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Near-surface temperatures are related to a variety of processes:

• cloud cover and , cloud optical properties and aerosol 
• albedo and radiative transfer
• precipitation
• surface fluxes
• turbulent diffusion in the atmosphere
• strength of land-atmosphere coupling
• soil moisture and temperature

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Differences between observed and first guess values such as these may lead to very low weight being given to the observation, or to it even being rejected. In many cases the analysed temperatures remain similar to first guess values despite the observations. Users beware!

Miscellaneous

Effects of aerosol and dust

Aerosol advected across a region can reduce incoming radiation.  Aerosol Optical Depth (AOD) measures the extinction of a ray of light as it passes through the atmosphere.  This can be due to advection of dust etc.  A very crude rule of thumb is that an anomaly (with respect to climatology) of 1 AOD unit corresponds to a 0.5-1.5 °C day-time temperature decrease under otherwise clear skies.  Cloud cover has a much stronger effect upon surface temperature   and mask any signal from the aerosols.  The radiative impact of the forecast aerosol value is more distinct for shorter lead-times (12 or 24 hours).  At longer lead times, the evolving differences in flow patterns and clouds may become more important for the surface temperature differences than the reduced solar radiation.  Section 2.1.5.7 gives more information on aerosols. 

Image Added 

Fig9.2.1-5: Example of forecast error associated with passage of a zone of associated with saharan dust.

Miscellaneous

• Forecast maximum 2m temperatures can be too low particularly during anomalously hot weather.
• If the predicted humidity is too low then maximum temperatures can be forecast to be too high.
• Post-processing (e
• Forecast maximum 2m temperatures can be too low particularly during anomalously hot weather.
• If the predicted humidity is too low then maximum temperatures can be forecast to be too high.
• Post-processing (e.g. using a calibrated statistical technique) usually improves 2m temperature forecasts, sometimes substantially so.
• Model 2m temperature output corresponds to short grass cover (possibly snow-covered), because by meteorological convention observations are ordinarily made over such a surface.  In  This strategy may not work so well in:
  • complex terrain - e.g. forests with clearings
  - this strategy may not work so well.
  • .
  • over snow areas.  The algorithm which derives 2m temperature uses the model surface temperature while the snow surface is above the earth's surface.  This can be an important consideration where there is deep snow.  See section on snow effects.  

Diurnal Range of temperatures

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Occasionally lower tropospheric temperature data has been given low weight during the analysis process.  Usually this relates to problems with assimilating the boundary layer structure in situations with a strong inversion, coupled with the fact that the background is a long way from the truth.  The analysis procedures tend to give lower weight to observations that show major departures from the first guess and, particularly if there is little support from adjacent observations, such data can even be rejected completely.  In consequence, the analysed temperature structure of the boundary layer may only move a small way towards correcting errors in the background.  From a mathematical standpoint it is also (unfortunately!) more difficult to correctly assimilate data near the surface than data higher up (Fig9.2.1-46).

Miscellaneous

• If the predicted humidity is too low then maximum temperatures can be forecast to be too high (e.g. East England and Germany).
• Model 2m dew point and humidity output corresponds to short grass cover (possibly snow-covered), because by meteorological convention observations are ordinarily made over such a surface.  In complex terrain - e.g. forests with clearings - this strategy may not work so well.

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Errors in the analysis of heat and moisture fluxes from the underlying ground have an important impact on the model surface temperature and moisture values and hence the derived 2m screen temperatures.  Fig9.2-4 6 & Fig9.2-5 7 illustrate the problem.   Low-level moisture can impact upon temperature forecasts; if humidity is too low then maximum temperatures can be forecast to be too high (e.g. East England and Germany).

Land surface characteristics (soil moisture, leaf area index) have an impact upon temperature forecasts.    Significant differences in temperature can occur over a short distance where there is a sharp change of surface characteristics.   This can influence the location and development of subsequent convection.

Fig9.2.1-46: An example of incorrect assessment of heat and moisture fluxes (left, temperatures; right, dew points), at Cordoba 12 June 2017: HRES forecast temperatures and dew points (red) and observed temperatures and dew points (black).  HRES has under-estimated the maximum temperatures by some 3ºC.  

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An influx of moist low-level air might also occur locally (e.g. effects of a strong sea breeze).  This can influence the location and development of subsequent convection.

 Fig9.2-57: Soil moisture 00Z 11 June 2017.  It is possible that there was too much moisture in the soil (yellow) when more arid conditions (brown) would have been more appropriate as suggested by the observed lower dew points during the day on 12th June  in Fig9.2-46.  Dew point errors are more likely to be indicative of soil moisture errors during the day, because there is much more convective overturning then. Conversely night-time dew point errors could be much more a function of very local effects - e.g. proximity of a lake or river.

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