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Analysed or forecast other cloud parameters can also have an impact.  Errors in the prediction of the temperature structure have a strong influence on forecast cloud layer(s) and on humidity forecasts, particularly in the lowest layers (Fig9.2.1.1-1 and Fig9.2.1.1-2).

Forecasts are influenced by incorrect:

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• cloud cover (from observations or satellite pictures) with cloud analyses or forecasts.
• cloud structure (from observed and background vertical profiles).

Fig9.2.1.1-1: The example illustrates how small errors in the extent and thickness of model forecast low cloud and fog can have large impact on forecast 2m temperatures.   Over Spain, 12UTC temperatures approached 12°C in areas where low cloud broke and cleared but remained cold where low cloud and fog persisted (subfreezing in some areas of freezing fog).  The model forecast low cloud to be variable in thickness and rather greater in extent than in reality.

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Generally, the shorter the forecast period the better temperature forecasts are, but even at T+12h errors can remain widespread and still large in few places.

Fig9.2.1.1-2: Charts showing distribution of low cloud over Europe at 12UTC 26 Dec 2024.  Also shown are actual and forecast (T+12) vertical profiles for Essen and Muenchen and the errors between the forecast and observed 2m temperatures more generally.  The largest positive errors (reds and yellows, model T2m too high) are where the model has cleared the cloud too quickly and allowed incorrect insolation.   The negative  errors ((blues, model T2m too low) are where the model has maintained the cloud too and hindered insolation.  The cloud distribution is critical and users should anticipate likely deficiencies by monitoring available surface or satellite information. 

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(Note: Previous to June 2023 (Cy47r3 and earlier), only a single layer snow model was available.  There was no mechanism to deal with density variations in the vertical within the snowpack.  This had an impact on energy fluxes which in turn had potential to adversely affect the forecasts of 2m temperature.   For example, when new low density snow falls onto old dense snow, the atmosphere might be "re-insulated" from a ground heat source, allowing 2m temperatures to drop lower in reality than in the model.  In practice this particular problem will be exaggerated by temperature sensors ending up closer to the snow surface when snow has fallen (assuming they are not elevated manually)).

Fig9.2.1.1-3: The snow depth in the vicinity of Murmansk is shown as a shade of green (5-10cm).   A snow depth of 10cm (actual snow depth, not water equivalent ) is the threshold for the IFS to assume the entire grid box fully snow covered (snow cover fraction = 1 ).  Thus a difference around this threshold value can change the tile partitioning and thus snow coverage may not be uniform or continuous over the grid box.  The snow-free tiles would have less insulation from the soil underneath so maintaining the average skin temperature to higher temperature compared to a fully snow-covered grid box.  This can potentially impact the 2-metre temperature computation.

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Overnight 2m temperatures tend to be too cold over rugged or mountainous areas.

Fig9.2.1.1-4: Example of temperature errors in mountainous areas. Forecast temperature at high altitude stations can be far too low due to mis-representation of the temperature inversion and over-development of night-time surface inversion.   Austria, DT 12UTC 27 Jan 2024, VT T+60 00UTC 30 Jan 2024.              

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. 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°C / -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. 

 Fig9.2.1.1-5: 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).

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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. 

Fig9.2.1.1-86: Example of forecast error associated with passage of a zone of associated with saharan dust.

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