A colleague of mine said something years ago that struck me as insightful: every model forecast ever issued was wrong! Wrong in some way or another, to a greater or lesser extent. Obviously, some forecasts are better than others, but none of them have ever been absolutely perfect. His point was to suggest that human forecasters need to avoid basing their forecasts purely on Numerical Weather Prediction (NWP) model output - a notion with which I agree fully. However, the same can be said of every forecast ever issued by human forecasters, as well! The reality is that we can never predict the weather with absolute certainty. I've not the space nor the inclination to go into the details of why this isn't just my opinion (maybe later) - it is, rather, based solidly in our scientific understanding of the atmosphere. So, put in terms of the information content of a weather forecast of any sort, a weather forecast is not a statement of what definitely and certainly is about to happen in the future, in detail.
Because weather has substantial impact on human society, it's obvious that people want to know what's going to happen weatherwise ahead of time. I'm fond of saying "Yes, of course, and people in Hell want a glass of ice water!" - which I heard many years ago from a co-worker. What people want isn't necessarily what they're going to get. The fact is that we have never been able to provide that sort of information and there is every reason to believe we'll never have that capability. That notwithstanding, our relationship to our users predominantly has been such as to perpetuate the myth that we can provide that with 100% confidence. Users want something and we pretend we can give it to them. Surely our users know by now that such a capability doesn't exist! Their own empirical evidence is that we can't do it and that evidence is at least a contributor to the widespread notion that weather forecasts are inevitably and totally wrong.
If plausible bounds are put on what constitutes a good forecast (as opposed to a perfect forecast), it should be noted that these days, today's weather forecasts are correct (within those bounds) a high percentage of the time (e.g., for 24-h daily maximum and minimum temperatures within 5 degrees of the observed value, it's about 85% or better). So our weather forecasts currently contain useful information (despite not being perfect), within some limits, out to about 7-10 days. What you experience is usually fairly close to what we forecast most of the time. Beyond that "predictability limit" of 7-10 days, our weather forecasts become no more accurate than what we would see if we simply forecast what climatology (i.e., the long term averages for a particular location, date, and time) says we should expect. At that limit point, we say our forecasts no longer have any skill, relative to climatology. The greater the lead time, the less accurate the forecasts (and the lower their skill), on the average, out to the predictability limit.
What I would like to have us do is re-negotiate the contract we have with the users of weather information. We need to be able to provide them with whatever forecast information we have, including some sort of statement of the uncertainty associated with the information we have. Let's put aside the existing relationship, in favor of putting information out that we actually have to capability to provide! Now the language of uncertainty is probability, and I'm constantly being told that people don't want probability (the glass of water in Hell problem) or they don't understand probability. You don't need to be an expert in probability theory to put it to good use, and many people are very familiar with the notion of odds (probability in another form). What we are doing now, with the lone exception of precipitation probabilities, is pretending to provide absolute certainty. The historical background of how Probability of Precipitation (PoP) was introduced is interesting but far more than I want to expound upon in this blog. Whatever the problems are with PoPs, they are a far more meaningful way to express our forecast information than all the non-probabilistic elements in a weather forecast. If we don't express our uncertainties, we are actually withholding information from forecast users! That can't be a good thing, and it comes back to bite us, time and time again.
An analogy with sports is a fair comparison, at least to some extent.
Our predictions for who will win the Super Bowl in the pre-season have much greater uncertainty than the night before the game is actually played. Even then, there remains some
uncertainty, and reasonable people can disagree about the outcome right
up to the time the whistle blows and the winner is known with absolute
certainty.
Therefore, to answer the question posed by the title of this blog, a weather forecast contains the forecaster's best estimate of what that forecaster (who might possibly be an NWP model) anticipates is going to happen with the weather. It's not a guess, but rather our assessment of the situation and what we believe is the most probable weather that will occur, at the time we issued the forecast, given the finite accuracy limits on the method used to create that forecast. As new information comes in, that forecast can change, sometimes dramatically. Our diagnosis of what is about to happen virtually never coincides precisely with reality, but at times we can get it fairly close, especially at the shorter lead times.
A weather forecast always should include information about forecast uncertainty and that is necessarily going to be more complicated to explain than just reading a list of numbers. More information inevitably requires more effort. If the user is going to make the best use of the information we reasonably can provide, the user must accept some of the responsibility to pay attention to the forecast, to learn what the forecast actually is saying. If all you want is the numbers, then you've forfeited a good deal of the value the forecast is trying to provide. The choice can be left up to the user.
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