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Widespread benefits are seen in the northern hemisphere winter troposphere, particularly the Rocky Mountains and Tibetan Plateau areas.  Results show in improvements in geopotential height, vector wind and humidity forecasts.

Forecasts of 2m air temperature at 2m use the skin temperature of the snow as if it were at the model ground surface ( rather than at the elevation of the snow surface).  This may lead to errors in forecast 2m temperatures in cases of deep snow.  See also Section 9.2 Derivation of 2m temperatures.

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The IFS multi-layer snow model uses up to five layers to represent the snowpack and the complex heat fluxes and interactions between them.   It represents the vertical structure and evolution of snow temperature, snow mass,  densitydensity, and liquid water content in each layer.  The energy flux at the top of the snowpack is snowpack surface is the balance of the upward and downward energy fluxes at the snow surface including , including the effect of any snow evaporation.     Albedo and surface fluxes vary according to the snow extent, depth and ground coverage (with account taken of trees in areas of forest), and age of the snow.  Heat flux from the underlying ground is also incorporated.  The fluxes are illustrated and explained in Fig2.1.4.4-1, Fig2.1.4.4-2, Fig2.1.4.4-3.

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• an Optimal Interpolation method which adjusts the model-analysed snow water equivalent and snow density prognostic variables.
• conventional measurements of snow depth (from SYNOP and other national networks) with additional national snow depth observations, particularly in Europe and North America.  These are generally an important and reliable source of information.  However, snow depth observations from many other regions of the world remain unavailable to IFS.  Thick hoar frost (which can look like a dusting of snow) can be incorrectly reported as very shallow snow.  This can be assimilated by the model despite no supporting evidence from other sources.  
• snow extent data from the NOAA/NESDIS Interactive Multi-sensor Snow and Ice Mapping System (IMS).  This combines satellite visible and microwave data with weather station reports.  It gives snow cover information and sea ice extent over the northern hemisphere at 4km resolution.  There is some manual intervention and quality control.  The IMS product only shows where at least 50% of the grid cell is covered by snow and .  This is converted to snow depths using relationships shown in  Fig2.1.4.4-7 and Fig2.1.4.4-8.  IMS data is not currently used by the IFS at altitudes above 1500m.
• SNOTEL ~900 automated observations of snow depth in USA.  These have to satisfy stringent QC before assimilation but have proved useful.

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• High impact considerations

  • Cloud and freezing fog strongly influence the energy fluxes into and from the snowpack.  The IFS may not correctly capture or forecast the extent or thickness of cloud.   It is very important to consider possible cloud formation, persistence or clearance and assess possible changes in energy transfer between cloud and snowpack.  Thick cloud at any level will reduce solar radiation, but .  But low cloud could be warmer than the underlying snow surface resulting in a net increase in downwards long wave radiation.

  • Marine convection and associated precipitation developed by the IFS may not penetrate sufficiently far inland.  Snow showers can drift further inland due to the lower fall speed of snowflakes.

  • Model forecast snowfall might increase the area or depth of snow cover incorrectly.  Partial cover of snow may become full cover as the accumulated model snow depth becomes >10cm.  This means "tiles" in HTESSEL describing land surfaces may incorrectly cease to be used.
  • Snow may accumulate then melt (e.g. with rain, or as as a warm front advances over a cold area).

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• Snow temperature considerations

  • Variation in the surface reflectance (snow-albedo) can influence surface heat flux and skin temperatures (by 1°C-4°C).  Fresh (white) snow has high albedo reflecting much of the incoming radiation.  Dirty or older (greyer) snow absorbs more radiation with greater heat flux into the snowpack.  The sun's elevation at high latitudes is limited (and non-existent in winter) which reduces the availability of solar radiation to the snow surface.  
  • Snow surfaces are likely to melt a little more readily in forests as the heat flux at the snow/atmosphere interface is rather larger than with exposed snow.
  • Phase changes can cause a delay in warming during melting or sublimation of snow.  In IFS, airborne snow tends to sublimate much more readily than the undisturbed snow on the ground.
  • If ground surface temperatures are above 0°C, shallow surface snow often takes too long to melt.  This can have an adverse impact on albedo and radiation fluxes.
  • Thermal properties of the snow can cause heat and moisture transfers to be effectively de-coupled.  Snow, especially new dry snow, is a good thermal insulator.
  • Snow depths may reduce gradually because the density of the snow has increased through compaction in the model (and also in reality) as the days progress.

  • Forest snow night time temperatures fall too low.  Even if the forest is dominant, the vertical interpolation to evaluate T2m is done as for an exposed snow tile.  This is because verifying SYNOP stations are always in a clearing.  In reality, forest generatedturbulence maintains turbulent exchange over the clearing and prevents extreme cooling.

  • The direction and strength of the low level winds can have a strong effect on snowfall:

    • Surface wind from land - temperatures can be lower and snowfall deeper.

    • Surface wind from sea – temperatures slightly higher and snow more sleety, at least at lower levels.

  • Forecast 2m temperatures over deep snow:
    • have good agreement with observations between −15°C and 0°C.
    • tend to be too warm by around 3-5°C compared to observations when T2m <-15°C.   Large night time errors of forecast temperatures, even by as much as 10°C too warm, are more likely under clear skies, .  This can occur even when this has clear skies have been correctly simulated by the model. 
    • have a relatively constant cold bias during the day of ~1.5°C compared to observations.
    • the amplitude of the forecast diurnal cycle of T2m underestimates the amplitude of the observed diurnal cycle by between ~10% to 30%.  Forecast minima tend to be warmer and daytime maxima colder than observations. 
    • verification of temperatures can be difficult.  This is due to variations in the height that temperature observations are made.  Some countries and locations:
      • maintain the sensors 2m above the snow surface, adjusted after every fall of snow.
      • have sensors higher than 2m above the ground to ensure measurement of air temperature throughout the year even after large accumulations of snow.   Deep late winter snow reduces the distance between snow surface and sensor.  In warmer periods of the year the sensor will be further from the ground surface than normal observations.  See Fig2.1.4.4-5. 

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• Ice

  • There is no representation or forecast of snow on sea ice, lake ice or glaciers.  If considering ice cover and thickness, thin   Thin sea ice or lake ice that is covered by thin snow grows or melts much faster than does thick ice with deep snow.

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