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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 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).
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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.
Fig9.2.1-2: Small errors in the extent of low cloud and fog can have large impact on forecast 2m temperatures. In the example positive temperature errors (model too warm) are over parts of Spain dominated by fog and low clouds. The contrast of temperatures between these foggy ares and the rest of the country are quite large – subfreezing temperatures in some areas with freezing fog in contrast to +12°C outside of the fog. Errors are relatively large.
Low clouds are forecast to be more widespread than in reality. Observations show fog in some areas at 12 UTC. The forecast vertical profiles shows the model was fairly good at representing temperature inversions. But the model missed clouds and fog by a small margins in places. Small errors can have large impact on surface temperatures. Too much cloud implies less insolation and persisting cold temperatures. Where the model breaks the inversion with consequent breaks in cloud cover allows temperatures to rise. The warm area in central northern Spain associated with high ground above the overnight inversion.
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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(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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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 (Fig9.2.1-34). From a mathematical standpoint it is also (unfortunately!) more difficult to correctly assimilate data near the surface than data higher up.
Fig9.2.1-34: 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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