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Evaluation of grid point data

For forecast ENS temperature data, all locations within each grid box surrounding a grid point are considered to have the same values as that forecast at the central grid point.  The fluxes of heat, moisture and momentum which in turn determine the surface values of temperature, dewpoint and wind at the grid point are calculated using the proportion of land within the surrounding area (where HTESSEL will be used) and lake/coastal seas (where FLake will be used).  For a sea grid point well offshore NEMO is be used to determine the surface fluxes of heat, moisture and momentum.

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Fig8.1.5.6: An example over southern England of "fraction of land cover" values showing the proportion of land and water within each 9km x 9km square centred on each grid point.  At grid point X the fluxes of heat, moisture and momentum  will be determined by 70%-80% by HTESSEL and 20%30% by FLake.  At grid point Y the fluxes of heat, moisture and momentum will be determined by 100% by FLake, even though the grid point lies over land. 

Selection of ENS grid point relevant for a chosen location:

For land locations:

• The nearest ENS grid point is selected from among the four ENS grid points surrounding the selected location.  Within these four grid points:
  • if there is at least one land grid point then the nearest land grid point is chosen (even though a sea grid point may be nearer).  A land grid point is one where the "fraction of land cover"  is greater than 50%.   
  • if there is no land grid point then the nearest ENS grid point is chosen (which will be a sea grid point).

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The process of selecting which gridpoints ENS that are used on meteograms is illustrated below, using relatively complex but informative examples.

Example of grid point data on meteograms

Fig8.1.5.8: 10-day medium-range meteogram for Oslo from HRES/Ensemble Control (blue line) and ENS members (box and whiskers) data time 00UTC 26 June 2023.   The map shows a close up of Oslo city.  The nearest land grid point to central Oslo is at 59.93N 10.83E which lies some 5km away from and some 141m higher than Oslo city centre.  This grid point may well be representative of Haugerud on the fringes of Oslo, but temperatures are reduced to near sea level using 6.5K/km lapse rate.

Examples of selection of grid point for meteograms

Example1: A medium sized island.

The Isle of Wight in southern England.   The island is approximately 40km long by 25km wide.  Coastal areas are strongly influenced by the sea while central parts are not.

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Fig8.1.5.7: ENS grid points over part of southern England.  Rectangles surrounding each grid point are coloured according to the "fraction of land cover" assigned to each grid point and shown by the scale on the right.  Within each rectangle all locations are considered to have the same values.  The fluxes of heat, moisture and momentum which in turn determine the surface values of temperature, dewpoint and wind at the grid point are calculated using the proportion of land (where HTESSEL will be used) and lake/coastal seas (where FLake will be used for lakes or shallow coastal water), or NEMO alone for grid points over open sea. Towns mentioned below are Ventnor (V), Bembridge (B), Freshwater (F) and the city of Portsmouth (P) and locations are marked by a cross.

Execution of the technique 

Example sites are shown on the diagram:

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In the above example, if winds were light and from the East (i.e. wind blowing from sea to land at Ventnor) the influence of the sea point S is helpful in the derivation of temperatures.  However, if the winds were from the north (i.e. wind blowing from land to sea at Ventnor) then the influence of the sea point S may not be relevant.

Example2: A lake surrounded by rugged orography.

Eastern Lake Geneva.  Vevey and Montreux are lakeside towns which are not far apart but have different grid points; one grid point has an altitude near lake level, the other has an altitude associated with the nearby mountains.

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• The area is very mountainous and different areas of each town have different altitudes.
• The model orography, though fairly detailed, is somewhat smoothed and does not exactly follow true land altitude at each grid point.
• Significant temperature adjustments are required from ENS grid point forecast values to better represent temperatures at the altitude of each town.  These adjustments make assumptions about the structure of the lower atmosphere.
• Speeds cannot be relied upon as winds are strongly modified by orography and local effects.

Example3: A mountainous oceanic island.

Canary Islands

Fig8.1.5.12: ENS grid points around the Canary Islands.  Rectangles surrounding each grid point are coloured according to the "fraction of land cover" assigned to each grid point and shown by the scale on the right.  Within each rectangle all locations are considered to have the same values.  The fluxes of heat, moisture and momentum which in turn determine the surface values of temperature, dewpoint and wind at the grid point are calculated using the proportion of land (where HTESSEL will be used) and coastal water (where FLake will be used), or NEMO alone for grid points over open sea.  Locations mentioned below are St Cruz de Tenerife and Mount Tiede; locations are marked by a cross.

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There are wide variations in orography within the islands (the islands are quite mountainous) and the representativeness of a grid point can be uncertain.

Example4: Isolated small islands.

Isole Eolie.  A set of small volcanic islands near southwest Italy.  The islands are roughly 5km x 5km or smaller.

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It is for the user to make adjustments to meteogram values, particularly temperature.

Considerations when viewing meteograms

The method of assessment and delivery of data for presentation on meteograms has been described in detail to give an understanding of the techniques involved.  

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