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Urban effects are not currently represented in HTESSEL.  Extensive concrete and buildings are likely to have very different characteristics from HTESSEL land tiles, and possibly also provide a source of heat (the heat island effect) and even moisture (from air-conditioning units).  Forecast screen temperatures in large urban areas, particularly cities and especially coastal cities, are commonly several degrees too low when compared to observations.  The problem is accentuated on relatively clear, calm nights, and can be even worse in winter where the urban area is surrounded by snow cover.   Users should assess the potential for deficiencies in low-level parameters and adjust as necessary.

Cloud effects

Effects of cloud cover

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Incorrectly analysed Analysed or incorrectly forecast cloud cover can cause has a large impact on forecasts of 2m temperature errors.  This errors causes.  Cloud cover can hinder or enable radiative cooling (or also heating by insolation) during the forecast process.

Commonly:

• too little cloud cover encourages:
  • night-time radiative cooling.  This results in significantly lower forecast 2m temperatures, especially over snow
  , increases overnight cooling and results in significantly lower
  • .
  • day-time radiative heating.  This results in higher forecast 2m temperatures.
• too much cloud cover reduces overnight cooling and discourages:
  • night-time radiative cooling.  This results in anomalously high forecast minimum 2m temperatures.

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• • day-time radiative heating.  This results in lower forecast 2m temperatures.

Much of the cold bias of night-time 2m temperature south of 60°N is associated with an underestimation of (low) cloudiness.  Wintertime night-time bias in Central Europe is smaller for occasions which are (nearly) clear-sky.  

The situation by day may be different, depending also on day length.  

where (nearly) cloud-free conditions have been forecast and observed.

Day length can also be important.  At higher latitudes, cooling during the long nights may not be offset by solar radiation during the short days leading to a gradual day-by-day lowering in 2m temperatures.  

It is also possible, though less common, to have too little cloud in the forecast yet with temperatures that are too high!  These more unusual winter-time error scenarios commonly build up over a period of time.

Other effects of cloud

Analysed or forecast other cloud parameters can also have an impact.  Errors 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 (Fig9.2.1-1).

It is also possible, though less common, to have too little cloud in the forecast, and temperatures that are too high!  These more unusual winter-time error scenarios commonly build up over a period of time.

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Forecasts are influenced by incorrect:

• optical depth. 
• cloud type.
• base height.

Verification of cloud

In order to assess how well the cloud has been captured forecasters should compare observed and forecast:

• cloud cover (from observations or satellite pictures) with cloud analyses or forecasts

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• cloud structure (from observed and background vertical profiles).

Fig9.2.1-1: 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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Currently snow is modelled by a multi-layer snow scheme allowing more a fairly realistic heat transfer.

(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)).

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