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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 .  In particularly lower weight is given where there is little support from adjacent observations, such .  Such data can even be rejected completely.

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• 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 .  It has captured saturation within the inversion base but remains generally drier (in relative humidity terms) and warmer in the boundary layer.  The forecast  The forecast boundary layer temperature is too warm (by ~5°C) and the cloud cover is not represented.
• At Lulea the analysed temperature structure remains similar to the first guess (blue) despite the observed .  This is despite observation of much colder near-surface temperature and warmer inversion top.  The inversion top is not well captured, and the moisture (in relative humidity terms) is are not well portrayed, and the identified.  The surface temperature is too warm (by ~5°C) - but had more cooling been forecast near the surface then the very lowest layers would could have been correctly captured.

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The amplitude of the diurnal cycle is generally underestimated over land (a deficiency shared by most forecasting models).  This is especially the case in Europe during summer when summer when the underestimation of temperature range reaches ~2°C across large areas.  Near-surface temperatures are generally too warm during night-time and slightly too cold during the day.  However, although the degree to which the amplitude of the diurnal cycle is underestimated depends on region and season.  Night-time 2m temperatures are about 1–2°C too warm and surface temperatures about 2°C too warm.

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The ground surface temperature (skin temperature) and surface albedo over land are very different to those over the water.  The land-sea mask defines whether the grid points are land or sea points, but .  But in coastal areas grid points will not capture the detail of the coastline and moreover .  Hence, surface radiative fluxes computed over the ocean may also be used by the atmospheric model IFS over the adjacent land.   This is because, for reasons of computational cost, the radiation code has to be run on a grid that is 6 times more coarse than the operational model grid.  This can lead to large near-surface temperature forecast errors at coastal land points.  To combat this problem the radiation code was changed and involved modifying the surface albedo when radiation calls are made. This leads to more to realistic coastal land temperatures.  Discussion of the land-sea mask and meteograms relates.

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Extensive concrete and buildings can possibly provide a source of heat (the heat island effect) and even moisture (from air-conditioning units).   Towns and cities are likely to have very different characteristics from other HTESSEL tiles which describe natural land coverage.   An urban tile in HTESSEL (introduced in Cy49r1) models the fluxes of heat, moisture and momentum and their effects around towns and cities.  Forecast screen temperatures in large urban areas, particularly cities and especially coastal cities, can still be a little low when compared to observations especially .   In particular, forecast screen temperatures can be too low on relatively clear, calm nights, and 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.

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Also, notably, at 12UTC the observed structure (black line) shows more cloud in reality than forecast and yet is still a lot colder. 

Turbulent Mixing effects

Biases in near-surface temperatures during winter conditions are very sensitive to the representation of turbulent mixing in stable boundary layers.  The partition of momentum transport between dry and moist updrafts is particularly important.  Comparison with radiosondes in the lower 200m of the atmosphere suggests underestimation suggests underestimation of the temperature gradient; this .  This is particularly pronounced at lower latitudes.  Full  Full resolution of the details of the temperature structure in the lowest layers of the atmosphere is not possible with current computational resources.

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• • low level inversions are particularly common at high latitudes in winter.
  • the closer the inversion is to the surface, the larger is the potential error (forecast surface and 2m temperatures too high).

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• • errors in wind profiles and/or wind directions relate to mis-representation mixing in convective boundary layers

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• • .

Vegetation effects

Temperature  errors (particularly in biases during spring and autumn) are in part related to the representation of vegetation (in terms of cover and seasonality), and evaporation over bare soil.  Heat flux from bare soil is also problematic.   Soil temperature and soil moisture is modelled in IFS but there is not a great deal of directly measured observations available.  However, the impact of heat and moisture fluxes can be a significant contributor to 2m and surface temperature errors, and hence have an impact on humidity.

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• more solar energy will be gained on south-facing (N Hem) slopes implying .  This implies actual temperatures may be higher than forecast,.
• less solar energy will be received on north-facing (N Hem) slopes implying .  This implies actual temperatures may be lower than forecast, particularly where they are in shadow for much of the time and in sheltered valleys.

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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 . 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 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. 

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• too little snow-cover and/or too much cloud is analysed then there is a risk forecast temperatures may be too high.
• too extensive snow-cover and/or too little cloud is analysed then there is a risk forecast temperatures may be too low (although . But in the case of too little cloud or more wind sometimes temperatures may be too high).
• the boundary layer structure is not successfully analysed then there is a risk forecast temperatures may correspondingly be in error.
• winds are too strong or too weak then forecast temperatures may have larger errors (particularly at high latitudes in winter where the role of insolation in offsetting radiative cooling is minima.

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Errors in near-surface dew point temperatures during winter conditions are very sensitive to the representation of turbulent mixing in stable boundary layers.  Comparison with radiosondes in the lower 200m of the atmosphere suggests underestimation suggests underestimation of the temperature gradient and especially the humidity gradient (giving a dry bias); this .  This is particularly pronounced at lower latitudes.  Full resolution of the details of the temperature structure in the lowest layers of the atmosphere is not possible with current computational resources.

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• more solar energy will be gained on south-facing (N Hem) slopes implying actual temperatures may be higher than forecast.  However upslope movement will increase humidity, possibly to saturation,.
• less solar energy will be received on north-facing (N Hem) slopes implying actual temperatures may be lower than forecast, particularly where they are in shadow for much of the time.  Thus high humidity may persist in sheltered valleys.

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Fig9.2.1-9: An example of incorrect assessment of heat and moisture fluxes (left, temperatures; right, dew points), at Cordoba 12 June 2017: Ensemble .   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.

 Fig9.2.1-10: 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 .  This is suggested by the observed lower dew points points during the day on 12th June in Fig9.2-9.  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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