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For a forecast ensemble where all values were the same, the CDF would be a vertical straight line.
Plot the CDF for 3 locations
This exercise uses the cdf.mv icon. Right-click, select 'Edit' and then:
- Plot the CDF of MSLP for the 3 locations listed in the macro.e.g. Reading, Amsterdam, Copenhagen.
- If time, change the forecast run date and compare the CDF for the different forecasts.
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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 10m/s = 20% probability ; winds > 40 mph 20m/s = 40% ; winds > 60 mph 30m/s = 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 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 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. |
Exercise 4. Exploring the role of uncertainty using OpenIFS forecasts
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The different macros available for this exercise are very similar to those in previous exercises.
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_spag.mv : this will produce spaghetti maps for a particular parameter contour value : this produces 4 plots showing the ensemble spread from the OpenIFS experiments compared to the analysis. ens_exps_to_an_spag.mv : this produces 4 plots showing the ensemble spread from the OpenIFS experiments : 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. |
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