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• sensible heat fluxes and latent heat fluxes
• downward long wave and shortwave radiation
• upward fluxes of heat through the layer(s) in the snowpack from the underlying ground.

Forecasts of Forecast air temperature at 2m are derived from the is derived by interpolation between: 

• the model forecast temperature at the lowest

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• model level (L137 at 10m) and 
• the model forecast temperature of the

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• underlying surface (the skin temperature). 

The skin temperature is itself derived using using HTESSEL which employs one or more “tiles” to describe the characteristics of the land.  These “tiles” evaluate heat fluxes into and from the underlying surfaces.

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• exposed snow - for areas without vegetation above the snowpack, and therefore without interference to the weak incoming and outgoing heat and momentum fluxes.
• forest snow - for areas where there is tree cover sufficient to interfere with incoming and outgoing heat and momentum fluxes.   The heat flux at the snow/atmosphere interface is rather larger than over exposed snow. 

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

Forecasts of air temperature at 2m use the skin temperature of the snow as if it were at the 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.

The multi-layer snow model

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, density, and liquid water content in each layer.  The energy flux at the top of the snowpack is the balance of the upward and downward energy fluxes at the snow surface 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.

The multi-layer snow model has a fairly realistic representation of the vertical density and temperature profiles of the snowpack which .   This allows a good representation of its thermal properties.  

The model represents:

• thermal coupling across the snow-atmosphere interface and within the thin top snow layer.  This allows a realistic representation of sporadic surface snow melt and is especially important for warmer sites with wet snow.
• variations in snow density with depth in thick and cold snowpacks.
• percolation, freezing and melting of water which has an effect upon the transfer, release and absorption of heat in each layer and on the surface.
• heat fluxes across the snow-soil interface and within the thin bottom snow layer.  This allows a representation of changes in permafrost.   

When fresh snow falls or melts away, it is added to or subtracted from the top of the snowpack.  Then the layers are reanalysed such that relatively shallow layers of snow are maintained at the top (5cm thick) and at the base (15cm thick) so that the atmosphere/snow and soil/snow heat fluxes can be best modelled.

The skin temperature (T_skin) over snow cannot rise above 0°C and any net positive heat flux at this temperature is used to warm or melt the snow layer.    The flux of heat might be:

• downwards from the atmosphere (e.g. by advection of warmer air,  by insolation, or by underlying an area of warm based cloud). 
• upwards from lower in the snowpack, though this could be offset by latent heat extraction where melting of snow occurs.

Vertical discretisation over flat terrains:

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Fig2.1.4.4-2: Schematic representation of the multi-layer snow scheme. Deep snow. Snow depth >27.5cm. Any additional snow accumulation is added into the fourth snow layer in order to preserve the characteristics and thermal flux qualities of thinner layers at base and top of the snowpack.  For snow depths between 12.5cm and 27.5cm additional snow is added proportionately to the layers as they are introduced.

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Fig2.1.4.4-3: Schematic representation of the multi-layer snow scheme for permanent snow (e.g. Greenland, Antarctica) and for glaciersfor glaciers.  Snow depth is defined as ≥10m.   Any additional snow accumulation snow accumulation is added into the fifth snow layer in order to .  This is ito preserve the characteristics and thermal flux qualities of thinner layers at the top of the snowpack.

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A different algorithm is applied to define the snow layers in regions of complex or mountainous terrain where snow depth >25 depth >25 cm.  These layers are thicker than used for a snowpack with same depth over a flat region (e.g. region.  For example, in complex terrain an 85cm deep snowpack is discretised with layer depths: 16.00cm, 17.25cm, 17.25cm, 17.25cm, 17.25cm).

Complex terrain is defined as regions where the standard deviation of the sub-grid-scale orography is greater more than 50m.

  Ground height data from internationally available datasets at 1km resolution are interpolated to a spectral representation at model resolution but smoothing misses important detail.  Statistical parameters (e.g. Also included are standard deviations of the mean height, slopes, and direction of unresolved orography) are fed into the model via the sub-grid-scale parametrisation of orography.  The spectral nature of model orography may mean that in rugged mountainous areas, where there are large variations in altitude over short distances, mountain .  However,  in rugged mountainous areas smoothing misses important detail and mountain peaks may be under-represented and narrow valleys may not be represented at all.  

Permanent snow areas:

In permanent snow areas (e.g. Greenland, Antarctica,  and glaciers) a fixed snow layering it is used.  The top four layers (counting from the one in contact with the atmosphere) have a constant depths constant depths of 50 cm, whereas any .  Any additional snow accumulation is added into the bottom layer (Fig2.1.4.4-3 and Fig2.1.4.4-4).

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Fig2.1.4.4-4: Permanent snow and glacier grid points. These are predominantly in Greenland and other Arctic mountainous island areas.  Permanent snow is located in the Himalayan region. Isolated locations are in the Alpine region, Caucasus mountains and southern Norway.  Many coastal regions of Greenland, Ellesmere Island and Baffin Island have less permanent snow allowing snowmelt .  Snowmelt and warming of the exposed rock to allow modelling of temperature and precipitation and precipitation changes. 

Sea ice:

There is no representation of snow on top of sea ice or ice on lakes.  Snow cover on ice acts to increase its persistence by increasing the albedo and reducing the heat flux into the modelled ice.  Thin sea ice or lake ice covered by thin snow grows or melts much faster than does thick ice with deep snow.  

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Fresh snowfall is added to the top layer, with a new snow density depending on air temperature and wind speed.  Both convective and dynamic snowfall is considered to be homogenous over the grid box.  Melted snow is removed from the top layer.  The snow mass is then redistributed across the different layers but .  However, relatively shallow layers of snow are maintained at the top and at the base of the snowpack.  This is so that the atmosphere/snow and soil/snow heat fluxes can be best modelled. 

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Snow depth water equivalent is the sum of frozen and liquid water within the snowpack.  Snow density considers meltwater refreezing, so the density will vary but the snow water equivalent should not change.

The snow depth of each layer is calculated by snow depth water equivalent divided by snow density for each layer.  

Snow depth is computed using:

• the liquid water equivalent of snow lying on the ground
• the average density of the snow layer (typically lower for fresh uncompacted snow, higher for compacted old snow).

The total snow depth is the sum of the snow depth of each layer.

When snow depth is:

• < 12.5cm, additional snow is added to the single snow layer.
• 12.5cm < 27.5cm additional snow is added proportionately to the layers as they are introduced.
• >27.5cm, only the layer 4 is used as the snow accumulation layer.

For permanent snow areas (e.g.Greenland, Antarctica) and over mountain glaciers:

• the snow depth is defined as 10m of water equivalent.
• there is a fixed snow density for all 5 layers.
• the depth of each of the upper four layers is 0.50m; fresh snowfall is added the bottom layer (i.e. layer 5 is used as an accumulation layer). See Fig2.1.4.4-3.

It is common for snow depth to be extremely high at grid points within these areas of permanent snow.  

Fresh snow seems to be too dense and compacted in the model.  As a rough rule of thumb:

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Data assimilation for snow on the ground

Snow cover, snow depth and snow compaction affect all IFS atmospheric forecast models.  It is important the IFS monitors actual values and updates the background fields accordingly.  Any discrepancy will cause errors in the forecast as several physical properties of snow influence:

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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 to give .  It gives snow cover information and sea ice extent over the northern hemisphere at 4km resolution4km resolution.  There is some manual intervention manual intervention and quality control.  The IMS product only shows where at least 50% of the grid cell is covered by snow and 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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• in data sparse areas and the representativeness of observations.
• after a prolonged period without observations.
• at altitudes above 1500m.
• near glaciers.  Glaciers are considered as very deep snow rather than ice - this can cause nearby correct observations to be rejected.
• at some high-latitude grid points in the model it is common for snow depth to be extremely high and may not be assimilated.

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Fig2.1.4.4-5:. Weather station at Røldalsfjellet (Norway).  The temperature sensor is mounted at 5m above the ground (left picture) to allow .  This allows sufficient clearance beneath the sensor with high snow accumulation (right picture). Photos:MET Norway.

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Fig2.1.4.4-7: Conversion of background and forecast snow water equivalents to snow cover.  Forecast snow water equivalents of 10cm or greater are considered as associated with full cover of snow on the ground; snow .  Snow water equivalent of 5cm is considered to be associated with half cover.

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Users should be aware of possible impacts on model forecasts, especially where snow .  Snow cover and associated colder surface temperatures may persist for longer than they should and influence other parameters too.

• 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 the possible cloud formation, persistence or clearance of cloud and to assess the possible changes in energy transfer between cloud and snowpack.  Thick cloud at any level will reduce solar radiation, 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).

• • The characteristics of each grid box and areal extent of each tile type are updated through the forecast period and can vary in a rapid and interactive way. 

  • Differing snowfall among the ENS members can cause increasing differences in evolution during the remainder of the model forecast period.  Nevertheless each member remains equally probable.

  • The statistical information on the slope and aspect of orography within each grid box (e.g. south-facing, steepness) is not detailed enough for forecasts at an individual location.  This can be important in mountainous areas and HTESSEL may under- or over-estimate solar heating and runoff.  Incorrect analyses and forecasts of snow are quite possible at altitudes above 1500m in data sparse areas, or after a prolonged period without observations.  Forecasts of snow depths can be too great at altitudes above >1500m due to insufficient melting of snow more especially at very high locations (e.g. Tibet).

  • Incorrectly reported shallow snow (say by thick deposition of frost) that has been assimilated can be persistent in the model and give misleading forecasts.

• 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, even when this has 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.  High snow depths in    Deep late winter mean a short snow reduces the distance between snow surface and the sensor, while sensor.  In warmer periods of the year the sensor will be in a greater distance than usual to further from the ground surface during the warm period of the yearthan normal observations.  See Fig2.1.4.4-5. 

• Snow depth and coverage considerations

  • The smooth nature of the snow surface can cause momentum fluxes to be decoupled and winds increase in the absence of friction.  
  • Strong winds can alter snow depth and snow compaction.  Transport of snow can bring areas of drifting with snow compaction and associated increase in density.  This can be particularly effective for polar snow, where snow temperature is extremely low throughout the winter and compaction due to other processes is limited.  Conversely, strong winds can carry away dry surface snow and reduce snow depth in exposed areas.  The user should consider this effect in periods of strong winds or in generally windy regions.
  • Bias in snow depths:
    • Short-range snow depth forecasts, when compared with independent observations, on average show high quality but with .  But there is a slight overestimation of snow depth in the background and analysis fields.
    • There is a tendency towards underestimation of snow depth in central Eurasia implying either melting or compaction is overestimated for these forested areas.

  • Snow depths are often underestimated due to the very conservative snow density relation between model precipitation and snow accumulation, even though the precipitation is predicted pretty well.  The density of snow settling on the ground increases very rapidly as wind speed increases.  Very roughly:

    • Little or no wind - 2 cm snow for 1 mm rainfall equivalent.

    • Strong wind – 0.6 cm for 1 mm rainfall equivalent.

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