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Evaluation

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of grid point data

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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.

Energy flux information at each grid point is governed by the "fraction of land cover" assigned to the area surrounding it (see Fig8.1.54.6A1-1).   Thus grid points in rectangles that are coloured:

• dark green are land points and HTESSEL will supply 90-100% of the flux information.
• mid-green are land points (but with 10-20% water surface) so HTESSEL will supply 80-90% and FLake 10-20% of the flux information.
• light green are land points (but with 20-30% water surface) so HTESSEL will supply 70-80% and FLake 20-30% of the flux information.
• turquoise are land points (but with 30-40% water surface) so HTESSEL will supply 50-60% and FLake 30-40% of the flux information.
• cyan are land points (but with 40-50% water surface) so HTESSEL will supply 50-60% and FLake 40-50% of the flux information.
• blue are sea points (i.e. >50% water surface) so FLake will supply 100% of the flux information in coastal waters.  NEMO   NEMO will supply 100% of the flux information in oceanic waters.
  

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• At coastal locations where there is less than 50% land cover in a grid box the water proportion is treated as a lake (using Flakeusing FLake) rather  rather than as an ocean (which would use NEMO).  

• Some water surfaces (e.g. The Great Lakes) are classed as lakes rather than sea and FLake is used exclusively.

Fig8.1.54.6A1-1: 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 by HTESSEL and  and 20%30% by FLake.  At grid point Y the fluxes of heat, moisture and momentum will be determined by 100% by FLake, even  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.

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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Fig8.1.5.6A: 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 sea grid points. 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 outlined above 

Example sites are shown on the diagram:

Example of grid point data on meteograms

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Fig8.1.4-2: 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.

Image Added

Fig8.1.4.1-3: 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:

• An inland location - Newport (location N
• An inland location - Newport (location N).  The ENS grid is scanned for the grid points surrounding the location (ENS 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 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 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 ENS grid is scanned for the grid points surrounding the location (ENS grid points MNRSNPJR) and the nearest land point is chosen (Point RJ).  This   This is a land point where the "fraction of land cover" is 60%-70%.  Surface 100% and the surface energy fluxes are determined 60%-70% by HTESSEL and 30%-40% by FLakeby HTESSEL.
• A coastal city location -
Bembridge
• Portsmouth (location
B
• P).
 The
•   The ENS grid is scanned for the grid points surrounding the location (ENS grid points
EFGH).  None is a
• ABCD) and the nearest land point
and a sea point
• is chosen (Point
E
• A).
 At this point
•  This is a land point where the "fraction of land cover" is
less than 50%
• 100% and the surface energy fluxes are determined by
FLake
• HTESSEL.  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 water surface.  HTESSEL does not take into account the urban nature of the city. 
• A coastal location - Ventnor Freshwater (location VF).  The ENS grid is scanned for the grid points surrounding the location (ENS grid points JHLKMNRS) and the nearest land point is chosen (Point JR).   This  This is a land point where the "fraction of land cover" is 100% and the surface 60%-70%.  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 ENS 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 ENS grid is scanned for the grid points surrounding the location (ENS grid points JHLK) and the nearest 60%-70% by HTESSEL and 30%-40% by FLake.
• A coastal location - Bembridge (location B).  The ENS grid is scanned for the grid points surrounding the location (ENS grid points EFGH).  None is a land point and a sea point is chosen (Point LE).  The  At this point the "fraction of land cover" is less than 50% and the surface energy fluxes are determined by FLake.  The influence of the sea will be more evident on ENS meteograms for location S than at location V. 

Users should note:

• Inspection of meteograms for nearby offshore locations (e.g. location S) may add useful information for nearby coastal locations (e.g. location V).
• The same ENS grid point is used for both locations N and V (even though location N is inland and location V is coastal).  Differences between the inland location N and the coastal location V will not be apparent.  
• Temperature is adjusted to reflect the differences in height between the altitude of each location and the corresponding ENS orography, using a lapse rate of 6.5K/km.
  

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.

Users should consider the relevance or impact of:

• a local effect (e.g. onset of a sea breeze).
• the local prevalence of persistent cloud (e.g.sea fog and low cloud drifting onshore).
• the influence of turbulent mixing with stronger winds.
• the effect of altitude.

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.  Disentangling coastal effects from altitude effects can be difficult. 

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Fig8.1.5.7: 10-day medium-range meteogram for Ventnor (a coastal location in southern England) from HRES and ENS data time 00UTC 09 May 2017.  During the first three days of the forecast the HRES temperature (blue line) is consistently cooler than the ENS members which are showing very little spread.  The ENS grid point is inland but the HRES temperature is interpolated from the three HRES grid points nearest to the location of the selected ENS grid point and adjusted for altitude from the HRES orography to the ENS orography.  The diagnosis of discrepancies between HRES and ENS meteograms is complex and it can be difficult to disentangle causes, but users need to be aware of possible reasons in each case.  Discrepancy may possibly be due to altitude-related temperature adjustments, and/or to differences in HTESSEL and FLake tiling at the ENS and HRES grid points.

Example2: A lake surrounded by rugged orography.

Eastern Lake Geneva.

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Fig8.1.5.8A:  ENS grid points around Lake Geneva.  Only one grid box has less than 50% land and any land locations within that box will be considered as if over water.  The other turquoise shades show the proportion of water cover within the each box and defines the proportional influence of FLake and HTESSEL for any point within the grid box.  For example, a point (T) towards the northeast of the right hand box surrounding grid point Q (coloured turquoise as between 50% and 70% land) even though remote from Lake Geneva will nevertheless experience 50% to 30% influence of the lake. Vevey is marked V on the chart.  Grid boxes are coloured according to the "fraction of land cover" scale on the right.

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Fig8.1.5.8B:  HRES grid points around Lake Geneva.  Much more surface detail is captured by the grid, resulting in a more realistic representation of the influences of land and water within each grid box.  Grid boxes are coloured according to the "fraction of land cover" scale on the right.

In the case shown in the diagram:

For ENS Meteograms:

Vevey (marked V in Fig8.1.5.8) is a town on the eastern shores of Lake Geneva and  is surrounded by three ENS land grid points (PQR) and one ENS lake grid point (S).   The nearest ENS land grid point to location V is grid point Q and this is selected as representative of Vevey, or any other location within the ENS box centred on the ENS grid point (Q).  The area around grid point (Q) (coloured turquoise) indicates 50-70% land cover with HTESSEL providing energy fluxes and 30-50% water cover with FLake providing energy fluxes. 

The temperature at a given location is adjusted using ENS orography from the ENS temperature (at the ENS altitude of the ENS grid point) to the ENS altitude at the desired location using a standard lapse rate (6.5K/km).  Temperatures can be adjusted higher or lower according to the difference in altitude between grid point and the location in question.  There are wide variations in orography within the area and the representativeness of a grid point can be uncertain.

Note, ENS sea or lake grid points do not always have an altitude of 0m.

• A 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.
• The spectral representation of orography in the IFS, can 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.

For HRES meteograms:

• The ENS grid point (Q) lies exactly on the line between two points (AB) and HRES values are linearly interpolated.  Both HRES grid points are over land:
  
  • Point A has 50-70% land cover (with 30-50% water surface) and HTESSEL will supply 50-70% and FLake 30-50% of the flux information. 
  • Point B has 50-70% land cover (with 30-50% water surface) and HTESSEL will supply 50-70% and FLake 30-50% of the flux information. 
• The values at these two points are linearly interpolated to the ENS grid point (Q).

Therefore, the HRES meteogram for any point within the HRES box centred on the ENS grid point. will predominantly be influenced by the land but retain some influence from the lake.  

The temperature at a given location is adjusted using HRES orography from the interpolated HRES temperature (at the HRES altitude of the ENS grid point) to the HRES altitude at the desired location using a standard lapse rate (6.5K/km).  Temperatures can be adjusted higher or lower according to the difference in altitude between grid point and  the location in question.  There are wide variations in orography within the area and the representativeness of a grid point, even though interpolated from three HRES grid point value, can be uncertain.

The temperature at a given location is adjusted for altitude using HRES orography (more detailed than ENS orography) from the 2m temperature using a standard lapse rate (6.5K/km).

Accordingly on the meteogram, because of the complexities of the orography around the location:

• the influence of the lake has a significant impact on the energy fluxes in HRES.  The area is very mountainous and there are large differences between HRES and ENS orography (see Metadata with the meteogram) and the true altitude of Vevey.  Thus significant temperature adjustments are required from ENS and HRES forecast values to better represent temperatures at the altitude of Vevey.  These adjustments make assumptions about the structure of the lower atmosphere.
• temperatures have greater diurnal variation in HRES (there is much greater diurnal radiative heating and cooling variation over land, which is only represented in HRES because only that uses HTESSEL for this location).
• the night-time temperature discrepancies are hard to disentangle but may be due to significant differences in temperature adjustment down to the level of Vevey because of large differences between HRES and ENS orography and laps rate assumptions, or differences in HTESSEL and FLake tiling at the HRES grid points.
• winds are just a little weaker (~1m/s) in HRES than in ENS (both have high surface roughness but ENS is higher in altitude).   However, speeds cannot be relied upon as winds are strongly modified by orography and local effects.

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Fig8.1.5.9: 10-day medium-range meteogram for Vevey (on the shores of Lake Geneva) from HRES and ENS data time 12UTC 20 May 2017.  During Sunday, Monday and Tuesday of the forecast the HRES night-time temperatures (blue line) are consistently warmer than the ENS members which are showing very little spread.  The ENS grid point is just inland but the HRES temperature is interpolated from the three nearest HRES grid points to the location of the selected ENS grid point with significant influence of the lake from three grid points. The diagnosis of discrepancies between HRES and ENS meteograms is complex and it is difficult to disentangle causes, but users need to be aware of possible reasons in each case.  The station is on the lake shore and the discrepancy may be due possibly to significant differences in temperature adjustment down to the level of Vevey because of large differences in orography of HRES and ENS, or possibly differeces in HTESSEL and FLake tiling at the HRES grid points. The heights from the HRES and the ENS are displayed as an indication of the heights used for the temperature correction.  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 small oceanic island.

El Hierro, Canary Islands

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 Fig8.1.5.10A: ENS grid points around the Canary Islands.  Note several island points have a fairly high proportion of sea in their grid box, and some points on islands have no land within their grid box and are considered as sea points. The island of El Hierro, marked EH on the chart, is not captured at all. Grid boxes are coloured according to the "fraction of land cover" scale on the right.

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Fig8.1.5.10B: Magnified view of ENS  grid points around El Hierro.  Even though two ENS grid points are on the land the land-sea mask indicates more than 50% of the grid box is water covered and therefore NEMO will be used to assess energy fluxes.

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Fig8.1.5.10C: HRES grid points around El Hierro.  Note there are more points on land.; one wholly land point and three partial land points with sea influence.   Grid boxes are coloured according to the "fraction of land cover" scale on the right of fig8.1.5.10A.

In the case shown in the diagram:

For ENS Meteograms:

For most of the island, the nearest ENS grid point (P or Q) is selected (which is an ENS sea point although geographically over the land).   However, in the far south of the island, there is no ENS grid point geographically over land and the nearest ENS grid point (R) is a sea point.

Therefore, the ENS meteogram for any part of El Hierro is for a sea point and will not capture the likely higher temperatures (or rainfall) on the mountainous island.

The temperature at a given location is adjusted using ENS orography from the ENS temperature (at the ENS altitude of the ENS grid point) to the ENS altitude at the desired location using a standard lapse rate (6.5K/km).  Temperatures can be adjusted higher or lower according to the difference in altitude between grid point and  the location in question.  There are wide variations in orography within the area (the island is quite mountainous) and the representativeness of a grid point can be uncertain.

Note, ENS sea grid point do not always have an altitude of 0m.

• A 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.
• The spectral representation of orography in the IFS, can 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.

For HRES meteograms:

• The ENS grid point in the NE of the island (grid point P) is surrounded by three HRES grid points (A, B, C): 
  • Point A has 90-100% land cover and uses HTESSEL.
  • Point B has 50-70% land cover (with 30-50% water surface) and HTESSEL will supply 50-70% and FLake 30-50% of the flux information. 
  • Point C has <50% land cover (i.e. >50% water surface) and NEMO will supply 100% of the flux information.
• The values at these three nearest points are interpolated to the ENS grid point (P).

The temperature at a given location is adjusted using HRES orography from the interpolated HRES temperature (at the HRES altitude of the ENS grid point) to the HRES altitude at the desired location using a standard lapse rate (6.5K/km).  Temperatures can be adjusted higher or lower according to the difference in altitude between grid point and  the location in question.

Therefore, the HRES meteogram for any part of El Hierro will include the influence of the land while retaining some influence of the sea.   

The temperature at a given location is adjusted for altitude using HRES orography (more detailed than ENS orography) from the 2m temperature using a standard lapse rate (6.5K/km).  There are wide variations in orography within the area (the island is quite mountainous) and the representativeness of a grid point, even though interpolated from three HRES grid point value, can be uncertain.

Accordingly on the meteogram:

• temperatures are relatively uniform in the ENS, but have greater diurnal variation in HRES (there is much greater diurnal radiative heating and cooling variation over land, which is only represented in HRES because only that uses HTESSEL for this location).
• winds are weaker at the HRES land grid point than at the ENS sea grid point (greater surface roughness).

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Fig8.1.5.11: 10-day medium-range meteogram for El Hierro (Canary Isles) from HRES and ENS data time 00UTC 19 May 2017.  The ENS meteogram is for a sea point and does not capture the likely higher temperatures or rainfall on the mountainous island. The HRES altitude (interpolated from HRES orography) is 246m and ENS altitude (interpolated from ENS orography) is 96m, and the temperatures of both have been adjusted from the sea-level 2m temperature using a standard lapse rate (6.5K/km).  The geographic altitude of the location of the meteogram is 881m. and temperatures (and winds) there are likely to be very different from the values suggested by ENS or HRES.   HRES, which uses land grid points, shows a large diurnal variation in temperature and with reduced winds, whilst ENS, which uses only a sea point, shows much more uniform temperatures and stronger winds. 

Final Remarks

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.  

Unfortunately, it's not straightforward to know which three HRES points are used and this can naturally have an impact on the value obtained.  For example, it may be that there are three HRES land grid points or two land and one sea grid points.  Which ones are unlikely to be clear to the user and may not be intuitive.   All that can be said is that it should always be the same three points used so the results should be consistent from day to day.  

Systematic differences between HRES and ENS can occur in connection with strong gradients along coasts, small islands or in mountainous regions.  Any such discrepancy is usually most clearly apparent during the first few days, when the spread is normally small. Influences of any adjacent sea areas may be over- or under-represented by the ENS and/or HRES meteograms.  

Users should use meteogram output with caution - the data should not be taken as definitive but should be assessed and possibly adjusted.   In particular users should:

• critically assess forecast values in the light of experience regarding differences between previous forecast values and actual observed observations.
• 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 representativeness of the meteogram in coastal, island or mountainous regions and take into account consequent differences between HRES and ENS meteograms.
• consider the impact of the land-sea mask value(s) at the grid point on the forecast parameter (temperature, wind, etc) on the meteogram.

Neither ENS nor HRES should be taken at face value without consideration of the ways each derives temperature values and the effects of local influences.

• 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 ENS grid is scanned for the grid points surrounding the location (ENS 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 ENS 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 ENS grid is scanned for the grid points surrounding the location (ENS 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 ENS meteograms for location S than at location V. 

Users should note:

• Inspection of meteograms for nearby offshore locations (e.g. location S) may add useful information for nearby coastal locations (e.g. location V).
• The same ENS grid point is used for both locations N and V (even though location N is inland and location V is coastal).  Differences between the inland location N and the coastal location V will not be apparent.  
• Temperature is adjusted to reflect the differences in height between the altitude of each location and the corresponding ENS orography, using a lapse rate of 6.5K/km.
  

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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Fig8.1.4.1-4: ENS 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.

Example sites are shown on the diagram:

• A lake side location - Montreux (location M).  The ENS grid is scanned for the grid points surrounding the location (ENS 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 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 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. 

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

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Fig8.1.4.1-5: 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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Fig8.1.4.1-6: 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.

Because of the complexities of the orography around the location users should note: 

• 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

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Fig8.1.4.1-7: 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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Fig8.1.4.1-8: 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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Fig8.1.4.1-9: 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.  Local uncertainty in forecast temperatures at altitude can have a large impact of model precipitation especially over mountainous islands and coasts.  

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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Fig8.1.4.1-10: 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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Fig8.1.4.1-11: 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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Fig8.1.4.1-12: 10-day medium-range meteogram for the town of Stromboli 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.  Local effects may be important (e.g. sea breezes).  Conditions at inland high ground will not be reliably indicated.   

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Fig8.1.4.1-13: 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.

Model representation of orography

Modelling the surface orography at an appropriate resolution is crucial to an effective forecast.  However, at some level, there always will be smoothing that misses important detail.   

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Fig8.1.4.1-14: 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.

Generally model orography matches true orography over large parts of the earth.  However, the spectral representation of orography in the IFS can:

• smooth true orography, particularly in rugged mountainous areas where there are large variations in altitude over short distances.  Mountain peaks may be under-represented and narrow valleys may not be represented at all.
• local effects can be under-identified where there are small scale variations in true or model orography, even where relatively low in altitude. 
• 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.  It is quite possible that the model sea surface level is negative!

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. 

In particular users should:

• 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 (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.

  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.  _{Amended/Updated 28/04/20 - Example 1 and Final Remarks}