NWC REU 2012
May 21 - July 31



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Conceptualizing How Forecasters Think About Uncertainty in the Context of Severe Weather

Astryd Rodriguez, James Correia Jr., Rachel Riley, and Gabe Garfield


What is already known:

  • Uncertainty in weather is derived from the chaotic nature of the atmosphere, sparseness of weather observations, errors inherent in the observing systems, and the increased availability of numerical models and their amplified use.
  • Forecasters have a hard time understanding uncertainty, and as a result, conveying it effectively among themselves and end users.

What this study adds:

  • Even though forecasters are aware of the uncertainty inherent in weather, they are not able quantify it, nor do they have a consensus on its definition.
  • Forecasters lack a conceptual model of uncertainty. Internal climatology is a mental tool forecasters use to asses uncertainty.
  • Population size and verification are the most important non-meteorological factors that influence major forecast decisions.


Uncertainty is inherent in every weather forecast. In order to create better methods to communicate uncertainty to the public and other end users, it is necessary to understand how forecasters think about and understand it. Around twenty hours of observational data from the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Tested Spring Experiment (HWT) 2012 was collected in order to analyze the participants’ assessment of uncertainty in a real forecasting environment. Also, ten in-depth interviews were carried out in which research and operational forecasters were asked several questions regarding uncertainty. The interviews were recorded and transcribed for analytical purposes. Results show that even though the forecasters in this study were aware of the inherent uncertainty in severe weather, they were unable to quantify it, nor did they have a consensus on its definition. Moreover, the forecasters lacked a conceptual model of uncertainty. Instead, they used their internal climatology as both a tool and a framework to describe and assess uncertainty. Finally, population size was the most important non-meteorological factor that they used to assess spatial uncertainty.

Full Paper [PDF]