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A comprehensive set of post-processed ENS ensemble products use a feature-based approach to represent objectively the location and behaviour of near-surface, synoptic-scale features typically associated with adverse weather (eg fronts, frontal waves, cyclonic features).  Co-location masking, a feature-type hierarchy and a minimum separation threshold, are all used together to help keep all cyclonic features 300km or more apart.  Mean sea level pressure, estimated from 1000hPa geopotential height and temperature, is shown as a reference point on many charts.  A tracking algorithm is used to follow the cyclonic features as they evolve in each ensemble member.  As a severe weather event approaches, the products can:

  • indicate an increasing risk of a major storm in the area of interest,
  • highlight the track that the storm is likely to take,
  • suggest the degree of confidence that can be placed in that track (see Fig8.1.9.8).

 

Fig8.1.9.1: To view extratropical cyclone forecasts:

  1. On charts page, click on extratropical cyclones.
  2. On extratropical cyclones page select the extratropical cyclones diagram.
  3. Select nominal data time of the forecast of interest.
  4. Select feature of interest - in this case a barotropic low (black).
  5. Display of product (in this case ENS ensemble member tracks, 1km wind speeds, 300hPa wind speeds, central pressures and vorticity).
  6. Step to next or previous nominal data time of interest.
  7. Select alternative presentation of forecast features (e.g.fronts, dalmation charts etc) from drop-down menu.


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Example Charts

The extratropical cyclone diagrams provide a comprehensive display of the variation between the forecasts of each member of the ENS ensemble regarding positions of fronts, depth of depressions, and strength of winds at 1km altitude. In the examples the features near Brittany relate to an extreme windstorm which in terms of European losses was the major windstorm of thr 2016-17 winter.  For interpretation of the charts see a guide to using cyclone database products.


Fig8.1.9.3: An example of a chart showing positions of fronts diagnosed from ENS ensemble members and HRES (see legend below chart for details) illustrating the variation in positions. Nominal data time of forecast: 00UTC 03  DT 00UTC 03 March 2017, T+84 VT 12UTC 06 March 2017.


Fig8.1.9.4:An example of a "Dalmation Plot" showing the centres of cyclonic features coloured to show an analysis of the cyclone class as derived from ENS ensemble members and HRES (see legend below chart for details) showing the variation in forecast positions.
Nominal data time of forecast: 00UTC  
DT 00UTC 03 March 2017.  , T+84 VT 12UTC 06 March 2017.  Note that not all the spots denote genuine low pressure centres; it is only the barotropic lows (black spots) that are guaranteed to be.


Fig8.1.9.5:An example of a "Dalmation Plot" showing the centres of cyclonic features, coloured to show an analysis of the forecast maximum wind strength, at 1km altitude, within 300km of each centre derived from ENS ensemble members and HRES (see legend below chart for details).  Chart highlights show the variation in positions and intensity. Nominal data time of forecast: 00UTC 03  DT 00UTC 03 March 2017, T+84 VT 12UTC 06 March 2017.  Note several members suggest a maximum wind of 65-85kn in the vicinity of northwest France.

Fig8.1.9.6: An example of a chart showing the percentage of ENS ensemble members predicting a cyclonic feature point will track within 300km in a 24-hour period T+72 to T+96 (i.e. 00UTC 06 March to 00UTC 07 March 2017).   Nominal data time of forecast: 00UTC  DT 00UTC 03 March 2017, T+84 VT 12UTC 06 March 2017.  Only cyclonic features with a maximum wind speed exceeding 60kn at 1km altitude within 300km of the centre at some point in the 24h period are included.  A probability greater than 60% (darker orange) is shown over the western English Channel and NW France.  For a cyclonic feature moving west-to-east in this part of the world the strongest winds will ordinarily be found to the south of the low track.  This needs to be taken into account - indeed it is important for the user to not misinterpret the shading on these strike probability charts as being like a simple wind gust probability chart.


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Fig8.1.9.7: An example of the clickable T+0 ENS ensemble CNTL chart nominal data time . DT 12UTC 04 March 2017, T+0 VT 12UTC 04 March 2017.  Automatically diagnosed positions of fronts are indicated by lines and important synoptic scale cyclonic features are shown by filled circles.  Black: Barotropic low, Orange: Frontal Wave, Green: Diminutive frontal wave.

Clicking on the frontal wave depression (orange) at the point shown by the red arrow in Fig8.1.9.7 will display the subsequent forecast movement and development as shown in Fig8.1.9.8 (albeit that plumes for some parameters have been omitted on this figure).


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Fig8.1.9.8A(top): Forecast tracks of frontal wave (arrowed in Fig8.1.9.7) from ENS ensemble members.  

Fig8.1.9.8B(left): Forecast central pressure (hPa) of each depression developing from the frontal wave as identified by the ENS ensemble members.

Fig8.1.9.8C(right): Forecast wind strengths (kn) at 1km altitude within 300km of each depression developing from the frontal wave as identified by the ENS ensemble members.

The ENS CNTL and HRES are shown respectively ensemble CNTL is shown by thin and thick green lines (though in some locations they overlap) green lines.  Most ENS ensemble members forecast the track of the selected cyclonic feature to curve towards Britain before moving SE into northwest France.   Almost all ENS ensemble members deepen the low, some to below 995hPa with winds at 1km altitude reaching more than 60kn and a few greater than 70kn.  The threat of severe weather is clearly shown but it is necessary to inspect the ENS ensemble members, meteograms, EFI charts etc. to identify the associated risk.   Nominal data time of forecast:  DT 12UTC 04 March 2017.

 Fig8.1.9.9A(top): Ensemble Extreme Forecast Index (EFI) and Shift of Tails (SOT) charts for mean daily 10m wind speed (left) and M-climate for this (right) at 99th quantile (typically 1 in 100 occasions in the ENS ensemble realises more than the values shown).   Nominal data time of forecast: 00UTC  DT 00UTC 03 March 2017, T+48 to 72. 

 Fig8.1.9.9B(bottom): Ensemble Extreme Forecast Index (EFI) and Shift of Tails (SOT) charts for maximum 10m wind gusts (left) and M-climate for this (right) at 99th quantile (typically 1 in 100 occasions in the ENS ensemble realises more than the values shown).  Nominal data time of forecast: 00UTC  DT 00UTC 03 March 2017, T+48 to 72. 

 EFI exceeding 0.7 in much of France suggests unusual winds, and 0.8 in some places suggesting very unusual winds for those locations.  Meanwhile areas of non-zero SOT suggest a genuinely extreme event is possible. 

Considerations when dealing with small cyclones

The spatial resolution utilised in generating the extra-tropical cyclone charts (~50km) is rather larger than the resolution intrinsic to HRES or ENSensemble, and is primarily applicable to monitoring mid-latitude depressions and their associated features. This is partly by design, in that we are trying to capture "synoptic scale features", and not every minor nuance in the model fields. Mid latitude depressions typically have a length scale of order 1000km and the program can extrapolate realistic central pressures from the surface pressure pattern.  As the feature is followed through the forecast period, feature specific plumes of central pressure, upper and lower altitude winds, and vorticity are plotted.  These are presented in the subsidiary diagrams.  However, resolution of the input data means a reduced capacity to correctly represent certain aspects (such as depth or maximum 1km winds) of small, deep vigorous cyclones, where the length scale is < ~200 km, say.  Nevertheless, the forecast positions of small vigorous centres are normally well captured.



Fig8.1.9.10: Extra-tropical cyclone chart for a small depression in the Mediterranean Sea.  Forecast data time DT 12UTC 27 Sep 2018.  The initial position of the low centre near Benghazi is identified by almost all ENS ensemble members, but just one or two and HRES locate the centre a little further east (identification of these may be "corrupted" because part of the depression in model fields was over the high ground in northeast Libya).  The forecasts generally fall into two groups: the larger group tracks the cyclone initially northwest and then more persistently northeast, the other consisting of HRES (thick green line) and a few  ENS ensemble members track a depression towards Cyprus.  The plumes show the very different evolution of these two groups. Closer inspection of HRES fields shows a particularly complex surface pressure evolution, whereby the main feature actually develops west of the tracked low, thereby showing more consistency with the main plume. This illustrates an additional difficulty associated with developing small-scale cyclones - as far as this HRES run was concerned the main feature wasn't actually present at analysis time.

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Fig8.1.9.11: Extra-tropical cyclone chart considering a small depression in the Meditteranean Mediterranean Sea with HRES MSLP charts. Forecast data time . DT 00UTC 28 Sep 2018.  The initial position of the low centre is identified by ENS ensemble members and HRES and the subsequent forecast tracks and plumes are fairly similar. The central pressure of the depression is analysed by HRES as 991hPa and by T+48 HRES and ENS forecasts show the centre to have a similar depth.  However, the plume indicates pressures no lower than 1000hPa which is a consequence of the lower resolution. While confidence  The higher resolution of the medium range ensemble allows a fairly good representation of small vigorous features.  Confidence may be placed in extra-tropical cyclone charts associated with larger mid-latitude depressions , users should not take as precise the values given for small vigorous features although and the trend of vorticity and other parameters will give a fair indication of trendsdevelopment or decay..

Considerations regarding Identification of Fronts

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Users should inspect other available IFS output to understand the structure of the atmosphere as forecast, and in particular should not rely on one model solution alone (e.g. HRES or ENS ensemble control) but instead assess uncertainty using the ensemble of frontal positions ("spaghetti fronts") and other products (e.g. meteograms). Users can then the interpret (and potentially adjust) IFS objective fronts in light of their knowledge and experience of the local geography and its influence.

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