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titleIsobaric maps
  • Geopotential at 500hPa + MSLP :                       primary circulation, Figure 1 from Pantillon et al.
  • MSLP + 10m winds :                                           interesting for Nadine's tracking and primary circulation
  • MSLP + relative humidity at 700hPa + vorticity at 850hPa  : low level signature of Nadine and disturbance associated with the cutoff low, with mid-level humidity of the systems.
  • Geopotential + temperature at 500hPa :            large scale patterns, mid-troposphere position of warm Nadine and the cold Atlantic cutoff
  • Geopotential + temperature at 850hPa :            lower level conditions, detection of fronts
  • 320K potential vorticity (PV) + MSLP,
  • 500hPa relative vorticity (see Fig. 14 in Pantillon) :     upper level conditions, upper level jet and the cutoff signature in PV, interaction between Nadine and the cut-off low.
  • Winds at 850hPa + vertical velocity at 700hPa (+MSLP) : focus on moist and warm air in the lower levels and associated vertical motion. Should not be a strong horizontal temperature gradient around Nadine, the winds should be stronger for Nadine than for the cutoff.
  • 10m winds + total precipitation (+MSLP) : compare with Pantillon Fig.2., impact on rainfall over France.

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Exercise 3 : The operational ensemble forecasts

Recap

  • ECMWF operational ensemble forecasts treat uncertainty in both the initial data and the model.
  • Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA) methods. Singular Vectors are a way of representing the fastest growing modes in the initial state.
  • Model uncertainty: sampled by use of stochastic parametrizations. In IFS this means Stochastically Perturbed Physical Tendencies the 'stochastically perturbed physical tendencies' (SPPT) and the 'spectral backscatter scheme' (SKEB)
  • Ensemble mean : the average of all the ensemble members. Where the spread is high, small scale features can be smoothed out in the ensemble mean.
  • Ensemble spread : the standard deviation of the ensemble members and , represents how different the members are from the ensemble mean.

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In this case study, there are two operational ensemble datasets and additional datasets created with the OpenIFS model where the initial and model uncertainty are switched off. The OpenIFS ensembles are discussed in more detail in latter later exercises and are not covered herein this exercise.

2012 Operational ensemble

The dataset labelled 'ens_oper' in the macros uses the : This dataset is the operational ensemble from 2012 and was used in the Pantillon et al. publication. A key feature of this ensemble is use of a climatological SST field (you should have seen this in the earlier tasks!).

2016 Operational ensemble

The dataset labelled 'ens_2016' in the macros : This dataset is a reforecast of the 2012 event using the ECMWF operational ensemble from March 2016. Two key differences between the 2016 and 2012 operational ensembles are: higher horizontal resolution, and coupling of NEMO ocean model to provide SST from the start of the forecast.

Note that the analysis was not rerun for 20-Sept-2012. This means the reforecast using the 2016 ensemble will be using the original 2012 analyses. Also important is that only 10 EDA members were in use at that time, whereas 25 would be used in the 2016 operational ensemble, so each EDA member will be used multiple times. This will impact on the spread and clustering seen in the tasks in this exercise.

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Visualising ensemble forecasts can be done in various ways. During this exercise, in order to understand the errors and uncertainties in the forecast, we will use a number of visualisation techniques. 

General questions

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  1. How does the ensemble mean MSLP and Z500 fields compare to the HRES forecast and analysis?
  2. Examine the initial diversity in the ensemble and how the ensemble spread and error growth develops.  What do the extreme forecasts look like?
  3. Are there any members that consistently provide a better forecast? Can you identify the members close to observations/analysis both from a qualitative and quantitative approach?

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