSU:  Michael Ek (ek@ats.orst.edu)
     OH:   Victor Koren (Victor.Koren@noaa.gov)
     OH:   Qingyun Duan (Qingyun.Duan@noaa.gov)
     OH:   John Schaake (John.Schaake@noaa.gov)

3. Name and address of model user;

     Environmental Modeling Center
     National Centers for Environmental Prediction
        (address as in Question 2)

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   
           (i.e. in the NCEP Mesoscale Eta Model)
   in a global climate model (GCM),     
   or other (please specify)?           

5. The first year when the model was used;

Operational use began in January 1996

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

The NCEP/OH/OSU CAPS model is a multi-layer soil model (2-4 soil layers typically) with an explicit seasonal cycle of vegetation, plus snowpack physics. The prognostic state variables are soil moisture and temperature in each soil layer, skin temperature, snowpack water, and canopy water. A single skin temperature is obtained from a single surface energy balance equation for the combined ground/vegetation/snowpack surface. The evaporation in the energy balance is a combination of three evaporation components: 1) direct from bare soil, 2) transpiration through plant canopy, and 3) canopy water evaporation. The weighting of these three evaporation components is controlled by a specified monthly annual cycle of NDVI-based green vegetation fraction, derived by NOAA/NESDIS from AVHRR satellite data. The vegetation resistance treatment is similar to that of Jarvis/Noilhan (e.g. in the French ISBA model). The soil hydraulics is governed by Darcy's Law, including both a hydraulic conductivity and diffusivity, which vary with soil moisture content.

7. Please specify any known application range or restrictions;


8. What are the development data needs;

Similar to what is provided in the PILPS Phase II, i.e. observed surface forcing and observed surface characteristics, plus validating observations of surface fluxes, snowdepth, soil moisture, soil temperature, and runoff (streamflow).

9. What are the operational data needs?

Surface forcing data at 30-minute intervals or better (precipitaion, surface radiation, air temperature, humidity, wind speed, pressure), plus a daily input snowdepth analysis.

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?

     snowpack water
     skin temperature (single effective for the ground, vegetation, snow)
     soil moisture in each soil layer (2-4)
     soil temperature in each soil layer (2-4)
     canopy water

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?

     For snow component only:

     Initial snowpack water
     Maximum visible albedo for deep snow (.55)
     Assumed snow density ratio 10 (ratio of snowdepth to water equivalent)
     Maximum skin temperature when snow melting (273.16 K)

13. What are the output data?

     For snow component model, the output data are:

     snowpack water equivalent
     skin temperature

     For entire land-surface model:

     All state variables in Question 11, plus all terms in the
     surface water and energy budget.

14. What computer language does your model use?


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

     The land-surface model as a whole has 21 subroutines.
     The snow component has one primary subroutine and
     significant parts of 3-4 other subroutines.

16. Number of lines of the snow code?

     The land-surface model as a whole has 2400 lines of code,
     including comment lines.  The snow component has about
     250 lines of code.

17. What is the recommended hardware?

UNIX workstation or UNIX mainframe (e.g. CRAY)

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

If the lowest level near-surface air temperature is at or below freezing (273.16), we assume the 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.

Only one layer in snowpack. Two to four soil layers.

21. What is your snow model time step?

     We have run it with time steps varying from
     10 minutes to 60 minutes.

     The typical land-surface/snow-component time step in
     the coupled mesoscale NWP model is 10 minutes.

22. Does your model snow albedo allow its

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

No such allowances, rather a single broadbrand albedo.

23. Is your model snow albedo a function of

      snow age                 
      grain size                
      solar zenith angle       X 
      snow depth?              X

Our snow albedo is also a function of fraction of vegetation.

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?

FIXED. See question 12.

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?

It is always assumed 100 percent areal coverage for all snow depths, even shallow snow.

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?

YES, but only insofar as presence of vegetation modifies the dependence of albedo on snowdepth.

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),          X
 different vegetation coverages (sparse vs. dense vegetation)?  X

36. Are snow interception, drip and melt on canopy surface allowed in your model?

No - our canopy interception treatment is not modified in the presence of snowfall

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

It is a function of LAI, but is not altered in the presence of snowfall.

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

The snow albedo is reduced as a function of the vegetation fraction.

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

We do not alter surface roughness in any way owing to snowpack effects.

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)?

It is added to other ground surface infiltration sources (such as nonfrozen precip or drip from canopy water).

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

As described in 41.a, all sources of surface liquid water (unfrozen precip, canopy drip, snowmelt) are added together and this total enters a surface infiltration algorithm, with determines what fraction enters the top soil layer and what fraction enters the surface runoff (as a function of soil moisture and precipitation rate).

42. How is frozen soil treated in your model?

No frozen soil effects at this time. (See Question 49)

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

If so, what data? (areas)

what are their temporal and spatial scales?

     Only limited testing of the snow component thus far, namely with the
     PILPS 2d, Valdai, Russia data.  The latter is a single site case
     spanning about 18 years with 30-minute forcing data, and validating
     data of soil moisture, snowdepth, and runoff.

     We (OH) are now preparing forcing and validating data for
     the Root River in Minnesota.

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

If so, how?

YES - In our operational NWP application, we initialize the snowdepth daily over our N. American Eta model domain using the operational, daily, 47-km, N. Hemisphere snowdepth analysis produced by the U.S. Air Force at Air Force Global Weather Center (AFGWC). The latter daily analysis product makes heavy use of visible and microwave satellite inferences of snowcover. The technical point of contact for this Air Force snowdepth product is Tom Kopp (koppt@afgwc.offutt.af.mil).

Because of the above daily update from an independent snow analysis prodedure, we have not deemed it necessary to introduce complex snowpack physics in the NCEP mesoscale Eta model, since the latter model is only executed over a 48-hour forecast. If we were running in a longer range forecast model (say weekly, monthly, seasonal, or annual), there would be stronger justification for advancing the physical treatment of our snowpack component.

During the winter of 1997-1998, we plan to replace the AFGWC daily snowdepth update with a newly developed, finer resolutuion daily snowcover product of NESDIS. This latter new product can be inspected at http://www.ssd.noaa.gov/SSD/ML/realtime.html. The technical point of contact for the latter is Bruce Ramsay of NESDIS/IPB (bramsay@nesdis.noaa.gov).

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?

See Question 44.

46. Please list any other previous applications.

Besides the coupling to the NCEP mesoscale Eta model, the NCEP/OSU land-surface scheme has been executed in uncoupled 1-D mode (single site) for ISLSCP/FIFE and PILPS 2a, 2b, and 2d; and in 2-D mode (area) in PILPS 2c and the ISLSCP Global Soil Wetness Project (GSWP).

47. Please specify verification criteria, if any?

In coupled NWP mode, the fundamental verification criteria are the forecast versus observed 2-meter shelter air temperature and the vertical atmospheric profile ("sounding") in the lowest 3 kilometers.

In the uncoupled mode, the fundamental verification criteria are the surface latent heat flux, sensible heat flux, skin temperature and runoff/streamflow.

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

The primary fitting procedures are to run in an uncoupled mode over many annual cycles (e.g. PILPS 2c and 2d) and to match the observed monthly runoff/streamflow and daily surface evaporation by adjusting parameters related to the infiltration, runoff, hydraulic conductivity, baseflow drainage, and canopy resistance.

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

     1) Adding frozen soil effects on a) surface infiltration
     of rainfall or snowmelt, b) soil thermodynamics,
     and c) canopy transpiration.

     2) Allowing for fractional snowcover percentages of less than
     one hundred percent and concurrently allowing skin temperatures
     to rise above 273 K.

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

Without snow component (see snow component later at bottom):

Chen, Fei, et al., 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophysical Research, 101, No D3, 7251-7268.

Chen, Fei, Zavisa Janjic, Kenneth Mitchell, 1997: Impact of Atmospheric Surface-layer Parameterization in the new Land-surface Scheme of the NCEP Mesoscale Eta Model. Accepted in Boundary-Layer Meteorology.

Betts, A., F. Chen, K. Mitchell, Z. Janjic, 1997: Assessment of the land-surface and boundary-layer models in two operational versions of the NCEP Eta model using FIFE data. Accepted in Monthly Weather Review.

Ismail, Y., J. Shuttleworth, J. Washburne, and Fei Chen, 1997: Evaluating the Eta model derived data against observations. Submitted to Monthly Weather Review.

With snow component:

Ek, M., and L. Mahrt, 1991: OSU 1-D PBL Model User's Guide, A one-dimensional planetary boundary layer model with interactive soil layers and plant canopy. Version 1.0.4. March 1991. Available from Dept of Atmospheric Sciences, Oregon State University.

-- Last updated Fri Oct 8 12:47:54 MST 1999 by Zong-Liang Yang.
For questions and comments, please contact Zong-Liang Yang