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Selection of grid point data to use on Meteograms

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 (where HTESSEL will be used) and lake/coastal seas (where FLake will be used), or for sea grid point (where NEMO will be used).

For forecast HRES temperature data, the values are interpolated from those forecast at the three HRES points surrounding the ENS point identified above.


Stage 1: 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 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 point).

For sea locations:

  • The nearest ENS sea grid point is selected from among the four ENS points surrounding the selected location (even though a grid point over land may be nearer).  NEMO will supply 100% of the flux information.
     

Flux information is governed by the "fraction of land cover" assigned to the ENS grid point (see Fig8.1.5.6A) .   Thus ENS grid points in rectangles:

  • coloured green are land points and HTESSEL will supply 90-100% of the flux information.
  • coloured light green are land points (although with some water surface) and HTESSEL will supply 70-90% and Flake 10-30% of the flux information.
  • coloured turquoise are land points (although with rather more water surface) and HTESSEL will supply 50-70% and Flake 30-50% of the flux information.
  • coloured blue are sea points and FLake will supply 100% of the flux information.


Stage 2: Selection of HRES grid point values relevant to a selected location:

For all land and sea locations:

  • The nearest ENS grid point to the location has been selected (as described in stage 1 above).  This is considered to be representative of the location in question.
  • The HRES grid is scanned to identify the three HRES grid points surrounding this ENS grid point..
  • These HRES grid points are used to interpolate HRES data to the ENS grid point as described in Section 3.3 Interpolation Techniques.

Flux information used by HRES is governed by the "fraction of land cover" assigned to the HRES grid points (see Fig8.1.5.6B).   Thus HRES grid points in rectangles that are coloured:

  • green are land points and HTESSEL will supply 90-100% of the flux information.
  • dark green are land points (but with 10-30% water surface) and HTESSEL will supply 70-90% and FLake 10-30% of the flux information.
  • turquoise are land points (but with 30-50% water surface) and HTESSEL will supply 50-70% and FLake 30-50% of the flux information.
  • blue are sea points (i.e. >50% water surface) and NEMO will supply 100% of the flux information.


Users should note, for flux information: 

  • Coastal waters (less than 50% water cover in a grid box) are treated as lakes (using Flake) rather than as oceans (using NEMO.  

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


It is for the user to assess critically the representativeness of the Meteogram displayed.  


The process of selecting which gridpoints from HRES and 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.


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), Newport (N), 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:

  • An inland location - Newport (location N). The ENS grid is scanned for the grid points surrounding the location (ENS grid points ABDE) and the nearest land point is chosen (Point E).
  • A coastal location - Ventnor (location V).  The ENS grid is scanned for the grid points surrounding the location (ENS grid points EFGH) and the nearest land point is chosen (Point E). 
  • An inland location - Freshwater (location F).  The ENS grid is scanned for the grid points surrounding the location (ENS grid points ABDE) and the nearest land point is chosen (Point A).
  • A coastal location - offshore of Ventnor (location O).  The ENS grid is scanned for the grid points surrounding the location (ENS grid points EFGH) and the nearest sea point is chosen (Point G).
  • A city location - Portsmouth (location P.   The ENS grid is scanned for the grid points surrounding the location (ENS grid points BCEF) and the nearest land point is chosen (Point B).

Users should note:

  • The nearest ENS grid point for location O is over sea and uses 100% NEMO.
  • The same ENS grid point is used for both locations N and V (even though N is inland and V is coastal) and HTESSEL will supply 90-100% of the flux information. This ENS grid point supplies the temperature (and other) data for the ENS meteograms for both locations N and V.
  • Temperature data is adjusted for altitude from the altitude of the ENS grid point to the altitude specified for the selected location, using a lapse rate of 6.5K/km.
  • No adjustment is made for the influence of the sea at the coastal location of location V and the effect of the sea may not be evident on ENS meteograms.  Inspection of meteograms for nearby offshore locations (e.g. location O) may add useful information.
  • The nearest ENS grid point to location P (point B) is within a turquoise rectangle and HTESSEL will supply 50-70% and FLake 30-50% of the flux information.  The influence of the sea will be more evident on ENS meteograms than at location V. 
  • The nearest ENS land grid point to location F is remote from that location (not even on the island) and land characteristics governing fluxes there may be very different from those on the island.


Fig8.1.5.6B: HRES grid points over part of southern England (black dots).  More detail is represented than by the ENS grid, but note two points to the northeast of the island are considered as sea points (one of which is by the city of Portsmouth, marked P).  Grid boxes are coloured according to the "fraction of land cover" scale on the right.  Small open circles denote the locations of certain ENS gridpoints that are discussed in the text.

In the case shown in the diagram:  

  • An inland location - Newport (location N).  HRES values are interpolated from the three HRES grid points (XYZ) surrounding the ENS grid point. Two of these HRES grid points are over land (both using 90-100% HTESSEL) and one lies over sea (using 100% FLake).  
  • A coastal location - Ventnor (location V).  Same as Newport.
  • An inland location - Freshwater (location F).  The ENS grid point lies exactly on the line between two points (JK) and HRES values are linearly interpolated.  Both HRES grid points are over land (both using 90-100% HTESSEL).
  • A coastal location - offshore of Ventnor (location O).  HRES values are interpolated from the three HRES grid points (RST) surrounding the ENS grid point.  All three HRES grid points are over sea (all using 100% FLake).
  • A city location - Portsmouth (location P).   HRES values are interpolated from the three HRES grid points (UVW) surrounding the ENS grid point.  There is one HRES grid point over land (using 70-90% HTESSEL and 10-30% FLake) and two over sea (both using 100% FLake).

Users should note for HRES:

  • The same ENS grid point is used for both locations N and V (even though location N is inland and location V is coastal). The interpolated HRES data supplies temperature (and other) data for the ENS meteograms for both locations N and V.  Differences between the inland location N and the coastal location V will not be apparent.  HRES values will normally differ from ENS values as a result of the differing use of energy flux procedures (HTESSEL, FLake, NEMO) at the surrounding HRES grid points.
  • Temperature is adjusted to reflect the differences in height between the altitude of each location and the corresponding HRES and ENS orography.   Despite being on the coast, location V is at greater altitude than location N and will consequently appear cooler.
  • No adjustment is made for the influence of the sea at the coastal location of location V and so the effect of the sea may not be evident on HRES meteograms.  However, one of the HRES grid points is a sea point (Z) and will give some indication of the influence of the sea.   Inspection of meteograms for nearby offshore locations (e.g. location O) may add useful information.
  • The ENS grid point (A) nearest to location F is remote from that location (not even on the island) and land characteristics governing fluxes may be very different.
  • In many cases locations are entirely outside the area defined by the three HRES grid points supplying data. This may seem undesirable, but the methodology adopted does deliver some consistency between the ENS and HRES usage, and indeed alternative approaches are not computationally viable for ECMWF at the current time.

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 Z is helpful in the derivation of temperatures by HRES .  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 Z may not be relevant and ENS temperatures may be better.   It is for the user to 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.


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.

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.


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.

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

 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.

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.


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).

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.


Amended/Updated 28/04/20 - Example 1 and Final Remarks

 


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