Page History
...
In general, temperatures are forecast fairly well over the globe. On average, systematic errors in forecast 2m temperatures are generally <0.5°C. Biases in 2m temperature (verified over land) vary geographically, as well as with season, time of day and altitude. Larger biases and errors occur over orography or in snow covered areas.
Diurnal temperature changes are strongly influenced by incoming and outgoing heat flux which itself is governed by the extent and thickness of cloud cover. Uncertainty in the model analysis of cloud can have a strong impact on forecast errors.
Most Most of the large errors seem to occur when the surface temperature is very cold, and the lowest levels may levels may become extremely stable. In such very stable air tiny amounts of energy can correspond to large temperature changes at the surface because surface because there is no convection to mix energy through the lower atmosphere. This is the main physical reason for large errors being relatively commonplace in such circumstances. Temperature errors often don’t depend strongly on the forecast range.
Effects contributing to temperature errors
Near-surface temperatures are related to a variety of processes:
The near-surface inversion is likely to be most influential and errors more likely with high pressure and calm conditions. It is vital to compare the observed and forecast thickness and extent of low cloud and the temperature and humidity structure of the lowest atmosphere.
Effects contributing to temperature errors
Near-surface temperatures are related to a variety of processes:
- cloud cover and cloud cloud cover and cloud optical properties
- albedo and radiative transfer
- precipitation
- surface fluxes
- turbulent diffusion in the atmosphere
- strength of land-atmosphere coupling
- soil moisture and temperature
...
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
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).
Effects of cloud cover
Analysed or forecast cloud cover has a large impact on forecasts of 2m temperature 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.
- day-time radiative heating. This results in higher forecast 2m temperatures.
- too much cloud cover discourages:
- night-time radiative cooling. This results in anomalously high forecast minimum 2m temperatures.
- 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 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. 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.
It is also possible, though less common, to have too little cloud in the forecast , and yet with temperatures that are too high! These more unusual winter-time error scenarios commonly build up over a period of time.
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.
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).
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.
- cloud structure (from observed and background vertical profiles).
Fig9.2.1-1: The example illustrates how small errors in the extent and thickness of model forecast low cloud and fog can have large impact on forecast 2m temperatures. Over Spain, 12UTC temperatures approached 12°C in areas where low cloud broke and cleared but remained cold where low cloud and fog persisted (subfreezing in some areas of freezing fog). The model forecast low cloud to be variable in thickness and rather greater in extent than in reality.
Thus over Spain at 12UTC there were:
- relatively large positive temperature errors (model forecast too warm, yellow areas) where fog and low clouds broke and cleared in the model but persisted in reality. Too much cloud implies less insolation and persisting cold temperatures.
- relatively large negative temperature errors (model forecast too cold, blue areas) where fog and low clouds broke and cleared in reality but persisted in the model. Breaks in cloud cover allows temperatures to rise.
The forecast vertical profiles show that the model was fairly good at representing temperature inversions. However, the model locally missed clouds and fog by a small margins. Such small errors had a large impact on surface temperatures. The warm area in central northern Spain associated with high ground above the overnight inversionFig9.2.1.2: 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:
...
- 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 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.
...
Currently snow is modelled by a 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)).
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.
...
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).
...
Overnight 2m temperatures tend to be too cold over rugged or mountainous areas.
...
Lake effects
The effect of lakes is parameterised using FLake and a lake cover mask. The sub-grid detail may not be completely captured and the energy fluxes may well be incorrectly estimated, particularly where frozen lakes are plentiful and/or forecast snow cover is uncertain. These aspects can:
...
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). 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
- 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. 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 much colder near-surface temperature
...
- and warmer inversion top. 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.
Differences . 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 In many cases the analysed temperatures remain similar to first guess values despite the observations. Users Users beware!
Miscellaneous
- Forecast maximum 2m temperatures can be too low particularly during anomalously hot weather.
- If the predicted humidity is too low then maximum temperatures can be forecast to be too high.
- Post-processing (e.g. using a calibrated statistical technique) usually improves 2m temperature forecasts, sometimes substantially so.
- Model 2m temperature 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.
...
- comparing analyses of temperature, dewpoint dew point and soil moisture with observed data
- assessing future "background" conditions and the potential impacts thereof (e.g. snowfall or cloud cover that might be different from atmospheric model predictions).
...
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
...
Errors in the representation of evaporation impact forecasts of near-surface humidity. Leaf area index is a measure of vegetation coverage and determines the degree of evapotranspiration. Higher values mean more evapotranspiration, and thus greater fluxes of moisture into the atmosphere.
...
Evaporation over 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.
...
- 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.
Lake effects
Lake temperatures can have an effect on forecast of dew point temperatures, particularly in deciding whether the lake is frozen or not. Proximity of a lake can have an influence on the humidity at a downwind location.
The effect of lakes is parameterised using FLake and a lake cover mask. The sub-grid detail may not be completely captured and the energy fluxes may well be incorrectly estimated, particularly where frozen lakes are plentiful and/or forecast snow cover is uncertain. These aspects can:
...
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.
...
- comparing analyses of temperature, dewpoint dew point and soil moisture with observed data
- assessing future "background" conditions and the potential impacts thereof (e.g. snowfall or cloud cover that might be different from atmospheric model predictions).
...
- too little snow-cover and/or too much cloud is analysed then there is a risk forecast temperatures may be too high and humidity too low.
- too extensive snow-cover and/or too little cloud is analysed then there is a risk forecast temperatures may be too low and humidity too high.
- in light winds, humidity over snow and water surfaces is likely to be rather higher than shown in background or forecast fields, particularly where flooding or with melting snowfields.
- 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 minimal.
Other errors in near surface Temperature and
...
dew point
Errors associated with thick fog.
Some errors have occurred in forecasts near-surface data associated with cases of thick fog. A bug in IFS has misrepresented the positive feedback between two interacting and imperfectly represented mixing processes in the near surface layers in the new moist physics scheme. The problem has been added to Known IFS forecasting issuesand a fix has been prepared with implementation in the next IFS upgrade expected late in 2022.
...
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.
The left panel shows that during this very hot spell the maximum temperature, on 12th, was under-predicted by 3ºC. This may be due to unrepresented local factors, such as urbanisation, though on the other hand the signal is also typical of what we often see during extreme summer heatwaves. This bias is a subject of current research; it may be symptomatic of an IFS inability to generate the superadiabatic super-adiabatic near surface layers that one sometimes sees on radiosonde ascents.
The right panel shows that on this occasion the magnitude of the dewpoint dew point errors was even larger overall. Again there are many possible reasons, but one candidate would be mishandling of moisture fluxes to/from the surface. In turn these could relate to soil moisture errors, or errors in handling the biology of evapotranspiration.
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 dew point errors could be much more a function of very local effects - e.g. proximity of a lake or river.
...
Soil moisture and temperature is modelled in four soil levels but there is a considerable lack of real-time observations of soil condition and moisture content. Nevertheless heat and moisture fluxes have an impact on model surface and 2m temperature and moisture.
The ensemble mean values of soil moisture slightly overestimate the diurnal cycle of soil temperature:
...
- comparing analyses of temperature, dewpoint dew point and soil moisture with observed data
- assessing future "background" conditions and the potential impacts thereof (e.g. snowfall or cloud cover that might be different from atmospheric model predictions).
...
Overview
Content Tools