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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 and Fig9.2.1-2).

Forecasts are influenced by incorrect:

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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)).

Fig9.2.1-23:  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.

Fig9.2.1-34: 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.              

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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 (Fig9.2.1-45).  From a mathematical standpoint it is also (unfortunately!) more difficult to correctly assimilate data near the surface than data higher up.

Fig9.2.1-45: 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. 

Fig9.2.1-56: Example of forecast error associated with passage of a zone of associated with saharan dust.

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Under clear-sky conditions there is generally little error during the day, but a moist bias in the evening.  In cloudy conditions the daytime the bias is dry and is in part related to the representation of turbulent mixing, in particular in cloudy convective cases.  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 and Fig9.2.1-2).

Turbulent Mixing effects

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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 (Fig9.2.1-65).

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.  Fig9.2-6 7 & Fig9.2.1-7 8 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.

Fig9.2.1-67: 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.

 Fig9.2-78: 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 Fig9.2-6.  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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