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 of the medium range ensemble (dotted for each individual ensemble member, solid for CONTROL-10). 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 Paris based on medium range ensemble run DT 12UTC 21 June 2023. 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 ensemble members showing a fall in 850hPa and 500hPa heights at differing times.
In contrast to Meteograms, plumes can display bi-modal characteristics. Large-scale bi-modality shows uncertainty in the ensemble 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 bi-modal 850mb temperature plume and a 500mb height plume that is not bi-modal. Clustering products can also help in the detection of large-scale bi-modality.
Fig8.1.7.2 Plume for 52°N 33°E (far NE Ukraine) DT 12UTC 23 May 2017. An example of trimodal characteristics within a plume. On Sunday 28 May the forecast 850hPa temperature of ensemble members splits into three main branches which persist for about two days: one rising to 10°C, the other with slightly more ensemble members falling to -3°C, and another, which includes CONTROL-10, 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, CONTROL-10 shows greater 500hPa geopotential height than any member of the ensemble.
Fig8.1.7.3: The ensemble 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 ensemble results.
Fig8.1.7.4: Two plumes from ensemble for Southampton, Data Time 00UTC 18 January 2017 (left) and Data Time 00UTC 19 January 2017 (right). Upper plumes show 850hPa temperature variations among ensemble, lower plumes show 500hPa geopotential height variations among ensemble 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 CONTROL-10 are not used in the calculation. CONTROL-10 usually remains 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 CONTROL-10 results are necessarily the best. In the case shown, CONTROL-10 moved a small cold and potentially snowy vortex northwards across Southampton while the perturbed ensemble solutions moved it past the city either to the east or west. The subsequent ensemble forecast showed much less uncertainty in temperature and depth of the vortex and it is questionable if the ensemble 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 ensemble 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 ensemble 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.
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