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Note: HRES and Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control Forecast (ex-HRES) 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 temperatures are generally <0.5°C.  Biases

Biases in 2m temperature (verified over land) vary geographically, as well as with season, time of day and altitude.  with:

• geography.
• altitude.
• season.
• time of day.

Larger biases and errors occur over orography or in in:

• mountainous regions, particularly where model and actual surface altitudes are dissimilar.  
• snow covered areas

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• , particularly in extremely stable conditions.

Diurnal temperature changes

Diurnal temperature changes are strongly influenced by incoming and outgoing heat heat flux which itself . This is principally governed by the extent and thickness of cloud cover.  Uncertainty in the model analysis  Model analysis and forecast of cloud and fog 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 of the atmosphere may become extremely stable.   In such very stable air tiny  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

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

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

This is the main physical reason for large .  

Errors in development and representation of near-surface inversion and/or low cloud cover is influential regarding errors in forecast surface and 2m temperatures.  These conditions are more likely with high pressure and cold, calm conditions and errors are relatively commonplace in such circumstances.  

It is vital to compare the observed and forecast thickness, the extent of low cloud, and 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 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.

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