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Large negative errors in night-time 2m temperatures (the forecast is too cold) can be present over the Alps or other mountainous areas.  A ridge/anticyclone can be associated with weak winds, large scale subsidence and a warm air mass.  Where such a ridge/anticyclone dominates over a mountainous area there can be large variations in temperatures at adjacent stations at very different altitudes.  Deep surface inversions of 2-metre temperature can develop and therefore temperatures in valleys drop considerably below freezing whilst they stay much higher even positive on mountain tops.  Temperatures high up can be positive day and night whilst in the valley inversions formed with a well-marked diurnal cycle – sub-freezing temperature at night in some places as low as -7 / -8°C but warmer at day time.  And the largest model errors tend to occur in the mountain tops where the model cannot represent well as the model has a smoother orography than reality.  As a result, especially with snow cover, the model builds sharp temperature inversions, even high up in the mountain where the air mass is warm.  The model is prone to very large temperature errors – errors are large at mountains tops as the model builds inversions which actually do not exist in the real world. 

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 Fig9.2.1-5: An example of 2m temperatures in wintertime anticyclonic conditions in complex terrain.  Temperature analysis at 09UTC 30 Dec 2024 covering southern Germany, Austria, eastern Alps and the Tirol (from INCA analysis).  Temperatures are around -10°C in valleys and at the same time up to +10°C on high ground above the low-level cold pools. The spectral representation of model orography means detailed features such as these simply cannot be represented in a model with 9km resolution leading to large forecast errors in temperatures at some stations.   Note also the fanning out of some of the coldest air as it exits the Inn valley southeast of Munich.  (INCA Central Europe is the Integrated nowcasting system for the Central European area).

Lake effects

The effect of lakes is parameterised using FLake and a lake cover mask.  The sub-grid detail may not be completely captured and the energy fluxes may well be incorrectly estimated, particularly where frozen lakes are plentiful and/or forecast snow cover is uncertain.   These aspects can:

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However, generally there is an improvement in forecast 2m temperatures.  But at times the analysed temperature structure of the boundary layer may only move a small way towards correcting errors in the background (Fig9.2.1-57).  From a mathematical standpoint it is also (unfortunately!) more difficult to correctly assimilate data near the surface than data higher up.

Fig9.2.1-57: Examples of the difficulty of assimilating temperature and humidity data in the lowest layers.

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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.  More information on aerosols and greenhouse gases is given elsewhere in the guide. 

Fig9.2.1-68: Example of forecast error associated with passage of a zone of associated with saharan dust.

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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.  Fig9.2-7 9 & Fig9.2.1-8 10 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.

Fig9.2.1-79: An example of incorrect assessment of heat and moisture fluxes (left, temperatures; right, dew points), at Cordoba 12 June 2017: Ensemble Control Forecast (ex-HRES) forecast temperatures and dew points (red) and observed temperatures and dew points (black).  Ensemble Control (ex-HRES) forecast 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.

 Fig9.2-810: 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 Fig9in Fig9.2-69.  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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