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For those interested: The code that computes the clusters can be found in the Python script: This uses the 'ward' cluster method from SciPy. Other cluster algorithms are available. See http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage The python code can be changed to a different algorithm or the more adventurous can write their own cluster algorithm! |
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Exercise 5. Percentiles and probabilities
To further compare the 2012 and 2016 ensemble forecasts, plots showing the percentile amount and probabilities above a threshold can be made for total precipitation.
Use these icons:
Both these macros will use the 6-hourly total precipitation for forecast steps at 90, 96 and 102 hours, plotted over France.
Task 1. Plot percentiles of total precipitation
Edit the percentile_tp_compare.mv icon.
Set the percentile for the total precipitation to 75%:
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#The percentile of ENS precipitation forecast
perc=75 |
Run the macro and compare the percentiles from both the forecasts. Change the percentiles to see how the forecasts differ.
Task 2: Plot probabilities of total precipitation
This macro will produce maps showing the probability of 6-hourly precipitation for the same area as in Task 1.
In this case, the maps show the probability that total precipitation exceeds a threshold expressed in mm.
Edit the prob_tp_compare.mv and set the probability to 20mm:
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#The probability of precipitation greater than
prob=20 |
Run the macro and view the map. Try changing the threshold value and run.
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Q. Using these two macros, compare the 2012 and 2016 forecast ensemble. Which was the better forecast? |
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.
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