Section 9.2.1 Causes of Errors in Forecast Temperature and Humidity

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.  

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 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.

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 optical properties
• albedo and radiative transfer
• precipitation
• surface fluxes
• turbulent diffusion in the atmosphere
• strength of land-atmosphere coupling
• soil moisture and temperature

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 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.

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.  

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).

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 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:

• Where too much snow is analysed or forecast then forecast 2m temperatures may drop too low.
• Where insufficient snow is analysed (or cleared too readily by the forecasted passage of weather systems) then forecast 2m temperatures may be too high.  
• In marginal snow situations, when precipitation at the surface comprises both rain and snow, the snow component accumulates as lying snow whereas in reality it would usually melt instantly.  This can lead to the proliferation of areas where a there is an incorrect small amount of snow cover.
• Even if snow-cover is reasonably well-represented large errors in forecast 2m temperatures can still occur.

At a given location, at a given time, all the snow on the ground is assumed to have the same density.  This density varies according to:

• 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.

Forecast temperatures can be in error by:

• as much as 10°C too warm (very occasionally even worse) where there is:
  
  • snow cover under anticyclonic conditions and light winds,
  • strong radiation under clear skies leading to a strengthening night-time surface inversion.

(Both the above scenarios are difficult to forecast, but Finland, Scandinavia and large areas of northern Europe are particularly prone to forecast temperatures being too warm).

• as much as 5°C or 10°C too low where there is:
  • spurious snow cover (i.e. snow that should have melted).  

(This effect is particularly evident where the model retains a snow depth >10cm, the level at which the ground is assumed to be completely covered).

Currently snow is modelled by a multi-layer snow scheme allowing 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)).

Fig9.2.1-2: 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.

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.

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.

Too much mixing increases the upward diffusion of heat,  hence reducing stability and/or temperature inversion and consequently the temperature fall at 2m and at the surface.  Errors tend to be much larger during low level inversion situations.  Note:

• • 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 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.  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.  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).

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).

The aspect of a location (i.e. orientation relative to the sun) is not taken into account.  Thus:

• more solar energy will be gained on south-facing (N Hem) slopes implying actual temperatures may be higher than forecast,
• 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 and in sheltered valleys.

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

Fig9.2.1-3: 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 of 2-metre temperature can develop and therefore temperatures in valleys drop considerably below freezing whilst they stay much higher even positive on mountain tops.  Temperatures high up can be positive day and night whilst in the valley inversions formed with a well-marked diurnal cycle – sub-freezing temperature at night in some places as low as -7 / -8°C but warmer at day time.  And the largest model errors tend to occur in the mountain tops where the model cannot represent 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. 

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:

• amplify errors in forecast 2m temperatures, and
  
• introduce biases...

There are a number of complications which are not fully understood regarding the influence of frozen or snow-covered lakes upon the forecasting of low-level and 2m temperatures.  Lake temperatures can have a significant effect on forecast of temperatures, particularly where there is uncertainty whether the lake is frozen or not.  The impact can be significant leading to some uncertainty in forecast 2m temperatures in areas with many lakes.   Some high latitude areas where lakes are plentiful and prone to winter-time freezing are:

• 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.  

In areas that are not completely flat, any stronger winds will tend to combat this tendency.  Low-lying areas can be warmer than would have otherwise been in the case (e.g. by re-establishment of a vertical lapse rate via adiabatic warming or cooling during descent or ascent over topography).  However, there is the further risk of turbulence cloud developing which can also decrease radiative cooling in the lower layers.

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.

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.  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 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.
• 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.  

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:

• comparing analyses of temperature, 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).

When assessing a forecast of 2m temperatures, users should try to assess whether:

• the snow-cover is adequately analysed or forecast, especially taking into account:
  • any observations of snow cover and density,
  • the tendency for insufficient melting of snow on the ground.
• the cloud cover is adequately analysed or forecast.
• the boundary layer temperature (and humidity) structure is adequately analysed.
• wind strength is adequately modelled.
• the temperature and moisture structures of the lower atmosphere are well represented.

Thus if:

• 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:

• cloud cover and cloud optical properties
• radiative transfer
• precipitation
• surface fluxes
• turbulent diffusion in the atmosphere
• strength of land-atmosphere coupling
• soil moisture and temperature

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 (Fig9.2.1-1).

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.

The 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).  In particular, spring evaporation is too high, and summer vegetation gets into stress conditions too quickly (over-depletion of soil moisture).  

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.

The aspect of a location (i.e. orientation relative to the sun) is not taken into account.  Thus:

• 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:

• amplify errors in forecast 2m dew point temperatures, and
  
• introduce biases...

There are a number of complications which are not fully understood regarding the influence of frozen or snow-covered lakes upon the forecasting of low-level and 2m dew point temperatures.  Lake temperatures can have a significant effect on forecast of temperatures and dew points, particularly where there is uncertainty whether the lake is frozen or not.  In warmer seasons, low-level humidity can be increased by the influence of lakes.  The impact can be significant leading to some uncertainty in forecast 2m temperatures and 2m dew points in areas with many lakes.   Some high latitude areas where lakes are plentiful:

• 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. 

Persistent or heavy rainfall can produce waterlogged soil or flooded areas which will increase the low-level humidity.

If winds are light, melting of falling snow and/or evaporation of falling rain or snow, can cause local cooling down to surface levels and an increase in low-level humidity.  Significant 2m temperature and 2m Dew point errors may develop if aspects of precipitation are not well captured by the model.

In areas that are not completely flat, any stronger winds will tend to combat this tendency.  Low-lying areas can be warmer than would have otherwise been in the case (e.g. by re-establishment of a vertical lapse rate via adiabatic warming or cooling during descent or ascent over topography).  However, there is the further risk of turbulence cloud developing which can also decrease radiative cooling in the lower layers.

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:

• comparing analyses of temperature, 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).

Errors in the prediction of temperature has a strong influence on humidity forecasts, particularly in the lowest layers.  When assessing a forecast of 2m dew points, consider also any potential problems with the forecasts of 2m temperature (outlined above).  Further attention should be given to whether:

• the boundary layer temperature (and humidity) structure is adequately analysed.
• wind strength is adequately modelled.
• the temperature and moisture structures of the lower atmosphere are well represented.
• the ground surface is flooded.

Thus if:

• 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 issues and 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.  Fig9.2-4 & Fig9.2-5 illustrate the problem.   Low-level moisture 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.

Fig9.2.1-4: An example of incorrect assessment of heat and moisture fluxes (left, temperatures; right, dew points), at Cordoba 12 June 2017: HRES forecast temperatures and dew points (red) and observed temperatures and 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 super-adiabatic near surface layers that one sometimes sees on radiosonde ascents.

The right panel shows that on this occasion the magnitude of the 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.

 Fig9.2-5: 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 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 dew point 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. 

The ensemble mean values of soil moisture slightly overestimate the diurnal cycle of soil temperature:

• First (top) soil layer up to 2°C too cold at night.
• All other (lower) soil layers are always too cold.

Investigation suggests too much energy is exchanged between the atmosphere and the land.  During the night too much energy is extracted from the soil and transferred to the atmosphere. This results in:

• soil temperatures that are too cold.
• earth skin temperatures and 2m temperatures that are too warm.

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:

• comparing analyses of temperature, 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).

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)

• See the impact of Cycle 47R3 on representation of soil moisture and cloud cover effects.