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Much of the cold bias of night-time 2m temperature south of 60°N is associated with an underestimation of (low) cloudiness.  The wintertime night-time bias in Central Europe is smaller for days which are (nearly) clear-sky.   However cloud cover is not solely responsible and underestimation of cloud optical depth and/or incorrect forecast of cloud type or base height could also play a part.  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 (Fig9.2.1.2-1).

Incorrectly analysed or forecast cloud cover can also cause 2m temperature errors, by hindering or enabling radiative cooling (or also heating by insolation) during the forecast process.   Commonly, too little cloud cover, especially over snow, results in significantly lower forecast 2m temperatures, whilst too much cloud reduces overnight cooling and gives anomalously high forecast minimum temperatures.  The situation by day may be different, depending also on day length.

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Forecasters should compare observed cloud cover (from observations or satellite pictures) with cloud analyses or forecasts to assess how well the cloud has been captured.

Fig9.2.1-1.2: Examples of the difficulty in forecasting temperature in lowest layers.  At Kryvyi, although it is cloudy in reality yet the observed temperature is still a lot colder than forecast.  The forecast boundary layer temperature is too warm (by ~5°C) and the cloud cover is not represented.   At Lulea the inversion top is not well captured, the moisture (in relative humidity terms) is not well portrayed, and the surface temperature is too warm (by ~5°C) - but had more cooling been forecast near the surface then the very lowest layers would have been correctly captured.

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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)).

Fig9.2.1.3-2: 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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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 (Fig9.2.1.4-3). From a mathematical standpoint it is also (unfortunately!) more difficult to correctly assimilate data near the surface than data higher up.

Fig9.2.1.4-3: Examples of the difficulty of assimilating temperature and humidity data in the lowest layers.  At Kryvyi the first guess (blue) is too warm, and also too dry (in relative humidity terms).  The analysed structure (red) after assimilation of the observed data (black) is slightly less warm and has captured saturation within the inversion base but remains generally drier (in relative humidity terms) and warmer in the boundary layer.  At Lulea the analysed temperature structure remains similar to the first guess (blue) despite the observed much colder near-surface temperature and warmer inversion top.  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!

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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 (Fig9.2.1.2-1).

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 (Fig9.2.1.4-3).

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 & Fig9.2 and Fig 9.2.3 -5 illustrate the problem.   Users should recognise the impact that lowLow-level moisture has 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.

Fig9.2.1.5-4: An example of incorrect assessment of heat and moisture fluxes (left, temperatures - diagram on left; dewpoints - diagram on rightright, dew points), at Cordoba 12 June 2017: HRES forecast temperatures and dewpoints dew points (red) and observed temperatures and dewpoints 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.

 Fig9.2.3-5: 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 . Dewpoint June  in Fig9.2-4.  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 dewpoint errors could be much more a function of very local effects - e.g. proximity of a lake or river.

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