Fig8.1.5.1: To view meteograms:
The ensemble meteogram provides a probabilistic interpretation of the ENS for specific locations. It displays the time evolution of the distribution of several meteorological parameters from the ensemble by a box and whisker plot. All ENS meteograms have a title section, giving the name (unless overwritten by the user), the true height of the chosen location, and the co-ordinates of the grid point used based on the ENS resolution. When creating a meteogram for a specific location, the four surrounding grid points are considered. If there is at least one land grid point within these four, then the nearest land grid point is used, otherwise the nearest sea grid point is used; ““ENS land point" or "ENS sea point”” is shown in the title section accordingly. Note that whenever the land-sea mask has a value >0.5 (i.e. more than 50% of the grid box is land) then this is a "land point"; all other points are "sea points". Forecast distributions are displayed using a box and whisker plot (see Fig8.1.5.2) which shows the median (short horizontal line), the 25th and 75th percentiles (wide vertical box), 10th and 90th percentiles (narrower boxes) and the minimum and maximum values (vertical lines).
Fig8.1.5.2: The box and whisker plot used in the ECMWF 10- and 15-day ensemble meteograms.
Ensemble meteograms are available for:
Note:
Fig8.1.5.3: 10-day medium-range meteogram for Dublin from HRES and ENS data time 00UTC 12 May 2017. The HRES and ENS CTRL are included in the 10-day ensemble meteogram for reference (Blue lines are HRES, Red lines are ensemble Control). The red numbers above the precipitation panel are the greatest precipitation value reached by any ENS member. ENS extreme values cannot be ignored as the evolution of every ENS member is considered to be equiprobable. In this example an unstable regime moved over the area during 13th and 14th and heavy showers were likely although a passage precisely over Dublin was of course uncertain. Note: the forecast temperatures are at 00UTC, 06UTC, 12UTC, 18UTC each day (15-day meteograms show forecast maximum and minimum temperatures for each day). UTC is used exclusively in the meteograms and maxima or minima will occur according to the longitude (or local time) of the location in question.
Fig8.1.5.4A: 15-day medium-range meteogram for Dublin from ENS data time 00UTC 12 May 2017. The displayed values are for the 24hr period each day, with additionally the distribution of 10m wind direction. Note: the forecast maximum and minimum temperatures are shown for each day (10-day meteograms show forecast temperatures at 00UTC, 06UTC, 12UTC, 18UTC).
Fig8.1.5.4B: As Fig8.1.5.4A with the addition of M-climate data. M-climate data is shown by colours with percentiles similar to the box and whisker scheme. The median wind forecast for 15 May lies well above the M-climate values (above the 75th percentile of the M-climate) with the whisker extending above the 99th percentile of the M-climate.
An alternative display (currently only available within the ecCharts meteogram platform) has a circle divided clockwise into eight arcs, each arc representing 1/8 cloud cover. So, for example, the arc covering 45°-90° represents 2/8 cloud cover. The shading within each arc is proportional to the number of members that forecast this particular degree of cloud cover or more.
Total precipitation in the 10-day ensemble meteogram is the accumulated precipitation (sum of convective and large-scale) over 6hr periods (00-06UTC, 06-12UTC, etc). In the 15-day meteogram it is the accumulated precipitation over 24hr periods (00-24UTC). Note:
On the 10-day meteogram the box-and-whisker plot locations align with the end of the 6 hour period.
Probabilities for intervals longer than the 6hr and 24hr time intervals cannot be deduced from the ensemble meteogram.
Periods of probabilities >0% in every interval can be wrongly interpreted as uninterrupted rain.
Consideration of the median alone can be wrongly interpreted as protracted dry spells.
The HRES is generally better able to generate realistic precipitation amounts and capture extreme rainfall than the ENS because of its higher resolution.
The precipitation shown on the ensemble meteograms cannot be directly intercompared as the rainfall range (y-axis) varies from one location to the next and from one forecast to the next. The rainfall range is chosen separately for each ensemble meteogram so that 100% of the predicted values are covered for the 15-day ensemble meteograms, and at least 90% of the predicted values are covered for the 10-day ensemble meteograms (if the top of the distribution is beyond the scale maximum the largest 6-hourly total is shown at the top as red numbers).
The HRES meteorological fields are interpolated using the four HRES grid points nearest to the location of the selected ENS grid point.
Because HRES values are interpolated and ENS values are not, the tendency for HRES to sometimes deliver higher wind speeds or larger rainfall totals than ENS, as discussed above, will to an extent be mitigated against on meteogram displays because of the interpolation. In turn, the degree to which this happens will depend on how near the closest HRES grid point is, spatially, to the chosen ENS grid point.
At longer lead times, the ensemble mean and the ensemble median will tend to gravitate asymptotically towards the M-climate. This is most clearly seen when the first ten days of the forecast are anomalous (e.g. after an initial spell of cold and rainy weather, the ensemble tends to indicate a return to milder and drier conditions at longer forecast ranges). This follows logically from the fact that at an infinite range, when predictive skill is completely lost, a climatological value constitutes the optimal forecast.
It is necessary to assess critically the parameters shown on meteograms. Verification of previous forecasts, particularly recent forecasts within a similar meteorological regime, may allow an insight into whether the latest HRES or ENS is likely to be the better forecast.
Occasionally ENS produces forecasts that diverge into a bi-modal (or possibly multi-modal) distribution of forecast results in two (or more) distinct patterns (e.g. if there is model uncertainty regarding positioning of a cold front, a number of ENS members for a given location may show warm midday temperatures while others show much cooler temperatures). Bi-modal distribution of forecast results will not be shown by meteograms (as box-and whisker plots cannot do this - the effect would be just to stretch out the boxes). However bimodal distributions can be apparent on plume diagrams.
If a majority of ENS members forecast temperatures below 0°C and, at the same time, a large number of members forecast substantial precipitation, there is no way to determine the likelihood of snowfall from the standard meteogram diagram alone - the precipitating members might all have temperatures well above 0°C (in this instance users are encouraged to use the ecCharts meteogram product, which shows ENS probabilities of precipitation types by category). Probability of combined events can only be calculated from the original ENS data. Several charts of combined probabilities are available on ecCharts.
The relative forecast spread may vary considerably between one parameter and another in the same forecast step. For example
The ensemble can only predict severe weather events of the kind that the resolution of the ENS can resolve. The HRES has a small advantage over the ENS with respect to rainfall rate or wind speed. The latest HRES should be considered together with the ENS as part of the ensemble. If the HRES deviates systematically from the ENS, forecasters have to use their experience or local knowledge to decide which information is the more realistic or representative and, if necessary, adjust one to the other. In such circumstances, it may be appropriate to give more weight to HRES.
Fig8.1.5.5: Ensemble Meteogram for Kontiolahti in eastern Finland, 12UTC 22 April 2011. The systematic difference between the HRES (blue line) and the ensemble Control forecast (red line) is around 5°C. Kontiolahti lies close to a small lake better captured in HRES than in the ENS.
When creating a meteogram for a specific location, the land-sea mask at the four surrounding ENS grid points is considered. If there is at least one land grid point within these four, then the nearest land point will be chosen and the meteogram title section shows "ENS Land Point" together with its location and ENS altitude. If only sea points are available then the nearest sea grid point will be chosen and the meteogram title section shows "ENS Sea Point" together with its location and ENS altitude of 0m. Data at the selected ENS point will have been calculated using HTESSEL and FLake according to the proportions of land and sea cover within the surrounding grid point box (see examples below, or the Land-Sea Mask section for details).
HRES values (meteorological fields and orography) are interpolated onto the ENS grid from the four HRES grid points surrounding the location of the selected ENS grid point and are added to the meteogram. Data at the selected HRES grid points surrounding the selected ENS grid point will have been calculated using HTESSEL and FLake according to the land and sea cover within their surrounding grid point boxes.
Therefore 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. Some influences of the adjacent sea areas may be over- or under-represented by the ENS and/or HRES meteograms. Users should consider the impact of the grid point(s) relative to the land-sea mask upon the indication of the forecast parameter on the meteogram (temperature, wind, etc). Users should critically assess differences in meteograms for coastal, island or mountainous regions.
For forecast temperature data, all locations within each grid box are considered to have the same values as 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 sea (shallow coastal water where FLake will be used).
Fig8.1.5.6A: ENS grid points over part of southern England. Within each box all locations are considered to have the same values as 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 sea (shallow coastal water where FLake will be used). Any location on the island within a green box will be represented by the single grid point near the centre where HTESSEL will supply 100% of the flux information. Locations on the island in the blue boxes will be represented by the corresponding sea points and FLake will provide 100% of the flux information. In the turquoise area the grid points are considered to be about 60% sea. Therefore HTESSEL will provide 40% and FLake 60% of the fluxes , including the city of Portsmouth (marked P). Ventnor is marked V, and Newport is marked N on the chart. Grid boxes are coloured according to the "fraction of land cover" scale on the right.
Fig8.1.5.6B: HRES grid points over part of southern England. 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 the city of Portsmouth, marked P). Grid boxes are coloured according to the "fraction of land cover" scale on the right.
Any location on the island within a green box will be represented by the single grid point near the centre where HTESSEL will supply 100% of the flux information. Locations on the island in the blue boxes will be represented by the corresponding sea points and FLake will provide 100% of the flux information. In the turquoise area the grid points are considered to be about 60% sea. Therefore HTESSEL will provide 40% and FLake 60% of the fluxes.
Both towns therefore have the same ENS and HRES data for presentation in the meteogram and differences between the inland Newport and the coastal Ventnor will not be apparent. However, temperature is adjusted to reflect the differences in height beteen the altitude of each town and the corresponding HRES and ENS orography. Despite being on the coast, Ventnor is at greater altitude than Newport and will consequently appear cooler.
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 temperatures (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 four 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 is difficult to disentangle causes but users need to be aware of possible reasons in each case. The station is on the coast and the discrepancy may possibly be due to temperature adjustment differences because of the adjustment of temperature to the HRES altitude of Ventnor which is higher than that of the ENS point, or possibly differeces in HTESSEL and FLake tiling at the HRES grid points and stronger influence of the HRES land grid point values.
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 towards the northeast of the right hand box (coloureded 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 detail is captured by the grid, resulting in a more realistic representation of the influences of land and sea within each grid box. Grid boxes are coloured according to the "fraction of land cover" scale on the right.
In this case the influence of the lake has a significant impact on the energy fluxes in the HRES data. 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 assume knowledge of the structure of the lower atmosphere.
Accordingly on the meteogram, because of the complexities of the situation:
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 four 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 coast 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 and assumptions of the structure of the lower atmosphere, 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.
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: HRES grid points around the Canary Islands. Note there are many more points wholly on land. The island of El Hierro has 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.
El Hierro (marked EH in Fig8.1.5.10) is an island in the Canary Island archipeligo. There are no ENS land grid points on El Hierro, but four ENS sea grid points surround the location of the island. Therefore the nearest ENS grid point to a location on El Hierro (which will be a sea grid point) is selected for the data to be shown on the meteogram.
The ENS grid point just selected is surrounded by four HRES grid points that are land points, each with a different proportion of sea cover and corresponding proportion of FLake and HTESSEL usage in determining surface temperature and humidity. The values at these surrounding four points (more in the shape of a rhombus) are interpolated to the location of the ENS grid point just selected.
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 is ly 96m, and the temperatures of both have been "reduced" to the height of the chosen location of 881m.
Accordingly on the meteogram:
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 is 96m (the ENS sea grid point selected appears to be very near the coast), but HRES temperatures have been "reduced" to the height of the chosen location of 881m. Nevertheless, there is a large diurnal variation in temperature with the HRES which uses land grid points, whilst temperatures remain much more uniform for the ENS which is a sea grid point. Similarly winds are stronger over the sea and reduced over land.