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Evaluating the forecasts

In this part of the exercise we will create the plot shown below:

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The ensemble spread is the standard deviation of the ENS 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(). 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 with a custom Text Plotting icon. You will see that the ensemble spread is fairly high in the investigated area.

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Checking the probabilities

In this part we will estimate the risk of the wind gust being higher than a certain threshold. We will compute the probability of the wind gust exceeding 22 m/s (≈ 80 k/m) and generate the plot shown below:

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Once you finished your Macro drag it into the bottom right map and customise it with the prob_shade Contouring icon and with a custom Text Plotting icon. You will see that there is indeed a reasonable probability hinting for a wind storm.

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Creating a stamp plot

In this part we will investigate the individual ENS members and create a plot showing them all for a given time step on the same page like this:

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Having done so run your Macro (this will take a minute or so) and try identify the ENS members predicting high wind speeds in our area.

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Creating a spaghetti plot

We finish the case study by looking into the predictability of the large scale flow pattern by generating spaghetti plots for 500 hPa geopotential from the same ENS run as we investigated before.  In a spaghetti plot each ENS member is rendered into the same map using a single isoline value. The plot we want to generate is shown below (it contains the spaghetti plot for 500 hPa geopotential using the 560 gpm isoline value):

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