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Observations of the “surface” wind are conventionally measured and reported as the wind 10m above the ground.  The lowest model levels (level 137 in HRES, ENS, Extended Range the medium range ensemble and the extended range ensemble or level 91 in Sethe seasonal ensemble) both lie close to this height and it would seem simplest to use these data directly as the forecast 10m wind.  However, to evaluate the momentum flux at the surface, with its consequent effect upon wind speeds at the lowest model levels, a representative "surface roughness" (the roughness length parameter) is assigned to each grid square within the model formulation. This naturally impacts upon the wind at the lowest model levels. This roughness length for the grid square will ordinarily not be typical for an individual location within that grid box (unless the grid box is very uniform).  Given the likely variability of vegetation and land use (e.g. see Fig9.3.2) one should not expect the lowest model level (level 137) data to agree well with observed winds.  

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Fig9.3.2: The red lines show the extent of a very approximate 9km x 9km schematic HRES grid schematic grid square surrounding a grid point (flag).  This is an example of the variability of land surface within an IFS grid box illustrating the difficulty in assigning a representative roughness length for the whole surrounding grid square.  HTESSEL uses up to six "tiles" to describe the different surfaces in the square to assess fluxes of momentum (Fig 9.3.4), and also fluxes of heat and moisture.  These values are used to evaluate the forecasted parameters (temperature, wind etc) at the grid point (flag).  An HRES meteogram  An HRES meteogram for a given location is interpolated from adjacent three grid points (flags) each derived from HTESSEL within its own surrounding grid square.  In the figure these are from adjoining grid squares that are outside of the picture.  An ENS meteogram is derived from the four grid points surrounding the station.  In  In this example the grid points might not include any wooded low-level grid points at all.  See Section on Selection of gridpoints for Meteograms for details.    

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To represent orographic features that are too small to be resolved by the ensemble model grid (9km HRES, 18km ENS) orographic ).  Orographic parameters are derived from the height of valleys, hills and mountains, taken from a land elevation data set with ~1km resolution which is first averaged to ~5km resolution.

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More extreme extra-tropical cyclones can give rise to swathes of damaging winds (Fig 9.3.6). Verification using many case studies has shown that in most cases both the mean winds and the gusts are reasonably well predicted, on average.  That is to say if the cyclone shape, central pressure and pressure gradients are well-forecast, then in general so are the winds.  There is also 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 Nevertheless, the ensemble 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 also extratropical cyclone diagrams when dealing with strong winds related to cyclogenesis events.  These products were designed, in part, with that particular use in mind.

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The guidelines in Section 1.1.6 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 2017 (that was incorrectly labelled "Zeus" in web links) 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 .  And even very small cyclones can have a sting jet associated.  Accordingly forecasters need to treat ENS ensemble output with particular care when there is potential for very small cyclones to develop - say with lateral dimensions of order 200km or less.

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