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http://intra.ecmwf.int/daily/d/dreport/2014/06/10/sc/

http://intra.ecmwf.int/daily/d/dreport/2014/06/11/sc/


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1. Impact

On 9 June severe convection affected western Europe. In Germany 6 people were killed, mainly by falling trees. Wind gusts up  to 42 m/s where reported from Duesseldorf airport.

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3. Predictability

  

3.1 Data assimilation

The plot below shows the time series of surface pressure for a set of observations inside a domain in west Europe. The figure includes time series for the fg and an departures bias and rms. From the 9th of June one can seen the impact of the strong convective activity on the model and the effort of the assimilation to bring closer the first guess to the observations.

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3.2 HRES


The figures below show the 24-hour precipitation (06-06UTC). The first plot shows the observations and the following HRES forecasts valid for the same period. In western Germany and Belgium several stations reported more than 30 mm. In the forecasts the band of convection was shifted somewhat to the west. For central France it seems like the convection was overestimated.

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The  figure above shows animation of RGB product from EUMETSAT, the max CAPE for +(6-24)h period (top-right) and maximum CAPE blended with CIN (CAPE is removed where CIN >= 200, bottom-left) based on 09@00 run for the HRES. One can see a good agreement between the high values of CAPE and the regions where the convection took place in particular over France and Benelux region. The convection triggered ahead of a cold front crossing the Iberia Peninsula seems to be in contradiction with the relative low values of CAPE forecast. It is possible that a dynamical lifting process helped to trigger the convection in the region.

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The lpot above show the evolution of the individual convective cells in the forecast (run 09@00) and compared with the satellite images. The top row is the satellite images every 3hrs between 15 UTC (9th) and 00 UTC (10th) and the lower panels are the simulated satellite images from HRES. The stars identify the individual cells. The red star in the northern Germany seen in satellite picture was not predicted by the model while the blue star was forecast by the HRES. The trajectory forecast of this cell is very close to the observed one but slightly displaced to west (consistent with the precipitation pattern discussed above). The model succeeded to predict the size and the life cycle of the blue star. The cells developing in France late in the evening are captured also by the high resolution model (amber star).

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The plot abpve compares the 24h acc. convective precipitation forecast initialised on 09@00 and the reported precipitation. The lines are an attempt to identify the trajectory of the convective cell (model and observed) which had a major weather impact in Germany. From the plot is clear how difficult it is to represent closely each individual cell in terms of its strength and location.

3.3 ENS

70% ENS percentile total PPN in 6h (top-left). Vis image at 1500 UTC and 3hrly PPN from HRES based on the latest run (bottom).

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The plots bleow shows the value of the maximum member for 18-hour precipiation precipitation (06-00UTC) for EC ENS (32km), COSMO-LEPS and COSMO-DE-EPS. All forecasts are initialised 9 June 00UTC. Note that the number of members in each ensemble affects this diagnostics (50 for EC, 16 for  COSMO-LEPS and 20 for COSMO-DE-EPS).

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titleEnsemble max precip

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