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Evaluation of grid point data
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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:
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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 FLake) 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.
Fig8.1.54.61-1: 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 HTESSEL and 20%30% by FLake. At grid point Y the fluxes of heat, moisture and momentum will be determined by 100% by FLake, even though the grid point lies over land.
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Example of grid point data on meteograms
Fig8.1.5.84-2: 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.
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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.
Fig8.1.54.71-3: 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.
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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.
Fig8.1.54.91-4: 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.
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The difference in geographical altitude reflects the hilly nature of land and towns near the lake.
Fig8.1.4.1-5.10: 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.
Fig8.1.54.111-6: 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.
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Example3: A mountainous oceanic island.
Canary Islands
Fig8.1.54.121-7: 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.
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There are wide variations in orography within the islands (the islands are quite mountainous) and the representativeness of a grid point can be uncertain.
Fig8.1.54.131-8: 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.
Fig8.1.54.141-9: 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.
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There are wide variations in orography within the islands (the islands are quite mountainous) and the representativeness of a grid point can be uncertain. Local uncertainty in forecast temperatures at altitude can have a large impact of model precipitation especially over mountainous islands and coasts.
Example4: Isolated Example4: Isolated small islands.
Isole Eolie. A set of small volcanic islands near southwest Italy. The islands are roughly 5km x 5km or smaller.
Fig8.1.54.151-10: 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.
Fig8.1.54.161-11: 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.
Fig8.1.54.171-12: 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.
Fig8.1.54.181-13: 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.
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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.
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
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