By Chris Bedford, Sailing Weather Service
Chris Bedford is the world’s leading meteorologist for the sport of sailing. Chris currently serves as the meteorologist for American Magic, the New York Yacht Club entry in the 36th America’s Cup. With over thirty years of experience in marine and coastal meteorology, Chris has consulted with thousands of teams in boats as small as 8 feet and as large as 100+. Besides American Magic, Chris has been a part of ten prior America’s Cup Teams (winning 4) and has supported eight Volvo/Whitbread/Ocean Race Teams (also winning 4). Chris is the meteorologist for the Chicago Yacht Club Race to Mackinac and other CYC racing events.
The flood of always changing data, observations, models, and circumstances make the task of weather prediction extraordinarily challenging. Personally, I feel that every forecast I make is obsolete the instant I send it out as there is always new information coming that will alter the forecast. Every meteorologist has developed their own approach and process to making a forecast. But there are common aspects that every trained forecaster follows before they apply their own spin on the problem. The common process is scientifically based. The individualized portion is the “art” of weather prediction, and that is unique to a particular forecaster.
Meteorology is a hard science. The atmosphere follows established laws of physics and chemistry. Chuck Doswell, a renowned severe weather meteorologist, refers to a good forecaster as one that can balance the “triad of components of a healthy science: 1) Theory, 2) Observation, and 3) Modeling.”
If your forecast process is comprised primarily of looking at a bunch of models and deciding which to believe, then you are a) not forecasting, and b) wasting your time. Of the myriad of models available (and there are literally over a hundred you could look at to make a single CYC Weather Challenge forecast), how can you know which is the “correct” one or, as some people refer to it, “the model of the day.” The goal is to ADD VALUE over the model, and that can only be accomplished by analyzing data (observations) and applying your understanding of meteorology (theory). Models are a GUIDE in that process (In fact, meteorologists refer to models as “Guidance”).
Weather forecasting is not black and white. “Add value” is – in and of itself – an interesting phrase. Adding value doesn’t necessarily mean getting the lowest error score. You can have the lowest error score, but make one wrong forecast at the wrong time and the impact on the user could be huge. You could predict the minimum temperature above 32F on 10 straight days and be correct 9 out of 10 times (90%!). But the farmer will only care about the one day you were wrong and it was 29F (resulting in a damaged crop since he/she did not initiate frost/freeze protocols based on the forecast).
The real emphasis is on providing actionable information for a user. Let’s consider another example. Say there is a crane operator who has an established limit of 25 knots, above which operations must cease to maintain a suitable safety envelope. Predicting whether or not the wind will exceed 25 knots is key and quite frankly an easier “GO/NO GO” forecast than predicting the maximum wind speed for the day. But for this particular user, you add value by identifying WHEN during the day that limitation will be exceeded AND communicating it effectively. Will it be over 25 knots all day or can some of the day be salvaged and, if so, when will that be so that work crews and related equipment can be scheduled to minimize downtime?
So, as you are sitting down to make your forecast today, think about your process. Have you reviewed the observations and analyzed the existing state of the atmosphere? Do the models adequately and consistently reflect the initial state of the atmosphere? Can you identify the processes at play (without models!) and understand their causes and potential outcomes based on meteorological theory? What is/are the forecast problem(s) for the day? Am I respecting and adequately reflecting uncertainty in my forecast and adding value over, say, a model consensus forecast?
When I next write, I’ll talk more about the forecast process and perhaps provide you some structure around which to make your CYC Weather Challenge forecast.