Page History

...

• 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. 

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 Rocky Mountains and the Tibetan Plateau areas, with widespread improvements in the troposphere in geopotential hight, vector wind and humidity forecasts in the northern hemisphere in winter.

The multi-layer snow model

...

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:

• generally 10mm of precipitation approximates to 10cm of snow depth. 
• for less dense newly fallen snow, 10mm of precipitation approximates to 10-15cm of snow depth.
• with wind effects the density of snow increases quite quickly, 10mm of precipitation approximates to 7cm of snow depth.  
• very low negative temperatures are required to considerably decrease the density and increase the equivalent snow depth in cm.

The current snow scheme tends to melt snow too slowly.

...

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

...

• an Optimal Interpolation method which adjusts the model-analysed snow water equivalent and snow density prognostic variables.
• snow cover parametrization in the snow model.  This has been  revised to improve the fraction of ground covered by snow.
• conventional measurements of snow 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 snow cover information and sea ice extent over the northern hemisphere at 4km resolution.  There 4km resolution.  Satellite-based snow cover information is of particular use in mountainous areas.  It reduces IFS positive biases in snow cover and snow depth, and has a strong positive impact on atmospheric forecasts. 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  Fig2.1.4.4-7 and Fig2.1.4.4-8.  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 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.

...

• snow observations are limited to those with a difference in altitude from model height of 500m or less.  Observations with less altitude difference gain higher weighting.    
• at some high-latitude grid points in the model it is common for snow depth to be extremely high

...

• .  The maximum allowed analysed snow depth value is 3m (revised from 1.4m).   Depths greater than 3m may not be assimilated

...

At high levels (altitudes >1500m) IMS data is not used and observations of snow depth are sparse or non-existent.  In these  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 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 Fig2.1.4.4-5 and Fig2.1.4.4-9).

Lake ice and sea ice do not have snow cover in the model. 

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

...

• 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 (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. 
    • 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.
    • verification of temperatures can be difficult.  This 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 late winter mean a short distance between snow surface and the sensor, while the sensor will be in a greater distance than usual to the ground surface during the warm period of the year.  See Fig2.1.4.4-5. 
    • 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.

• 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 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.

...