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2m Temperature errors:

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.  Most of the large errors seem to occur when the surface temperature is very cold, and the lowest levels may become extremely stable.   In such very stable air tiny amounts of energy can correspond to large temperature changes at the 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:

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Some of the above processes in turn depend on land surface characteristics (vegetation, soil type, soil texture, etc.) and processes.  

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 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).

Urban effects

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 cover 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).

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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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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.  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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Errors in wind profiles in the boundary layer, and in wind direction at the surface, are related to the representation of mixing in convective boundary layers, and in particular with the partition of momentum transport between dry and moist updrafts.

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.  Soil temperature and soil moisture is modelled in IFS but there is not a great deal of directly measured information available.  However, the impact of heat and moisture fluxes can be a significant contributor to 2m and surface temperature errors.

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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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Overnight 2m temperatures tend to be too cold over rugged or mountainous areas.

Lakes 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:

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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.

Analysis Problems

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.

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Fig9.2.1.4: 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!

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.

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, 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.  

Suggested considerations to offset temperature errors

The forecaster should assess the potential for error due to the above factors by:

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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 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.

2m Dew point and Humidity errors:

2m dew point temperature biases (verified over land) vary geographically, as well as with season and time of day with a daytime dry (low dew point) bias generally.   Large humidity errors can also occur (not always with the same sign as temperature or dew point errors).  Humidity errors often don’t depend strongly on the forecast values and range of temperature. 

Effects contributing to dew point temperature errors

Near surface dew points and humidity are related to a similar variety of processes to those for temperature:

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Some of the above processes in turn depend on land surface characteristics (vegetation, soil type, soil texture, etc.) and processes.  

Cloud Cover effects

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).

Turbulent Mixing effects

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 of the temperature gradient and especially the humidity gradient (giving a dry bias); 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.

Too much mixing increases the upward diffusion of heat and moisture and hence reduces the temperature and dew point fall at 2m and at the surface.   Errors in wind profiles in the boundary layer, and in wind direction at the surface, are related to the representation of mixing in convective boundary layers, and in particular with the partition of momentum transport between dry and moist updrafts.

Vegetation, Soil moisture and Evaporation 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.

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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.

Orography effects

IFS model orography smooths out valleys and mountain peaks, especially at lower resolutions.  A forecast 2m dew point 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.

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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.

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.

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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

Melting of snow, and evaporation of rain or snow, can cause local cooling that will be realised down to surface levels if winds are light or over relatively flat areas.  However, in areas that are not completely flat, any stronger winds will tend to combat this tendency, re-establishing a vertical lapse rate, via adiabatic warming or cooling during descent or ascent over topography, making low lying areas warmer than would have otherwise been the case. 

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Persistent or heavy rainfall can produce waterlogged soil or flooded areas which will increase the 2m dew point and humidity.

Analysis Problems

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).

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.

Suggested considerations to offset dew point errors

The forecaster should assess the potential for error due to the above factors by:

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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 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 Dewpoint

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 associated with soil moisture.

Impact of Heat and Moisture Fluxes

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.  Fig9.2.2 and Fig 9.2.3 illustrate the problem.   Users should recognise the impact that low-level moisture has upon temperature forecasts; if humidity is too low then maximum temperatures can be forecast to be too high (e.g. East England and Germany).

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 Fig9.2.3: 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 during the day on 12th. Dewpoint 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 errors could be much more a function of very local effects - e.g. proximity of a lake or river.

Summary of Soil temperature errors:

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. 

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Flooding may occur after heavy or prolonged rainfall but will not be modelled.  Incorrect soil characteristics and/or a water surface will cause errors in the forecast low-level temperatures.

Suggested considerations to offset soil temperature and moisture errors

The forecaster should assess the potential for error due to the above factors by:

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Users should recognise the impact that low-level moisture has upon temperature forecasts; if humidity is too low then maximum temperatures can be forecast to be too high.

Additional Sources of Information

(Note: In older material there may be references to issues that have subsequently been addressed)

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