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g=read("fc_pf.grib") |
Our GRIB contains several 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 we will write a loop going through the time steps. First, define the fieldset that will contain the resulting meansresults:
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e_mean=nil |
Next, add this piece of code for the loop (we store the time steps in a list).:
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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 happen.Filedset f now contains 50 fields. Next, compute the probability in as follows:
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f=f > val
f=100*mean(f) |
In t
Last, add it to the resulting fieldset:
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e_prob = e_prob & f |
We finish the macro by returning the resulting fieldset:
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return e_prob |
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 prob_shade Contouring icon and the title_prob Text Plotting icon. You will see that the ensemble mean hints that high wind speed can happen.
Visualising the ensemble spread
The ensemble spread is the standard deviation of the perturbed forecast members. You can compute 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(). Now do it.
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 ....
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