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Sonia Savelli

Senior Research Cognitive Psychologist

Email

ssavelli@apl.washington.edu

Phone

206-221-2362

Education

B.S. Mathematics, University of Waterloo, 1988

Ph. D. Philosophy, University of Washington, 2009

Publications

2000-present and while at APL-UW

Boater safety: Communicating weather forecast information to high stakes end users

Savelli, S., and S. Joslyn, "Boater safety: Communicating weather forecast information to high stakes end users," Weather Clim. Soc., 4, 7-19, doi:10.1175/WCAS-D-11-00025.1, 2012.

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1 Jan 2012

Recreational boaters in the Pacific Northwest understand that there is uncertainty inherent in deterministic forecasts as well as some of the factors that increase uncertainty. This was determined in an online survey of 166 boaters in the Puget Sound area. Understanding was probed using questions that asked respondents what they expected to observe when given a deterministic forecast with a specified lead time, for a particular weather parameter, during a particular time of year. It was also probed by asking respondents to estimate the number of observations, out of 100 or out of 10, that they expected to fall within specified ranges around the deterministic forecast. Almost all respondents anticipated some uncertainty in the deterministic forecast as well as specific biases, most of which were born out by an analysis of local National Weather Service verification data. Interestingly, uncertainty and biases were anticipated for categorical forecasts indicating a range of values as well, suggesting that specifying numeric uncertainty would improve understanding. Furthermore, respondents' answers suggested that they expected a high rate of false alarms among warning and advisory forecasts. Nonetheless, boaters indicated that they would take precautionary action in response to such warnings, in proportions related to the size of boat they were operating. This suggests that uncertainty forecasts would be useful to these experienced forecast consumers, allowing them to adapt the forecast to their specific boating situation with greater confidence.

Reducing probabilistic weather forecasts to the worst-case scenario: Anchoring effects

Joslyn, S., S. Savelli, and L. Nadav-Greenberg, "Reducing probabilistic weather forecasts to the worst-case scenario: Anchoring effects," J. Exp. Psychol.-Appl., 17, 342-353, 2011.

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1 Dec 2011

Many weather forecast providers believe that forecast uncertainty in the form of the worst-case scenario would be useful for general public end users. We tested this suggestion in 4 studies using realistic weather-related decision tasks involving high winds and low temperatures. College undergraduates, given the statistical equivalent of the worst-case scenario (1 boundary of the 80% predictive interval), demonstrated biased understanding of future weather conditions compared with those given both bounds or no uncertainty information. We argue that this was due to an anchoring effect on numeric estimates, which were closer to the worst-case scenario than was warranted and increased linearly as the anchor became more extreme. In many situations tested here, anchoring in numeric estimates also extended to subsequent binary decisions, leading participants with the worst-case scenario to take action more often than did other participants. These results suggest that worst-case scenario forecasts can mislead the user. They appear to convince people that wind speeds will be higher and temperatures will be lower than what are indicated by the forecast. In addition, participants systematically "corrected" the forecast they were given. This effect was most prominent in the condition in which no uncertainty was provided, suggesting that people feel compelled to take uncertainty into account, even when it is not acknowledged by the forecast. Both the anchoring and correction biases were least evident when both bounds were provided, suggesting that balanced uncertainty leads to the best understanding of future weather conditions.

Communicating forecast uncertainty: Public perception of weather forecast uncertainty

Joslyn, S., and S. Savelli, "Communicating forecast uncertainty: Public perception of weather forecast uncertainty," Meteorol. Appl., 17, 180-195, 2010.

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1 Jun 2010

The general public understands that there is uncertainty inherent in deterministic forecasts as well as understanding some of the factors that increase uncertainty. This was determined in an online survey of 1340 residents of Washington and Oregon, USA. Understanding was probed using questions that asked participants what they expected to observe when given a deterministic forecast with a specified lead time, for a particular weather parameter, during a particular time of year. It was also probed by asking participants to estimate the number of observations, out of 100, that they expected to fall within specified ranges around the deterministic forecast. Almost all respondents (99.99%) anticipated some uncertainty in the deterministic forecast. Furthermore, their answers suggested that they expected greater uncertainty for longer lead times when the forecasted value deviated from climatic norms. Perhaps most noteworthy was that they expected specific forecast biases (e.g. over-forecasting of extremes), most of which were not borne out by an analysis of local National Weather Service verification data. In summary, users had well-formed uncertainty expectations suggesting that they are prepared to understand explicit uncertainty forecasts for a wide range of parameters. Indeed, explicit uncertainty estimates may be necessary to overcome some of the anticipated forecast biases that may be affecting the usefulness of existing weather forecasts. Despite the fact that these bias expectations are largely unjustified, they could lead to adjustment of forecasts that could in turn have serious negative consequences for users, especially with respect to extreme weather warnings.

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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