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Sub-grid drag processes have a major impact upon the momentum flux at the surface and the evaluation of low-level winds. However, it is difficult to establish appropriate values for individual locations or areas. The effect on the momentum transfer at the surface is parameterised by a "roughness length" for the various vegetation types or land usages and may well vary with time as crops grow or are harvested, with autumnal leaf fall, or with widespread forest clearances etc. The setting of roughness lengths has substantial uncertainty.
Fig93Fig9.A3-1:Typical atmospheric model wind profiles over surfaces with differing roughness lengths (z).
Observations of the “surface” wind are conventionally measured and reported as the wind 10m above the ground. The lowest model levels (level 137 in the medium range ensemble and the extended range ensemble or level 91 in the 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 Fig93BFig9.3-2) one should not expect the lowest model level (level 137) data to agree well with observed winds.
Stations sited according to WMO guidelines (which ECMWF uses for verification) are located in open terrain, over short grass and with no obstacles in the vicinity; hence a low roughness length parameter is appropriate. However the roughness length parameter assigned to the grid square will usually be greater. Consequently observed winds at WMO station sites will often be stronger than those forecast at the lowest model level (level 137). This is because of the influence of areas of greater roughness within the grid square. ECMWF addresses this discrepancy as described immediately below.
Fig93Fig9.B3-2: The red lines show the extent of a very approximate 9km x 9km 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 (Fig93Fig9.D3-4), 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 ENS meteogram for a given location is interpolated from adjacent four 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. 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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Whilst such adjustments should lead to better agreement with WMO station measurements, there are still, arguably, issues with the 10m wind output in regions with very irregular (very mountainous) topography, as illustrated on Fig93Fig9.C3-3.
Fig93Fig9.C3-3: 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.
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- over land, is dependent upon the surface vegetation (See Fig93Fig9.D3-4).
- over sea, the drag on the low-level air flow is modelled using the Charnock parameter. This includes an aerodynamical roughness length that is a function of the 10m wind speed, but limited to a maximum value of that associated with 10m winds of 40m/s. Read more information about coupling between the atmosphere and ocean waves in waves near tropical storms.
Also considered is a representation of drag from small scale orography. Flow around steep orographic features gives temporary, variable and quickly fading atmospheric waves that lead to drag - Turbulent Orographic Form Drag (TOFD).
Fig93Fig9.D3-4: Sub-grid drag mechanisms. Scales smaller than 5km. Roughness length parameters (z) indicate the relative effects of different surfaces. It is important to note that roughness length (z) is only a parameter for use within the calculations; it does not represent typical heights of the obstruction.
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- low-level blocking (strong drag at lower levels where the flow is forced around a mountain) - red arrows in Fig93EFig9.3-5
- orographic gravity wave effects (gravity waves are excited by the “effective” sub-grid mountain height, i.e. height where the flow has enough momentum to go over the mountain) - black arrows in Fig93Fig9.E3-5
- low-level blocking (strong drag at lower levels where the flow is forced around a mountain) - red arrows in Fig93EFig9.3-5
Fig93Fig9.E3-5: Sub-grid drag mechanisms - topographic. For scales larger than 5km.
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- Local surface winds depend strongly on local exposure.
- Surface winds vary on small space and time scales.
- Sometimes verifying sites are not well exposed, particularly at unofficial locations. Winds may not be measured as well in some directions as in others.
- Near-surface wind forecasts have weaknesses in many mountain areas, due to the difficulty of parameterising interaction between the airflow and the highly varying sub-grid orography (see Fig93Fig9.C3-3).
- When interpolating (as for meteograms) the positions and altitudes of IFS grid points surrounding a selected location of interest have a significant impact upon the forecast values.
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More extreme extra-tropical cyclones can give rise to swathes of damaging winds (Fig93Fig9.F3-6). 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. 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.
Fig93Fig9.F3-6: Conceptual model of the life cycle of an extreme cyclonic windstorm. The areas prone to significant gusts are:
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