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.

Value of the Mesoanalyst Position

Modern-day forecasting within the National Weather Service (NWS) continues to change as modeling, technology, and communications evolve. For some this is exciting...for others perhaps not as much. In reality, I would venture to say that for many, the evolution is a mix of both.

Last year, I read a #WxTwitter thread that discussed the Mesoanalyst role (Meso-A, I'll call it). The discussion drifted from the original topic to a discussion about hand analysis. The way the chat became so focused around hand analysis got me to thinking that perhaps there needs to be a change in how the position is viewed (I include myself in this).

Filling the Meso-A role doesn't always have to involve hourly hand analyses or manually calculating CAPE. The thought of either of those probably scares some people away. Perhaps at one time the position was centered around manually analyzing surface and upper air charts...for some it probably still is. Technology continues to advance and while I hope manual analysis is never completely abandoned, the days of whipping out the colored pencils may not be as widespread as it once was. So, let's step away from how tasks are accomplished and instead evaluate the role in consideration of current technology and model capabilities and whether it can still play an important role in the current NWS service model.

A key concept within the NWS right now is IDSS - Impact-Based Decision Support Services. Achieving goals within this framework requires some evolutions of past service models/concepts/methodologies. I believe one such evolution involves the Meso-A concept. To determine if a concept needs to evolve, we should start with why it was put there in the first place. I see the forecaster in the Meso-A position as being tasked with monitoring and effectively communicating the current state of the environment as well as expected near-term changes / trends.

Note that there is no explicit clarification on how this task is accomplished. Instead of focusing on a specific task, or means to accomplish a task, we should focus on the goal of the position to help determine if it fits into the current framework of NWS operations. Just purely based on how I defined it above, I would argue that it does still play an important role. But, let's dig a little deeper.

With advances in modeling and analysis, have we reached a point where the Meso-A tasks can be combined with other roles (communications, warnings, forecasting) as opposed to it being the primary task of one forecaster? This question may actually be more important than the definition of the position.

The Meso-A position isn't the only one to evolve. Think about how the communications role has evolved with the advent of social media. Comms isn't just answering the phones anymore. In fact, comms has evolved so much, it will sometimes be a 2-forecaster job. Does DSS get its own forecaster or is that lumped in with comms? In addition, radar operators now have Dual Pol, SAILS, TWIP research, etc to consider. Offices handle tasks differently, but the reality is that the workload has evolved with evolutions in messaging and technology.

With that in mind, is it possible that the Meso-A position is actually even more important now than ever before? Maybe in the past the forecaster answering the phones could also handle monitoring and communicating environmental changes. Now that person may be tasked with phones, social media, and DSS briefings. Consider, too, how the position can support ALL of the other positions.

A stronger-than-forecast low-level jet quickly developing with a subsequent increased tornado threat, for example, might be missed by other forecasters knee-deep in warnings, social media graphics, or in-situ oral briefings to emergency managers. The Meso-A forecaster noting this change, especially if it was not modeled well, could provide very important information to aid in warning decisions, staffing and/or forecaster responsibilities, and messaging to the public and partners.

Even with advances in modeling, there remains an important human element at times...it's the Target of Opportunity concept within the NWS. Whether the models are handling an event well or not, at minimum it can help to have someone monitor and communicate the current state of the atmosphere as well as important trends. Then, when you do have those events that are not being handled well by the  models, you have someone who can manually adjust on the fly as an important part of the office's life-saving mission.

The NWS strives to protect lives and property and I firmly believe the Meso-A position, if seen and utilized effectively, can be a vital Target of Opportunity within this mission. I have always believed in the role of the Mesoanalyst, but in thinking about it further, I have become even more convinced of its relevance. But, don't take my word for it. For some additional thoughts on the position, check out the findings in these RAMMB and OPG documents.

One final comment...the heart behind this post was not to argue for or against how the role is performed. My hope is to encourage folks to see the value of the role without the distraction of how one goes about accomplishing associated tasks. Effectively monitor and communicate...that's the idea. How that is done will differ from forecaster to forecaster, but I believe it is a vital support function during many severe weather events.