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which can be written:
where:
- Aa and Af are the atmospheric and model variability respectively around the climate
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- cov refers to the covariance.
Hence the level of forecast accuracy is determined not only by the predictive skill, as reflected in the covariance term, but also by the general variability of the atmosphere, expressed by Aa, and by how well the model simulates this, expressed by Af.
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This does not look very impressive, rather the opposite, but, paradoxically, both Users X and Y benefit highly from this special service. This is because they are now free to interpret the forecasts in their own way. (see Fig12.A.20).
- User X,
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- has low protection costs
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- and can afford to interpret the "don't know” forecast as if it could rain and therefore
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- decides to take protective action. By doing so, User X drastically lowers his costs to €10 per day
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- . This is €20 cheaper than following
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- the forecasts of Agency C.
- User Y,
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- has expensive protection
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- costs and will prefer to interpret "don't know” forecast as if there will be no rain and decides not to
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- take protective action. By doing so, User Y lowers his costs to €26 per day
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- . This is similar to following the forecasts of Agency B.
Fig12.A.20: The expected daily expenses when the end-users are free to interpret the "don't know” forecast either as "rain", if they have a low c/L ratio, or as "no rain", if their c/L ratio is high.
So what might appear as "cowardly" forecasts prove to be more valuable for the end-users! If If forecasters are uncertain, they should say so and thereby . In this way forecasters can gain respect and authority in the longer term.
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