Baseballs and Caps - Messaging Uncertainty

Imagine yourself at the forecast desk, or about to brief partners, or working up a “Key Takeaways” graphic for social media. You’re looking through various model guidance and you see a very consistent trend for a very concerning environment over the next 24 hours, characterized by extreme instability (4000-5000 j/kg MLCAPE), modest shear (30-35kt effective), weak inhibition (-25 j/k CIN), and very steep lapse rates (8-9 C/km). It’s the kind of environment you tell your spouse to keep the flashlight and basement handy. But, there’s a caveat. You can’t find any appreciable large-scale forcing for ascent and low-level convergence is weak, at best. Oh, and there’s a little ridging building in aloft. It’s one of those very conditional, but non-zero threats. The thing is, we’re not talking about a conditional threat of pea size hail and 30 mph wind gusts. This is a conditional threat of baseballs, 80 mph wind gusts, and the occasional EF-3 tornado. It may be that only one storm forms, but the impact would be high. But, from a probabilistic standpoint, it’s likely that people will see puffy clouds and experience a hot, muggy, storm-free day. What do you do?

It’s a not-to-uncommon question faced by Meteorologists on the Plains, especially in the transition from spring to summer patterns, where instability is trending higher, but the stronger flow aloft is shifting away from the area and forcing is weak. It’s a target of opportunity that probably deserves some attention, but how much?

This week I begin my fourth year forecasting the weather in the Central Plains, and I still have a lot to learn. These conditional high-impact days are intriguing, a challenge to forecast, and carry a lot of weight. If you tell people severe weather is possible, will they treat it like it’s likely? Does the crying wolf syndrome come into play? What if you skip messaging the low probability of occurrence, but then it happens? Which is worse, crying wolf or “it hit without warning”? Maybe the answer varies by region of the country? Maybe there isn’t a one-size-fits-all-regions answer.

There are a lot of factors at play here, some of which social science probably gives us the best avenue moving forward in an increasingly probabilistic realm of messaging.

I recently asked some fellow Mets, on Twitter, what they thought this scenario (if interested, you can read the thread to see the discussion). Something in that discussion that struck me was our ability, as Meteorologists, to message the environment. We focus a lot on messaging the forecast, but I’m seeing more and more that the message can’t stop there. Dr. Julie Demuth suggests that we “stop thinking of a given forecast as the end-point and start thinking of it as the starting-point for a conversation.”. In our scenario above, our users are seeing a low probability of storms in our forecast, but do they understand the magnitude of severe weather that could occur if storms do form? I wonder if targets of opportunity extend beyond just our forecast grids. Maybe the environment, itself, is a target.

We may not change our forecast, but we might consider changing our messaging.

Three years ago this month, I worked a shift that featured a conditional, high-impact environment.  I ended up working a 13 hour shift that day…a day that included baseball size hail and an EF-3 tornado. Three years later, I worked a shift with a similar conditional threat with a very similar environment. That shift ended on time, and did not include any baseballs or tornadoes. Two shifts with a similar, high-impact potential, but with drastically different outcomes.

Probabilistic forecasting isn’t exactly new for Meteorologists, or even our users. However, it appears that we are moving into an advanced realm of forecasting and messaging that has a higher ratio of probabilistic to deterministic info, with a learning curve both for us and for our users. I get the sense that there isn’t one right answer for all scenarios, but I still am not sure where to land on these “fringe” events, so-to-speak. I’m hoping continued discussion, group-think, trial and error, user feedback, and social science findings will help us as we continue to seek effective messaging of low-probability events.

If you have any ideas, suggestions, or comments on this topic, I’d love to hear them! Comment on the post, shoot me a message, or add to the Twitter discussion. We’re better off if we learn together.