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b) Use the oper_to_an_diff.mv icon and plot the difference map between a forecast date and the analysis.
Choose We suggest looking at only one model run (forecast lead-time ) to look at (discuss with your team colleagues(run). If you want to change the default date, edit the following line:
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- 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 (SPPT) and the spectral backscatter scheme (SKEB)
- Singular Vectors: a way of representing the fastest growing modes in the initial state.
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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General questions
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THIS NEEDS IMPROVING
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Available plot types
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For these exercises please use the Metview icons in the row labelled 'ENS'. ens_rmse.mv : this is similar to the oper_rmse.mv in the previous exercise. It will plot the root-mean-square-error growth for the ensemble forecasts. ens_to_an.mv : this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, (c) the HRES deterministic forecast and (d) the analysis for the same date. stamp.mv : this plots all of the ensemble forecasts for a particular field and lead time. Each forecast is shown in a stamp sized map. Very useful for a quick visual inspection of each ensemble forecast. stamp_diff.mv : similar to stamp.mv except that for each forecast it plots a difference map from the analysis. Very useful for quick visual inspection of the forecast differences of each ensemble forecast. ens_to_an_runs_spag.mv : this plots a 'spaghetti map' for a given parameter for the ensemble forecasts compared to the analysis. Another way of visualizing ensemble spread.
Additional plots for further analysis:
ens_1x1.mv : this plots a single map of a single ensemble member, the mean or the spread. pf_to_cf_diff.mv : this useful macro allows two individual ensemble forecasts to be compared to the control forecast. As well as plotting the forecasts from the members, it also shows a difference map for each. |
Getting started
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Please refer to the handout showing the storm tracks labelled 'ens_oper' during this exercise. It is provided for reference and may assist interpreting the plots. Each page shows 4 plots, one for each starting forecast lead time. The position of the symbols is represents the centre of the storm centre valid for 28th Oct 2013 at 12Z12UTC. The colour of the symbols is the central pressure. The actual track of the storm from the analysis is show as the red curve with the position at 28th 12Z highlighted as the hour glass symbol. The control forecast for the ensemble is shown as the green curve and square symbol. The lines show the 12hr track of the storm; 6hrs either side of the symbol. Note the propagation speed and direction of the storm tracks. The plot also shows the centres of the barotropic low to the North. Q. What can be deduced about the forecast from these plots?
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Task 1: RMSE "plumes"
This is similar to task 1 in exercise 2, except now the RMSE curves for all the ensemble members from a particular forecast will be plotted. All 4 forecast dates are shown.
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Her Majesty The Queen has invited Royals from all over Europe to a garden party at Windsor castle on 28th October 2013 (~20 miles west of London). Your team is responsible for the weather prediction and decision to have an outdoor party for this event. You have 3-4 days to plan for the event. You can use the data and macros provided to you to first derive probabilities of severe weather at this location (this doesn't need to be exact so use the information for Reading). ii) The price of ordering the marquees and outdoor catering for the event is £100,000. Chances of the marquees falling apart when winds > 20 mph = 20% probability ; winds > 40 mph = 40% ; winds > 60 mph = 80%. Now what would the probabilities of the marquees failing be, given this new information from the rental service and the weather prediction you made? This is type of problem is often discussed in terms of risk, or the idea of the cost/loss ratio of a user. Here the loss (L) would be some financial value that would be at loss if the the bad weather forecast event happens and no precautionary actions had been taken. The costs (C) would be the financial value associated with precautionary actions in case the event happens. The ratio of the costs to the loss, often called C/L ratio, could then be used for decision making by users. If the costs are substantially smaller than the potential loss, then already a relatively small forecast probability for the event would indicate to take precautionary actions. Whereas for large C/L this is less, or not at all, the case. |
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Q. Compare the spread from the different experiments. Is it what you would expect?
Q. The OpenIFS experiments were at a lower horizontal resolution. How does the RMSE spread compare between the 'ens_oper' and 'ens_both' experiments?
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