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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 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:

• the energy and water exchanges between snow surface and atmosphere.
• 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.  These are:

• snow temperature,
• water equivalent of snow,
• 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.

At high levels (altitudes >1500m) IMS data is not used and observations of snow depth are sparse or non-existent.  In these cases snow depth prediction depends only upon the short range IFS evolution.  Thus there can be little or no decrease in snow water equivalent (if it remains cold enough), though an increase after further forecast or actual snowfalls.   Snow depths may also reduce because the density of the snow tends to increase with time through compaction in the model (and also in reality).  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. 

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• A description of the structure and evaluation of multi-layer snow models and associated consequences can be found at:
• Impact of a Multi-Layer Snow Scheme on Near-Surface Weather Forecasts 
• IFS DOCUMENTATION – Cy47r3 Operational implementation 12 Oct 2021 PART IV: PHYSICAL PROCESSES. (link when issued)
• Improving ERA5-Land soil temperature in permafrost regions using an optimized multi-layer snow scheme
• Hydrological Impact of the New ECMWF Multi-Layer Snow Scheme
• Read more on the model snow depth and sea ice with example chart and model sea-surface temperature with example chart.
• Read more on snow data assimilation or land surface monitoring by satellite (LDAS).

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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.

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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.

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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.

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 Forecasts show a gradual decrease in snow water equivalent during a dry period.

At high levels observations are more sparse and IMS data is not used (>1500m).  Background fields rely on earlier snow depth forecasts.  Forecasts show constant snow water equivalent during a dry period.