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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.  

Data assimilation for snow on the ground

Snow cover, snow depth

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and

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snow

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compaction

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affect all IFS atmospheric forecast models

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.  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

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influence:

  • the energy and water exchanges between snow surface and atmosphere.

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  • the upward heat flux from the ground into the atmosphere, which in turn influences surface snowmelt and sublimation.
  • the albedo.

Model variables of snow need to be reanalysed at each analysis cycle.

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 These are:

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  • snow temperature,
  • water equivalent of snow

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Snow cover

Depth of snow is diagnosed from the water equivalent of the modelled snow. 

  • Total snow cover is assumed where snow depth is diagnosed as >10cm.  Only snow or forest snow "tiles" are used by HTESSEL.
  • Partial snow cover is assumed where snow depth is diagnosed as <10cm.  A snow water equivalent of 6cm is considered to be associated with 60% cover (See Prognostic variables that affect energy fluxes Fig2.1.12B).   Other "tiles" which describe the location are used by HTESSEL in addition to the snow or forest snow "tiles".

See the section Prognostic variables that affect energy fluxes for more information on snow data and its assimilation into the model.

Considerations interpreting snow forecast information:

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

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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 the possible 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.

  • 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.

    • 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).

  • 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).

  • Mis-reported shallow snow that has been assimilated can be persistent in the model and give mis-leading forecasts.

Snow Temperature considerations

  • ,
  • liquid water content.

Snowfields are initialized every day at 00UTC from continuous offline data.  

Snow data assimilation at ECMWF relies on:

  • 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 mis-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 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 is converted to snow depths using relationships shown in  Fig2.1.12B and Fig2.1.12C.  IMS data is not currently used by the IFS at altitudes above 1500m.

Incorrect analyses and forecasts of snow are possible:

  • in data sparse areas.
  • after a prolonged period without observations.
  • at altitudes above 1500m.

Snow depths in such regions rise in response to forecast snowfall but may not decrease sufficiently at other times (See example in Fig2.1.12D).  

Snow depth

The snow depth in the model changes when fresh snow falls or when snow on the ground melts, evaporates or is compressed.  The response in dry periods at different altitudes is shown in Fig2.1.12D. 

 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 uncompacted fresh snow, higher for compacted old snow).

At some high-latitude or ‘glacial’ grid points in the model it is common for snow depth to be extremely high.

Snow cover

Snow cover is diagnosed from the water equivalent of the modelled snow: 

  • Total snow cover is assumed where snow depth is diagnosed as >10cm.  Only snow or forest snow "tiles" are used by HTESSEL.
  • Partial snow cover is assumed where snow depth is diagnosed as <10cm.  A snow water equivalent of 6cm is considered to be associated with 60% cover (Fig2.1.12B).   Other "tiles" which describe the location are used by HTESSEL in addition to the snow or forest snow "tiles".

See the section Prognostic variables that affect energy fluxes for more information on snow data and its assimilation into the model.

Considerations interpreting snow forecast information:

Users should be aware of possible impacts on model forecasts, especially where 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 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.

    • 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.

      • 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).

    • 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).

    • Mis-reported shallow snow that has been assimilated can be persistent in the model and give mis-leading forecasts.

  • Snow Temperature considerations

    • Variation in the surface reflectance (snow-albedo) can influence surface heat flux and skin temperatures.  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 (because verifying SYNOP stations are always in a clearing).  In reality, forest generatedturbulence maintains turbulent exchange over the clearing and prevents extreme cooling.

    • 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.Forecast 2m temperatures over deep snow:

  • Snow depth 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 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.

  • Ice

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

Additional sources of information

(Note: In older material there may be references to issues that have subsequently been addressed)



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Fig2.1.12A: Snow depth (cm) and sea-ice cover (%) in the high resolution forecast (HRES).  DT 12UTC 07 Feb 2023 T+00.  Note frozen lakes (e.g. NW Russia, north Caspian Sea, Uzbekistan) are also plotted as "sea ice".  FLake represents or generates ice on coastal or inland  water.  


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Fig2.1.12B: 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 water equivalent of 5cm is considered to be associated with half cover.


Image Added

Fig2.1.12C: Conversion of IMS information into an estimate of snow water equivalent for data assimilation.   IMS delivers binary information on the presence of snow for each grid cell but does not give information on snow depth.

  • If the background snow water equivalent is 0cm and IMS shows snow cover then the updated snow water equivalent is set to 5cm.
  • If IMS shows no snow cover then the updated snow water equivalent is set to 0cm.

IMS strongly impacts upon any updates to the background snow depth field.  Only if both IMS and background fields indicate snow is the IMS information not used.


Image Added

Fig2.1.12D: Forecast snow water equivalent at high level stations (blue) and low level stations (red) during the winter of 2019/20.

At low levels background fields are updated using IMS data and numerous observations of snow depth. Forecasts respond to this information and consequently show a gradual decrease in snow water equivalent during a dry period

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  • 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.Forecast 2m temperatures over deep snow:

  • Snow depth 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 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.

  • Ice

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

Additional sources of information

(Note: In older material there may be references to issues that have subsequently been addressed)

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