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Example of grid point data on meteograms

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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.

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The difference in geographical altitude reflects the hilly nature of land and towns near the lake.


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Fig8.1.5.10: 10-day medium-range meteogram for Vevey (on the shores of Lake Geneva) from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.


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Fig8.1.5.11: 10-day medium-range meteogram for Montreaux (on the shores of Lake Geneva) from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.

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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.

It is difficult to disentangle causes, but users need to be aware of possible reasons in each case.  Note that 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.

Example3: A mountainous oceanic island.

Canary Islands

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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.

Example sites are shown on the diagram:

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Example3: A mountainous oceanic island.

Canary Islands

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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.


Example sites are shown on the diagram:

  • A coastal town - St Cruz de Tenerife.  The ENS grid is scanned for the grid points surrounding the location (ENS 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 ENS grid is scanned for the grid points surrounding the location (ENS 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.  

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


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Fig8.1.5.13: 10-day medium-range meteogram for Santa Cruz de Tenerife from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.

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Fig8.1.5.14: 10-day medium-range meteogram for Mount Tiede from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.


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

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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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Fig8.Fig8.1.5.13: 10-day medium-range meteogram for Santa Cruz de Tenerife from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.

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Fig8.1.5.14: 10-day medium-range meteogram for Mount Tiede from HRES/Ensemble Control (blue line) and ENS 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 6.5K/km lapse rate.

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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Fig8.1.5.15: ENS 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.

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15: ENS 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.


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Fig8.1.5.16: 10-day medium-range meteogram for the town of Malfa on Malfa Island from HRES/Ensemble Control (blue line) and ENS members (box and whiskers) data time 00UTC 26 June 2023.   The ENS 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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Fig8.1.5.17Fig8.1.5.16: 10-day medium-range meteogram for the town of Malfa Stromboli on Malfa Stromboli Island from HRES/Ensemble Control (blue line) and ENS members (box and whiskers) data time 00UTC 26 June 2023.   The ENS grid is scanned for the grid points surrounding the location (ENS grid points ABCD).  None is a land point and nearest sea point is chosen (Point D).  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 ABCD (i.e. .  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  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.   

There are wide variations in orography within the islands (the islands are quite mountainous).   Grid points are almost exclusively over the sea so land effects will not be taken into account.  The representativeness of a grid point can be very uncertain though may be appropriate for coastal parts.  Inland parts of small islands will be largely similar to the coasts but nevertheless there is likely to be large local variations in conditions.  Local effects can be very important with local sea breezes, nocturnal breezes, shelter, etc.  Many small islands are mountainous - Malfa rises to 860m and the active volcano on Stromboli rises to 926m (the effects of volcanic activity are not dealt with by IFS).  

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

Considerations when viewing meteograms

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Fig8.1.5.18: 10-day medium-range meteogram for the Stromboli volcano on Stromboli Island from HRES/Ensemble Control (blue line) and ENS members (box and whiskers) data time 00UTC 26 June 2023.   The ENS 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.    


There are wide variations in orography within the islands (the islands are quite mountainous).   Grid points are almost exclusively over the sea so land effects will not be taken into account.  The representativeness of a grid point can be very uncertain though may be appropriate for coastal parts.  Inland parts of small islands will be largely similar to the coasts but nevertheless there is likely to be large local variations in conditions.  Local effects can be very important with local sea breezes, nocturnal breezes, shelter, etc.  Many small islands are mountainous - Malfa rises to 860m and the active volcano on Stromboli rises to 926m.    The effects of volcanic activity are not dealt with by IFS).  

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.  

IFS uses a spectral representation of orography and so there is some smoothing, particularly in mountainous areas.  This means that will have model station heights that are different from the  geographic height.  For the majority of locations the differences are relatively minor.  But there can be a significant difference at locations where there are large variations in geographic heights over a relatively small distance (e.g. deep valleys in rugged terrain, isolated steep islands, or coastal towns adjacent to mountainous regions).

Note: The station height on the meteogram is defined for:

  • named locations, towns, cities as the value of the model altitude at that point.
  • locations defined by latitude and longitude as the geographic altitude at that point.

Users should use meteogram output with caution - the data should not be taken as definitive but should be assessed and possibly adjusted.   ENS forecast values should not be taken at face value but there should always be consideration of the ways that temperature and other values are derived.  The effects of local influences are most important.  Disentangling coastal effects from altitude effects can be difficult. 

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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 locationpoint 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 (6.5K/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 ENS 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 ENS 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 ENS orography.  ENS 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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