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Surface Wind

Mean Winds

A roughness length parameterisation is used to obtain a realistic, area averaged surface drag and this is used during post-processing to interpolate atmospheric model winds from 40m down to 10m assuming a roughness length over grassland

Fig9.3.1: Atmospheric model wind profiles over surfaces with differing roughness lengths (z). The roughness length of grassland is used to interpolate forecast winds from 40m down to 10m, but higher roughness lengths for trees or mountains would produce rather different low-level wind profiles.

Users should be aware that:

  • The surface wind over land is often rather low compared to verifying surface observations.  This is because for each grid box:
    • the roughness length parameterisation is imprecise as local surfaces, topography and orography vary considerably,
    • the rougher elements (trees, mountains) often predominate and the average roughness length is higher than for grassland.
  • Near-surface wind forecasts have weaknesses in some mountain areas, due to the difficulty in parameterising the interaction between the airflow and the highly varying sub-grid orography (see Fig9.3.2).
  • Local surface winds depend strongly on local exposure. 


Fig9.3.2:  Mean sea level pressure (MSLP) isobars and 10m wind from data time 00UTC 2 March 2011 T+12hr forecast verifying 12UTC 2 March 2011.  The 10m winds are unrealistically weak over the rugged Norwegian mountains.  Values of 10m/s might be realistic in sheltered valleys, but not on exposed mountain ranges.



Near surface gusts are computed by adding a turbulence component and a convective component to the mean wind:

Ugust  = U10 + 7.71U*  f(z/L) + 0.6 max(0,U850 -U925) 


  • U10 is the 10m wind speed (now the lowest atmospheric model level in HRES and ENS).

  • U* is the friction velocity – also obtained from the wind speed at the lowest model level

  • L is a stability parameter.
  • z is a roughness length
  • (U850 -U925). The convective contribution is taken as proportional to the positive wind shear between the model levels that correspond to 850hPa and 925hPa in a standard atmosphere, so the same model levels are used for this everywhere at all times.

Users should be aware that:

  • In spite of there being a convective contribution in the above equation, which does help, extreme gusts associated with vigorous convection (e.g. MCSs or squall lines) are generally under-estimated, sometimes by a factor of 2 (e.g. 60+kt gusts when 30-40kt gusts predicted).
  • Current resolution cannot represent the internal circulations of convective systems and empirical rules relating instability to gusts do not work in all cases.
  • Gusts can be as much as 2 to 3 times the mean wind speed, but this depends on stability of the atmosphere and roughness of the surface.  Users should consider local effects (e.g. funneling, particularly in stable conditions, katabatic flow, CAPE, CAPE-shear and convective activity).
  • ECMWF's CAPE and CAPE-shear EFI fields have been designed to help the forecaster to predict extreme weather related to deep moist convection; this includes predicting extreme convective gusts.
  • There can be an unrealistic localised reduction in gustiness over a relatively short distance over water (~2 grid lengths).  Strong gusts could be underestimated just offshore (lake or sea) when the wind is blowing off the land.

Winds related to Intense Extra-tropical Cyclones

More extreme extra-tropical cyclones can give rise to swathes of damaging winds.  Verification using many case studies has shown that in both cases both the mean winds and the gusts are quite well predicted, on average.  That is to say if the cyclone shape, central pressure and pressure gradients are well-forecast, then so are the winds, in general.  There is no clear evidence to suggest that extreme cyclones are under- or over-deepened on average, though some case studies show that the onset of the phase of most rapid deepening can sometimes come a little too late (meaning, for a standard eastward-moving low, a little too far east).  In turn this tends to mean that the onset of very strong winds may actually be further west than predicted, but conversely those very strong winds may actually not extend as far east as in model output.  A particularly difficult strong wind swathe to predict is the one associated with the rare phenomena known as the "sting jet".  Evidence from a few cases suggests that IFS gusts are not biased but are representative of the real gusts experienced during a sting jet event.  However because this phenomena is intrinsically difficult to predict, and because the damage swathe associated with it is narrow, and typically short-lived, one has to expect large errors in forecasts for specific locations in potential sting jet scenarios.  Because of these factors and the often finely balanced nature of extreme cyclogenesis events (that relate to sting jets) one must expect some jumpiness in wind forecasts in these situations, even at relatively short ranges.  This is particularly true for HRES but also, sometimes, for the ENS.  Nonetheless, the ENS can help a great deal with highlighting the intrinsic uncertainty in gust forecasts in very cyclogenetic situations.  Users are also encouraged to make use of the cyclone database products when dealing with strong winds related to cyclogenesis events.  These products were designed, in part, with that particular use in mind.

Fig9.3.3: Conceptual model of the life cycle of a cyclonic windstorm.  The areas prone to significant gusts are:

  • yellow zone: the warm jet or conveyor within the warm sector and/or at the cold front.
  • orange zone: the cold jet or conveyor within the cold sector on the northern flank (N Hem) of the depression.
  • red zone: the sting jet in the flow around the western flank of some depressions.

See cyclone database products for key to colours in the cyclone life-cycle phases.

Very small cyclones

The above guidelines hold for most cyclones, but very small cyclones pose extra difficulties. They may be beyond the capabilities of the IFS horizontal resolution, particularly for the ENS.  A windstorm that hit Brittany during March 17 (that was incorrectly labelled "Zeus") was one such example.  For this case there were systematic resolution-related differences in intensity between ENS and HRES.  HRES developed a more intense windstorm, and was more accurate.  There have been other cases too.  And even very small cyclones can have a sting jet associated.  Accordingly forecasters need to treat ENS output with particular care when there is potential for very small cyclones to develop - say with lateral dimensions of order 200km or less.

Additional Sources of Information

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

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