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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 in coastal areas grid points will not capture the detail of the coastline and moreover surface radiative fluxes computed over the ocean may also be used by the atmospheric model 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.

Despite the above IFS improvements, coastal temperatures still need to be viewed with caution, especially where urban areas are next to the sea.  The local drift of surface air from a land or sea source may differ significantly from model forecast low-level winds.  This can be especially true where the orography is complex and influence the actual, analysed, and forecast low-level winds.  Users should consider these aspects when assessing coastal temperature forecasts (e.g. forecast Southern European coastal temperatures have been observed, at times, to be too low during a Mediterranean heatwave).

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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.

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Fig9.2.1.2: 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.

Snow cover effects

The analysis and forecast of snow depth, snow compaction, and snow cover are important for forecasting lower-layer and near-surface temperatures and they can have a significant impact on forecast accuracy.  However, the relationship between snow cover and temperature is complex:

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• its age (the model facilitates slow, natural compression),
• melting (small amounts of snow on the ground tend to take too long to melt, even if the temperature of the overlying air is well above 0°C).
• interception (of rain)
• addition of new snow.

The albedo is related to the extent (and age) of snow cover and snow characteristics in analysed and forecast fields have an effect on the radiation that could be absorbed.  This has a corresponding impact on forecasts of 2m and surface temperatures.    Better assessment of the albedo when the multi-layer snow scheme is introduced will allow faster response to changes in the radiative forcing.

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Currently snow is modelled by a multi-layer snow scheme allowing more realistic heat transfer.

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In this example a single layer snow model was used in Cy47r3 and before.  There is a significant difference between the observed (black) and forecast T+72 HRES forecast (red) temperature structure at Murmansk (location shown by the arrow).  The observed structure is much colder than that forecast, and in this case, surface snow cover appears to have been critical to the forecast.  The observed temperature structure could be due to stronger radiative cooling due to more extensive and/or deeper snow cover than is indicated in the IFS snow depth chart.  A multi-level snow model is used in Cy48r1 and later.

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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 information 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.

Leaf area index is a measure of vegetation coverage and determines the degree of shading and how much radiation is absorbed or reflected.  Leaf area index varies in the model, month by month.  However, the leaf area index will not be representative if there is anomalous weather e.g. wind storms may strip leaves from trees, widespread fires may clear vegetation (and change the albedo).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.

Orography effects

IFS model orography smooths out valleys and mountain peaks, especially at lower resolutions.  A forecast 2m temperature may be unrepresentative if it has been calculated for an altitude significantly different from the true one.   A more representative height might be found at one of the nearby grid points.  Any remaining discrepancy can be overcome using Model Output Statistics (MOS) or statistical post-processing (see additional sources of information below).

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