The Subtle Yet Fundamental Differences Between Deterministic and Probabilistic Weather Forecasting (credit: National Weather Service and Tropical Tidbits)
DISCUSSION: For many years, weather forecasting has proven challenging for forecasters and researchers. Marked by consistent improvement yet continual obstacles, the nature of forecasting any type of weather event from benign showers to a full-scale severe weather outbreak is loaded with stochasticity. But at the core of weather forecasting, two schools of thought dominate the practice: deterministic and probabilistic forecasting. Each one of these is subtly different at the surface, but fundamentally they have their characteristic differences.
Deterministic forecasts are based specifically on a given value or range for an area at a given time (e.g., temperature at morning rush hour or evening commute). This is the kind of product we are used to seeing on forecast bulletins and on local news media. Examples of a deterministic forecast include the first tweet above with a range of values for potential ice accumulation over central Oklahoma. A precise value or time is important for the general public as it gives people a frame of reference for what to expect.
Probabilistic forecasts take on a different approach and instead focus on the likelihood that a parameter of any weather event is likely to exceed or occur in a given area. There are indeed various tools that facilitate the growth and understanding of improving forecast accuracy through probabilistic forecasting methods. Mentioned in a previous article with more detail, ensembles are different iterations with parameters tuned slightly differently to reflect differing outcomes. This approach sacrifices a specific (or range) number in exchange for a probability of occurrence beyond a certain threshold (ex: probability of rainfall total greater than 0.01 inches suggested by the second tweet above). Yes, it may seem tricky given that it’s different than the accustomed way, but it is meant to illustrate the difference and carries an emphasis of its own regard.
Agencies like NOAA’s National Severe Storms Laboratory are leading projects such as the Warn-on-Forecast experiment which utilizes sophisticated and refined modeling and data collection techniques to generate weather forecasts based greatly on probability of occurrence. What is the ultimate goal? Forecasters could utilize the added information and produce more accurate forecasts and respond quicker to developing hazards. This in turn could lead to a greater chance of saving life and property in the event of hazardous weather phenomena like tornadoes, flash floods, and large hail by providing ample watch/warning times to the public.
It’s safe to say that as forecasting techniques become more refined with time, these two schools of thought will continue to branch out in technicality and carry a bigger impact in their own regard. What do you think about the differences between the two? Would you rather prefer a deterministic forecast with say a total range of rainfall in a given day, or a probabilistic forecast with a message of likelihood that it will rain/storm on a given day or time? Let us know in the comments!
Image credit: Tropical Tidbits
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© 2019 Meteorologist Brian Matilla