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This product shows the 10-day evolution of three parameters: 850hPa temperature, 6h precipitation (up to the time shown) and 500hPa geopotential height for user-defined locations during the 10 days of the forecast.   Lines show the evolution in each IFS run (dotted for the ENS, solid for the CTRL, and dashed for HRES).  Shading (for temperature and height only) denotes the probability that the value of the variable will fall in a particular range (see panel titles) at a given time. 


 Fig8.1.7.1:  A plume diagram for Ventnor based on ENS run at 00UTC 4 March 2017.  Shading denotes the probability that the value of the variable will fall in a particular range (see top panel legend) at a given time.  For 850hPa temperature, for example, the lightest green shading denotes that the probability is between 0.5% and 10% that the observed value will lie within a range of +/-0.5C of the y-axis value.  Probability of 500hPa heights is shown similarly using a blue shading.  The ensemble forecast indicates uncertainty between days 5 and 7 with ens members showing a fall in 850hPa and 500hPa heights at differing times. The HRES and the Control both show a later change in these values.

In contrast to Meteograms, plumes can display bi-modal characteristics.  Large-scale bi-modality shows uncertainty in the ENS evolution (e.g. part of the ensemble may favour a transition to blocking; the rest may  favour a zonal regime).  Local bi-modality reflects smaller scale location or timing uncertainties (e.g. a front or minor low is forecast by different members either upstream or downstream of a particular location, resulting in quite different local weather forecasts).  It is important to distinguish between these kinds of bi-modality.  Large-scale bi-modaility might for example be denoted by a bimodal 500mb height plume (perhaps accompanied by a bimodal 850mb temperature distribition).  Local front-related bimodality might be indicated by having at the same time a bimodal 850mb temperature plume and a 500mb height plume that is not bimodal.  Clustering products can also help in the detection of large-scale bimodality.

 Fig8.1.7.2 Plume for 52°N 33°E (far NE Ukraine) data time 12UTC 23 May 2017.  An example of trimodal characteristics within a plume.  On Sunday 28 May the forecast 850hPa temperature of ENS members splits into three main branches which persist for about two days: one rising to 10°C, the other with slightly more ENS members falling to -3°C, and another, which includes HRES and CTRL, lying between.  The forecast 500hPa heights show a similar split with some showing a temporary significant dip in contour heights while others show minor rise.  Note also that on 31 May the HRES and CTRL show greater 500hPa geopotential height than any member of the ENS.


Fig8.1.7.3: The ENS mean and spread for 500hPa heights from the same forecast verifying at 00UTC 28 May.  High standard deviation on right hand chart near 25ºE due to uncertainty regarding the timing and positioning of the major upper trough near 30°E.  The Normalized standard deviation on left hand chart shows greater spread (and hence uncertainty) near 25ºE  than recent ENS results.


Fig8.1.7.4: Two plumes from ENS for Southampton, Data Time 00UTC 18 January 2017 (left) and Data Time 00UTC 19 January 2017 (right).  Upper plumes show 850hPa temperature variations among ENS, lower plumes show 500hPa geopotential height variations among ENS for each of the two forecasts.  The ensemble mean (not shown) lies in the spread.  Any individual ensemble member will lie anywhere within the spread, but the unperturbed HRES and CTRL are not used in the calculation.  The HRES and CTRL usually remain within the spread (eg plumes on the right), but on a few occasions (theoretically around 4% at longer lead-times) they extend outside of the plume (eg plumes on the left).  When this happens it is imperative to study the evolution of the atmosphere in some detail to decide the most likely evolution - and definitely not to assume that the unperturbed CTRL results are necessarily the best, nor HRES because of finer resolution.   In the case shown, HRES and CTRL moved a small cold and potentially snowy vortex northwards across Southampton while the perturbed ENS solutions moved it past the city either to the east or west.  The subsequent ENS forecast showed much less uncertainty in temperature and depth of the vortex and it is questionable if the ENS developed it at all.  Even so, because of the evidence from the earlier forecast, it would be unwise to dismiss the vortex altogether as it cannot be assumed the later ENS forecast is necessarily the best even though it has benefitted from later observations.


Fig8.1.7.5: An example of uncertainty.  850hPa temperature plumes for Reading, England from ENS runs of 00UTC 31 Aug, 12UTC 1 Sep, 00UTC 3 Sep 2018 in association with uncertain forecasting of a cut-off low moving southeast across Britain.  Large and even multi-modal spread in the plume is evident on the forecast based on 00UTC 31 Jan, but the spread becomes much more narrow by the forecast from 00UTC 3 Sep.  Cut-offs tend to be difficult for models to forecast and consequently users should consider the consistency of the model evolution. The blue lines are for cross-referencing; they denote the same values on each panel.

Fig8.1.7.6A(left): Plume data time 12UTC 27 July 2017 for Reading, UK. In the previous ENS run, the forecast for Thursday 3 August showed much more consistency between HRES and ENS CTRL and members. The differences were due to the proximity of a depression passing to the northwest of England - on the later run (Fig8.1.7.6B) the CTRL brings the feature closer to Reading than previously while HRES persists with holding the depression a little further west and maintains a strong pre-frontal surge of high temperatures at 850hPa., as did ENS in the previous run (Fig8.   When presented with changes such as these, the forecaster should investigate the differing evolution of the weather systems shown by the HRES and ENS members.  Consider using weighting of the HRES and ENS appropriate to the forecast lead-time.

Fig8.1.7.6B(right): Meteogram data time 00UTC 28 July 2017 for Reading, UK.  The forecast for Thursday 3 August is an example of the HRES (dashed line) differing from the ENS CTRL (solid line) and the majority of ENS members, particularly with respect to 850hPa temperature forecasts.  However, HRES forecast 500hPa geopotential height is closer to the majority of the ENS members while the ENS CTRL is nearer to a group of ENS members showing lower values.


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