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Modelling the surface energy flux is crucial to the effectiveness of the grid point data as a basis for production of a meteogram at a given location.  A colour code shows the proportion of surface energy fluxes from land or water sources surrounding each grid point.  This is described in the section: Modelling surface energy fluxes. 

Where the selected ensemble point is:

• inland, data is calculated using HTESSEL within the grid point box surrounding the location of interest.
• in coastal areas, data is calculated using HTESSEL and FLake according to the proportions of land and coastal sea cover within the grid point box surrounding the location of interest. 
• over the ocean, data is calculated using NEMO.

More details are given in the sections: Selection of grid points for meteograms, Land-sea mask,  Modelling land surfaces, Modelling coastal waters,  Modelling lake and coastal waters, Modelling ocean surfaces. 

Some influences of the adjacent sea areas or mountains may be over- or under-represented by the ensemble meteograms.  This can significantly affect the forecast parameter on the meteogram (temperature, wind, etc).    Users should assess differences in meteograms for coastal, island or mountainous regions.   In particular consider:

• the impact of the grid point(s) relative to the land-sea mask, especially where surface winds might blow onshore or there are differences in instability over sea or land.
• the variation of the altitude of the land, especially when compared with the model representation of orography.   Forecast values at the grid point nearest to the location are adjusted for altitude using a standard lapse rate assumption.  The difference in surface temperature can be considerable.

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Fig8.1.4-1: 10-day medium-range meteogram for Oslo from Ensemble Control Forecast (blue line) and ensemble 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 5.5K/km lapse rate.

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Fig8.1.4.1-2: Ensemble 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.

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• An inland location - Newport (location N).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points NPJR) and the nearest land point is chosen (Point J).  This is a land point where the "fraction of land cover" is 100% and the surface energy fluxes are determined by HTESSEL.
• A coastal city location - Portsmouth (location P).   The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points ABCD) and the nearest land point is chosen (Point A).  This is a land point where the "fraction of land cover" is 100% and the surface energy fluxes are determined by HTESSEL.  There will be no influence of a water surface.  HTESSEL does not take into account the urban nature of the city. 
• A coastal location - Freshwater (location F).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points MNRS) and the nearest land point is chosen (Point R).  This is a land point where the"fraction of land cover" is 60%-70%.  Surface energy fluxes are determined 60%-70% by HTESSEL and 30%-40% by FLake.
• A coastal location - Bembridge (location B).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points EFGH).  None is a land point and a sea point is chosen (Point E).  At this point the "fraction of land cover" is less than 50% and the surface energy fluxes are determined by FLake.  There will be no influence of land energy fluxes.  In fact any land location within grid box EFGH will be treated similarly, no matter how far away from the coast. 
• A coastal location - Ventnor (location V).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points JHLK) and the nearest land point is chosen (Point J).  This is a land point where the "fraction of land cover" is 100% and the surface energy fluxes are determined by HTESSEL.  No adjustment is made for the influence of the sea and the effect of the sea may not be evident on ensemble meteograms.  This grid point is the same as selected for the inland town of Newport (location N) even though the town of Ventnor (location V) is right on the coast. 
• A location near land - offshore of Ventnor (location S).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points JHLK) and the nearest sea point is chosen (Point L).  The surface energy fluxes are determined by FLake.  The influence of the sea will be more evident on ensemble meteograms for location S than at location V. 

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Fig8.1.4.1-3: Ensemble grid points over Lake Geneva.  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 (where FLake will be used). Towns mentioned below are Montreux (M) and Vevey (V); locations are marked by a cross.

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• A lake side location - Montreux (location M).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points DEFG) and the nearest land point is chosen (Point D).  This is a land point where the "fraction of land cover" is 50%-60%.  Surface energy fluxes are determined 50%-60% by HTESSEL and 40%-50% by FLake.  The grid point D actually lies at Montreux itself.  However it has a model altitude of 801m while Montreaux has a geographical altitude of 582m. 
• A lake side location - Vevey (location V).  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points ABCD) and the nearest land point is chosen (Point A).  This is a land point where the "fraction of land cover" is 60%-70%.  Surface energy fluxes are determined 60%-70% by HTESSEL and 30%-40% by FLake.  The grid point A has a model altitude of 693m while Vevey has a geographical altitude of 412m. 

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Fig8.1.4.1-4: 10-day medium-range meteogram for Vevey (on the shores of Lake Geneva) from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The nearest land grid point to Vevey is at 46.50N 6.79E which lies some 5km away from and some 281m higher than Vevey city centre.  This grid point may well be representative of the mountains to the northeast of Vevey, but temperatures are reduced to Vevey level using 5.5K/km lapse rate.

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Fig8.1.4.1-5: 10-day medium-range meteogram for Montreaux (on the shores of Lake Geneva) from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The nearest land grid point to Montreaux is at 46.43N 6.92E which is almost coincident with the city.  However, the model altitude is some 219m higher than Montreaux city centre.  Temperatures are reduced to Montreaux level using 5.5K/km lapse rate.

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Fig8.1.4.1-6: Ensemble 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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• A coastal town - St Cruz de Tenerife.  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points ABCD) and the nearest land point is chosen (Point A).  This is a land point where the "fraction of land cover" is 90%-100% and the surface energy fluxes are determined by HTESSEL.  
• A mountain location - Mount Tiede.  The ensemble grid is scanned for the grid points surrounding the location (ensemble grid points EFGH) and the nearest land point is chosen (Point F).  This is a land point where the "fraction of land cover" is 100% and the surface energy fluxes are determined by HTESSEL.  The grid point F actually lies at the peak of Mount Tiede itself.  However it has a model altitude of 1977m while Mount Tiede has a geographical altitude of 3385m.  

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Fig8.1.4.1-7: 10-day medium-range meteogram for Santa Cruz de Tenerife from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The nearest land grid point to Santa Cruz is at 28.51N 16.28W which lies some 5km away from and some 173m higher than Santa Cruz.  This grid point may well be representative of the hills to the northeast of Santa Cruz, but temperatures are reduced to Santa Cruz level using 5.5K/km lapse rate.

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Fig8.1.4.1-8: 10-day medium-range meteogram for Mount Tiede from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The nearest land grid point to Mount Tiede is at 28.30N 16.63W which is almost coincident with the mountain peak.   However, the model altitude is some 1408m lower than the height of the mountain.  Temperatures are corrected to mountain peak level using 5.5K/km lapse rate.

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Fig8.1.4.1-9: Ensemble grid points around southwest Italy.  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 marked on the diagram.

The grid points either touch the islands but with less than 50% land cover, or miss the islands completely.  All fluxes of heat, moisture and momentum are derived using  FLake.

Fig8.1.4.1-10: 10-day medium-range meteogram for the town of Malfa on Malfa Island from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The ensemble grid is scanned for the grid points surrounding the location.  None is a land point and nearest sea point is chosen.  This point is actually situated on land but the "fraction of land cover" is less than 50% and the surface energy fluxes are determined by FLake.  There will be no influence of land energy fluxes.  In fact the whole island including the mountains will be treated similarly, no matter how far away from the coast.  This grid point may well be representative of the southwest coast of the island.  However, local effects may be important on other coasts (e.g. sea breezes).  Conditions at inland high ground will not be reliably indicated, particularly for Monte dei Porri which rises to 860m.   

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Fig8.1.4.1-11: 10-day medium-range meteogram for the town of Stromboli on Stromboli Island from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The ensemble grid is scanned for the grid points surrounding the location.  None is a land point and nearest sea point is chosen.  There will be no influence of land energy fluxes.  In fact the whole island including the mountains will be treated similarly, no matter how far away from the coast.  Local effects may be important (e.g. sea breezes).  Conditions at inland high ground will not be reliably indicated.   

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Fig8.1.4.1-12: 10-day medium-range meteogram for the Stromboli volcano on Stromboli Island from Ensemble Control Forecast (blue line) and ensemble members (box and whiskers) data time 00UTC 26 June 2023.   The ensemble grid is scanned for the grid points surrounding the location.  None is a land point and nearest sea point is chosen.  There will be no influence of land energy fluxes.  In fact the whole island including the mountains will be treated similarly, no matter how far away from the coast.  Conditions at inland high ground will not be reliably indicated.  Note the temperature data at the sea grid point (model height -8m due to the spectral representation of altitude) is amended to that at 422m (the model height at Stromboli volcano) which is itself less than the true geographic height of 926m.    

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Fig8.1.4.1-13: Schematic of the spectral representation of orography.   Model orography matches true orography over large parts of the earth but is less exact in rugged mountainous regions.  See also Section on Model Orography for further points regarding orography.

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• critically assess forecast values in the light of experience regarding differences between previous forecast values and actual observed observations.
• consider the representativeness of the meteogram in coastal, island or mountainous regions and take into account consequent differences in height between altitude of the grid point and that of the desired town or location.
• consider the variation in temperature (and possibly precipitation) that might be expected in different parts of the town or city.  Some cities spread from sea level to a few hundred metres in altitude.
• consider the structure of the lower atmosphere as IFS temperature adjustments make assumptions of a uniform lapse rate (5.5K/km).  In the case of temperature inversions the forecast of 2m temperature needs to be used with great care; in such situations, depending on the inversion level, the standard lapse rate assumptions can be very inappropriate.
  
• consider meteograms for nearby offshore locations which can add useful information for adjacent coastal locations.
• consider if the same ensemble grid point has been selected by IFS for both inland and coastal locations.  Meteograms may not indicate correctly the differences between the locations.  
• note influences of any adjacent sea areas on coastal areas may be over- or under-represented by the ensemble meteograms.  
• assess the effect of the forecast winds (e.g. if the wind blows from land to sea then the influence of a nearby sea point may not be relevant).
• assess whether a local effect might be relevant (e.g. onset of a sea breeze), or the local prevalence of persistent cloud (e.g.sea fog and low cloud drifting onshore), or the influence of turbulent mixing with stronger winds. 
• consider the land-sea mask value(s) at the grid point and the consequent impact of heat, moisture and momentum fluxes by HTESSEL and FLake on the forecast parameters.
• note some areas well inland from coasts can be governed by fluxes derived using FLake.  Sea grid point (defined as a grid point surrounded by >50% water surface) can be over land and have an altitude defined by ensemble orography.  Ensemble sea grid points do not always have an altitude of 0m.
• note the spectral representation of orography in the IFS, can:
  • smooth model orography and local effects can be under-identified.
  • lead to "topographic ripples" over adjacent sea/large lakes, which decay with offshore distance, and which are most prominent where there are steep-sided high mountains nearby.
• note wind speeds cannot be relied upon in mountainous areas as winds are strongly modified by orography and local effects.

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  It is for the user to assess critically the representativeness of the meteogram displayed and to make adjustments in the light of local knowledge and experience.

(FUG Associated associated with Cy50r1)