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Introduction

The ECMWF operational ensemble forecasts for the western Mediterranean region exhibited high uncertainty while Hurricane Nadine was slowly moving over the eastern N.Atlantic in Sept. 2012. Interaction with an Atlantic cut-off low produced a bifurcation in the ensemble and significant spread, which controls both the track of Hurricane Nadine and the synoptic conditions downstream.

The HyMEX (Hydrological cycle in Mediterranean eXperiment) field campaign was also underway and forecast uncertainty was a major issue for planning observations during the first special observations period of the campaign.

This interesting case study examines the forecasts in the context of the interaction between Nadine and the Atlantic cut-off low in the context of ensemble forecasting. It will explore the scientific rationale for using ensemble forecasts, why they are necessary and they can be interpreted, particularly in a "real world" situation of forecasting for a observational field campaign.

 

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titleThis case study is based on the following paper which is recommended reading

Pantillon, F., Chaboureau, J.-P. and Richard, E. (2015), 'Vortex-vortex interaction between Hurricane Nadine and an Atlantic cutoff dropping the predictability over the Mediterranean,   http://onlinelibrary.wiley.com/doi/10.1002/qj.2635/abstract

In this case study

In the exercises for this interesting case study we will:

  • Study the development of Hurricane Nadine and the interaction with the Atlantic cut-off low using the ECMWF analyses.
  • Study the performance of the ECMWF high resolution (HRES) deterministic forecast of the time.
  • Use the operational ensemble forecast to look at the forecast spread and understand the uncertainty downstream of the interaction.
  • Compare a reforecast using the May/2016 ECMWF operational ensemble with the 2012 ensemble forecasts.
  • Use principal component analysis (PCA) with clustering techniques (see Pantillon et al) to characterize the behaviour of the ensembles.


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Note

If the plotting produces thick contour lines and large labels, ensure that the environment variable LC_NUMERIC="C" is set before starting metview.


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Differences between the 2018 ENM tutorial and the original 2016 tutorial.

1. Exercise 6. "Exploring the role of uncertainty" has been completely removed.




Note
titleCaveat on use of ensembles for case studies

In practise many cases are aggregated in order to evaluate the forecast behaviour of the ensemble. However, it is always useful to complement such assessments with case studies of individual events, like the one in this exercise, to get a more complete picture of IFS performance and identify weaker aspects that need further exploration.

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Q. Using these two macros, compare the 2012 and 2016 forecast ensemble. Which was the better forecast for HyMEX flight planning?

Exercise 6. Exploring the role of uncertainty

To further understand the impact of the different types of uncertainty (initial and model), some forecasts with OpenIFS have been made in which the uncertainty has been selectively disabled. These experiments use a 40 member ensemble and are at T319 resolution, lower than the operational ensemble.

As part of this exercise you may have run OpenIFS yourself in the class to generate another ensemble; one participant per ensemble member.

Recap

    • EDA is the  Ensemble Data Assimilation.
    • SV is the use of Singular Vectors to perturb the initial conditions.
    • SPPT is the stochastic physics parametrisation scheme.
    • SKEB is the stochastic backscatter scheme applied to the model dynamics.

Experiments available:

  • Experiment id: ens_both.  EDA+SV+SPPT+SKEB  : Includes initial data uncertainty (EDA, SV) and model uncertainty (SPPT, SKEB)
  • Experiment id: ens_initial.  EDA+SV only  : Includes only initial data uncertainty
  • Experiment id: ens_model. SPPT+SKEB only : Includes model uncertainty only

The aim of this exercise is to use the same visualisation and investigation as in the previous exercises to understand the impact the different types of uncertainty make on the forecast.

A key difference between this exercise and the previous one is that these forecasts have been run at a lower horizontal resolution. In the exercises below, it will be instructive to compare with the operational ensemble plots from the previous exercise.

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titleTeam working

For this exercise, we suggest either each team focus on one of the above experiments and compare it with the operational ensemble. Or, each team member focus on one of the experiments and the team discuss and compare the experiments.

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titleAvailable plot types

 The different macros available for this exercise are very similar to those in previous exercises.

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For this exercise, use the icons in the row labelled 'Experiments'. These work in a similar way to the previous exercises.

ens_exps_rmse.mv     : this will produce RMSE plumes for all the above experiments and the operational ensemble.

ens_exps_to_an.mv   : this produces 4 plots showing the ensemble spread from the OpenIFS experiments compared to the analysis.

ens_exps_to_an_spag.mv : this will produce spaghetti maps for a particular parameter contour value compared to the analysis.

ens_part_to_all.mv     : this allows the spread & mean of a subset of the ensemble members to be compared to the whole ensemble.

Info

For these tasks the Metview icons in the row labelled 'ENS' can also be used to plot the different experiments (e.g. stamp plots). Please see the comments in those macros for more details of how to select the different OpenIFS experiments.

Remember that you can make copies of the icons to keep your changes.

Task 1. RMSE plumes

Use the ens_exps_rmse.mv icon and plot the RMSE curves for the different OpenIFS experiments.

Compare the spread from the different experiments.

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titleQuestions

The OpenIFS experiments were at a lower horizontal resolution.  How does the RMSE spread compare between the 'ens_oper' and 'ens_both' experiments?

Task 2. Ensemble spread and spaghetti plots

Use the ens_exps_to_an.mv icon and plot the ensemble spread for the different OpenIFS experiments.

Also use the ens_exps_to_an_spag.mv icon to view the spaghetti plots for MSLP for the different OpenIFS experiments.

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Q. What is the impact of reducing the resolution of the forecasts? (hint: compare the spaghetti plots of MSLP with those from the previous exercise).
Q. How does changing the representation of uncertainty affect the spread?
Q. Which of the experiments ens_initial and ens_model gives the better spread?
Q. Is it possible to determine whether initial uncertainty or model uncertainty is more or less important in the forecast error?

If time:

  • use the ens_part_to_all.mv icon to compare a subset of the ensemble members to that of the whole ensemble. Use the stamp_map.mv icon to determine a set of ensemble members you wish to consider (note that the stamp_map icons can be used with these OpenIFS experiments. See the comments in the files).

Task 3.  What initial perturbations are important

The objective of this task is to identify what areas of initial perturbation appeared to be important for an improved forecast in the ensemble.

Using the macros provided:

  • Find an ensemble member(s) that gave a consistently improved forecast and take the difference from the control.
  • Step back to the beginning of the forecast and look to see where the difference originates from. 

Use the large geographical area for this task. Use the MSLP and z500 fields (and any others you think are useful).

Task 4. Non-linear development

Ensemble perturbations are applied in positive and negative pairs. This is done to centre the perturbations about the control forecast.

So, for each computed perturbation, two perturbed initial fields are created e.g. ensemble members 1 & 2 are a pair, where number 1 is a positive difference compared to the control and 2 is a negative difference.

  • Choose an odd & even ensemble pair (use the stamp plots). Use the appropriate icon to compute the difference of the members from the ensemble control forecast.
  • Study the development of these differences using the MSLP and wind fields. If the error growth is linear the differences will be the same but of opposite sign. Non-linearity will result in different patterns in the difference maps.
  • Repeat looking at one of the other forecasts. How does it vary between the different forecasts?

If time:

  • Plot PV at 320K. What are the differences between the forecast? Upper tropospheric differences played a role in the interaction of Hurricane Nadine and the cut-off low.

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Appendix

Further reading

For more information on the stochastic physics scheme in (Open)IFS, see the article:

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