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Our GRIB contains three time steps (84, 90 and 96 hours, respectively) and we would like to compute the ensemble mean for each one separately. To achieve this goal we will write a loop going through the time steps. First, define the fieldset that will contain the results:

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By using the return statement our Macro behaves as if it were a fieldset (GRIB file). Drag it into the bottom left map and customise it with the wgust_shade Contouring icon and the title_mean Text Plotting icon. You will see that the ensemble mean hints that high wind speed can happenfor higher wind speeds in the area of question.

Visualising the ensemble spread

The ensemble spread is the standard deviation of the perturbed forecast members. You can compute it in a very similar way to the ensemble mean. The only difference is that this time you need to use the stdev() function instead of mean(). Drag Now it is your task to write a Macro for it. Once you finished your Macro drag it into the bottom right map and customise it with the wgust_spread_shade Contouring icon and the spread_mean Text Plotting icon. . You will see that the ensemble .spread is fairly high in the investigated area indicating that ...

Part 2:

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Checking the probabilities

The next step is to In this part we will estimate the risk of the wind gust being higher than certain thresholds: i.e. we will compute some probabilities.  We will write a macro to compute the probability of the wind gust exceeding 20 m/s,

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