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Note: HRES and Ensemble Control Forecast are scientifically, structurally and computationally identical. With effect from Cy49r1, Ensemble Control Forecast output is equivalent to HRES output where shown in the diagrams. At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.
2m Temperature errors
Bias
In general, temperatures are forecast fairly well over the globe. On average, systematic errors in forecast 2m temperatures are generally <0.5°C.
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- mountainous regions, particularly where model and actual surface altitudes are dissimilar.
- snow covered areas, particularly in extremely stable conditions.
Diurnal Range of temperatures
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 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, 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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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:
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- the extent of low cloud. This can have a strong impact on solar radiation and hence forecast 2m temperatures. If too little cloud is modelled then 2m temperatures can rise to over-high maxima, particularly in summer. Too much low cloud will keep 2m temperatures too low.
- 1000-500hPa thickness, 1000-850hPa thickness.
- the temperature and humidity structure of the lowest atmosphere.
Coastal effects
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. Hence, surface radiative fluxes computed over the ocean may also be used by the 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 six 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 relates to the problem.
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Note: A similar problem occurs at times with AIFS forecast 2m temperatures.
Urban effects
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. 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.
Cloud effects
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.
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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 forecast temperature structure strongly influences forecast cloud layer(s) and humidity forecasts, particularly in the lowest layers (Fig9.2.1.1-1 and Fig9.2.1.1-2).
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- 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).
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Fig9.2.1.1-1: 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 where low cloud broke and cleared. However, temperatures 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.
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Generally, the shorter the forecast period the better temperature forecasts are, but even at T+12h errors can remain widespread and still large in few places.
Fig9.2.1.1-2: Charts showing distribution of low cloud over Europe at 12UTC 26 Dec 2024. Also shown are actual and forecast (T+12) vertical profiles for Essen and Muenchen and the errors between the forecast and observed 2m temperatures more generally. The largest positive errors (reds and yellows, model T2m too high) are where the model has cleared the cloud too quickly and allowed incorrect insolation. The negative errors ((blues, model T2m too low) are where the model has maintained the cloud too and hindered insolation. The cloud distribution is critical and users should anticipate likely deficiencies by monitoring available surface or satellite information.
Snow cover effects
Analysis and forecast of snow depth, snow compaction, and snow cover are important for forecasting lower-layer and near-surface temperatures. 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. New low density snow falling onto old dense snow may "re-insulate" the atmosphere from a ground heat source. This would allow 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).
Fig9.2.1.1-3: 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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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 of the temperature gradient. 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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- 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).
- errors in wind profiles and/or wind directions relate to mis-representation mixing in convective boundary layers.
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.
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. The leaf area index will not be representative if there is anomalous weather. For example, wind storms may strip leaves from trees, widespread fires may clear vegetation (and change the albedo).
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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Overnight 2m temperatures tend to be too cold over rugged or mountainous areas.
Fig9.2.1.1-4: Example of temperature errors in mountainous areas. Forecast temperature at high altitude stations can be far too low due to mis-representation of the temperature inversion and over-development of night-time surface inversion. Austria, DT 12UTC 27 Jan 2024, VT T+60 00UTC 30 Jan 2024.
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 can develop. Temperatures in valleys can drop considerably below freezing while they stay much higher, even positive, on mountain tops. Temperatures high up can be positive both day and night. But in the valley there may be a well-marked diurnal cycle as inversions form and erode. Temperatures may be sub-freezing temperature at night (locally as low as -7°C / -8°C) but warmer at day time. The largest model errors tend to occur in the mountain tops. This is because the model cannot represent temperatures very 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.
Fig9.2.1.1-5: An example of 2m temperatures in wintertime anticyclonic conditions in complex terrain. Temperature analysis at 09UTC 30 Dec 2024 covering southern Germany, Austria, eastern Alps and the Tirol (from INCA analysis). Temperatures are around -10°C in valleys and at the same time up to +10°C on high ground above the low-level cold pools. The spectral representation of model orography means detailed features such as these simply cannot be represented in a model with 9km resolution. This can lead to large forecast errors in temperatures at some stations. Note also the fanning out of some of the coldest air as it exits the Inn valley southeast of Munich. (INCA Central Europe is the Integrated nowcasting system for the Central European area).
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. This is particularly true where frozen lakes are plentiful and/or forecast snow cover is uncertain. These aspects can:
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- NE Scandinavia: mainly Finland, north Sweden.
- Russia: Mainly West of the River Yenisey, also River Lena valley, parts of NE Siberia.
- Canada: Mainly east of the Rockies and particularly: Labrador, Quebec, Ontario, Manitoba, Saskatchewan, Nunavut, Northwest Territories.
- Alaska: Low lying areas.
- Possibly some low lying parts of Southern Argentina.
Low Level Winds and Precipitation effects
If winds are light, melting of falling snow and/or evaporation of falling rain or snow, can cause local cooling down to surface levels. Significant 2m temperature errors may develop if aspects of precipitation are not well captured by the model.
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Persistent or heavy rainfall can produce waterlogged soil or flooded areas which will retard temperature rise.
Effects of aerosol and dust
Aerosol advected across a region can reduce incoming radiation. Aerosol Optical Depth (AOD) measures the extinction of a ray of light as it passes through the atmosphere. This can be due to advection of dust etc. A very crude rule of thumb is that an anomaly (with respect to climatology) of 1 AOD unit corresponds to a 0.5-1.5 °C day-time temperature decrease under otherwise clear skies. Cloud cover has a much stronger effect upon surface temperature and mask any signal from the aerosols. The radiative impact of the forecast aerosol value is more distinct for shorter lead-times (12 or 24 hours). At longer lead times, the evolving differences in flow patterns and clouds may become more important for the surface temperature differences than the reduced solar radiation. More information on aerosols and greenhouse gases is given elsewhere in the guide.
Fig9.2.1.1-6: Example of forecast error associated with passage of a zone of associated with saharan dust.
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.
- Model 2m temperature output corresponds to short grass cover, because by meteorological convention observations are ordinarily made over such a surface. This strategy may not work so well in:
- complex terrain - e.g. forests with clearings.
- over snow areas. The algorithm which derives 2m temperature uses the model surface temperature while the snow surface is above the earth's surface. This can be an important consideration where there is deep snow. See section on snow effects.
Suggested considerations to offset temperature errors
The forecaster should assess the potential for error due to the above factors by:
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