Forecasts and their Value

Economic Value of Weather and Climate Forecasts, Richard W. Katz and Allan H. Murphy, editors, 1997 includes some hard core math.  But the idea explored is straightforward enough, and much of each paper included is spent on the considerations which direct the mathematics, so you needn't be up on the math to gain from the reading. 

Fundamentally, a forecast has zero economic value if it can't be, or isn't/won't be, used to increase a profit or reduce a loss.  The value lies in the decisions which can be (and are) made based on the forecast, and not the forecast's accuracy (abstractly considered) itself.

On an extreme example, the value of climate forecasts to James Inhofe is zero.  There is nothing, given his public statements, he would do in response to a climate forecast (regardless of how good) differently than with no information.   Also limited value of hurricane forecasts 5 days ahead to Rush Limbaugh, who dismissed (September 5th, 2017) the (extremely accurate, as it turned out) forecast of Hurricane Irma's landfall in Florida on the 10th.  On the 5th (follow link to news story with the details), he was dismissing the forecast as fake news / liberal conspiracy, and advising his listeners to ignore the forecast.  On the 8th, just 2 days ahead of the storm, he evacuated from Florida.  Given his listeners and advertisers, it may well have profited him to delay response.  People who couldn't evacuate because they listened to him for too long, different matter.

But it illustrates a different issue -- lead time and actions.  If you don't (can't, won't) do anything differently with 5 days' lead time than with 2 days' lead time, there's no value to you in the extra lead time for the forecast.  Scientists, of course, are very interested in the difference -- the better we understand hurricanes, the better (farther ahead) we can predict them.  But that's hard to put a dollar value to.  For me, certainly, 5 days lead time in knowing a hurricane is coming is far better than 2 days.  It gives me time to prepare the house for the winds and waters, and to make a considered retreat (meaning no traffic jam) outside the range of the hurricane.  With just 2 days warning, that becomes hard to do.  On the other hand, it would be no more helpful to me to know of a hurricane coming in August 16, 2019 than to know of one coming August 16, 2018.  And August 16th is probably no more useful to me than July 16th (from my vantage of June 3rd).

There are people and interests other than me and mine, however.  Home supply and repair stores, for instance, might profit greatly from knowing that they'll need a large stock of material and staff prior to a particular date.  Or even just that odds are higher than usual that their area will have a hurricane.

What are some weather or climate decisions you make?  How much difference does the accuracy of the prediction make?  How far ahead does it matter for your decisions to have the prediction?

Arctic Ice Guesses 2014

Have to bite the bullet here and discuss my guesses for the September 2014 Arctic sea ice extent average.  The thing which has made them so difficult is that they're so different from each other.  Now, one method I've retired.  It was simply so bad last year that there's no point in continuing it.  That is the one I did based on a population growth (of ice-free area) curve.

That leaves, however, two different model-based guessers.  The first one, which appears at the Sea ice prediction network as 'Wang', is based on doing a statistical regression between what the CFSv2 (climate forecast system, version 2) predicts for September ice area and what is observed.  The second also uses CFSv2, but in a different way.  Namely, we know that the model is biased towards ice being too extensive (which the Wang method addresses statistically) and to being too thick.  The Wu method is based on thinning the ice and seeing what the extent is thicker than a critical limit (60 cm it turns out).  (Both Wang and Wu work with me, or vice versa, and we discuss how to work on these guesses.)

The guesses are:
June -- Wang -- 6.3 million km^2 0.47 stdev
July -- Wang -- 5.9 million km^2 0.47 stdev
July -- Wu -- 5.1 million km^2 0.56 stdev
June -- Wu -- 4.8 million km^2 0.65 stdev

One of the things to notice is that the two estimates moved towards each other from June to July.  Wu rose, the higher Wang declined.  The second is, the Wu method has a standard deviation (variability of its estimate) that is double what it was last year.  Whatever is going on in the model, it is much less self-consistent in previous years.  Much more uncertain.  This is one of the reasons for ensemble modeling (part of the Wu approach).

You can also see that the Wang estimate is the highest of all -- even higher than the Watts up with that group estimate.  This is true in both June and July.

So, what's up?  Well, I'm not sure.  Some of it is certainly related to sea ice thickness estimates.  Xingren (Wu) did a different approach based on thickness for June, which we didn't submit, but which landed in between the official June estimates from Wang and Wu.  With the step towards convergence from June to July between Wu and Wang methods, I'm inclined to guess (a meta-guess) 5.5 million km^2 for September.  If this were to occur in reality, it probably suggests something important.  What, exactly, I'm still pondering.