Communicating forecast uncertainty: public perception of weather forecast uncertainty

2010 ◽  
Vol 17 (2) ◽  
pp. 180-195 ◽  
Author(s):  
Susan Joslyn ◽  
Sonia Savelli
Keyword(s):  
Public Perception ◽  
Weather Forecast ◽  
Forecast Uncertainty

scholarly journals Understanding emergency manager forecast use in severe weather events.

2018 ◽  
pp. 95-105 ◽  
Author(s):  
Sean Ernst ◽  
Daphne LaDue ◽  
Alan Gerard
Keyword(s):  
Critical Incident ◽  
Information Needs ◽  
Weather Forecast ◽  
Severe Weather ◽  
Relational Trust ◽  
Forecast Uncertainty ◽  
Emergency Managers ◽  
Emergency Manager ◽  
Uncertainty Information ◽  
The Impact

For Emergency Managers (EMs), preparations for severe weather have always relied on accurate, well-communicated National Weather Service (NWS) forecasts. As part of their constant work to improve these forecasts, the NWS has recently begun to develop impact-based products that share forecast uncertainty information with EMs, including the Probabilistic Hazard Information (PHI) tool. However, there is a lack of research investigating what forecast uncertainty information EMs understand, and what information needs exist in the current communication paradigm. This study used the Critical Incident Technique to identify themes from incidents involving weather forecast information that went well, or not so well, from the perspective of the EMs responding to them. In total, 11 EMs from a variety of locales east of the Rockies were interviewed—six of whom were county-level, two city, two state, and one from a school district. We found that EMs sought increased forecast detail as a potential event approached in time and built relational trust in the NWS through repeated interactions. EMs had difficulty preparing for events when they did not have details of the expected impacts, or the likelihood of those impacts, for their regions. In summary, EMs are already starting to work in an uncertainty-friendly frame and could be responsive to the impact details and increased forecaster relations proposed with the PHI tool.

Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty

2016 ◽  
Vol 24 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Rafal Kicinger ◽  
Jit-Tat Chen ◽  
Matthias Steiner ◽  
James Pinto
Keyword(s):  
Weather Forecast ◽  
Forecast Uncertainty ◽  
Airport Capacity ◽  
Explicit Consideration ◽  
Capacity Prediction

Changes to intrinsic weather forecast uncertainty in one scenario of extreme future climate

2016 ◽  
Vol 142 (698) ◽  
pp. 2102-2118 ◽  
Author(s):  
J. G. McLay ◽  
C. A. Reynolds ◽  
E. Satterfield ◽  
D. Hodyss
Keyword(s):  
Weather Forecast ◽  
Future Climate ◽  
Forecast Uncertainty

Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public

2008 ◽  
Vol 23 (5) ◽  
pp. 974-991 ◽  
Author(s):  
Rebecca E. Morss ◽  
Julie L. Demuth ◽  
Jeffrey K. Lazo
Keyword(s):  
Weather Forecast ◽  
Nationwide Survey ◽  
Forecast Uncertainty ◽  
Weather Forecasts ◽  
Temperature Forecast ◽  
Different Types ◽  
Probability Of Precipitation ◽  
Uncertainty Information ◽  
The U.S ◽  
Do So

Abstract Weather forecasts are inherently uncertain, and meteorologists have information about weather forecast uncertainty that is not readily available to most forecast users. Yet effectively communicating forecast uncertainty to nonmeteorologists remains challenging. Improving forecast uncertainty communication requires research-based knowledge that can inform decisions on what uncertainty information to communicate, when, and how to do so. To help build such knowledge, this article explores the public’s perspectives on everyday weather forecast uncertainty and uncertainty information using results from a nationwide survey. By contributing to the fundamental understanding of laypeople’s views on forecast uncertainty, the findings can inform both uncertainty communication and related research. The article uses empirical data from a nationwide survey of the U.S. public to investigate beliefs commonly held among meteorologists and to explore new topics. The results show that when given a deterministic temperature forecast, most respondents expected the temperature to fall within a range around the predicted value. In other words, most people inferred uncertainty into the deterministic forecast. People’s preferences for deterministic versus nondeterministic forecasts were examined in two situations; in both, a significant majority of respondents liked weather forecasts that expressed uncertainty, and many preferred such forecasts to single-valued forecasts. The article also discusses people’s confidence in different types of forecasts, their interpretations of the probability of precipitation forecasts, and their preferences for how forecast uncertainty is conveyed. Further empirical research is needed to study the article’s findings in other contexts and to continue exploring perception, interpretation, communication, and use of weather forecast uncertainty.

scholarly journals Reply to “Comments on ‘What Is the Predictability Limit of Midlatitude Weather?’”

2020 ◽  
Vol 77 (2) ◽  
pp. 787-793
Author(s):  
Y. Qiang Sun ◽  
Fuqing Zhang ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Jan-Huey Chen ◽  
...  
Keyword(s):  
Weather Forecast ◽  
Model Error ◽  
Parametric Model ◽  
Initial Condition ◽  
Forecast Uncertainty ◽  
Global Models ◽  
Perfect Model

Abstract In their comment, Žagar and Szunyogh raised concerns about a recent study by Zhang et al. that examined the predictability limit of midlatitude weather using two up-to-date global models. Zhang et al. showed that deterministic weather forecast may, at best, be extended by 5 days, assuming we could achieve minimal initial-condition uncertainty (e.g., 10% of current operational value) with a nearly perfect model. Žagar and Szunyogh questioned the methodology and the experiments of Zhang et al. Specifically, Žagar and Szunyogh raised issues regarding the effects of model error on the growth of the forecast uncertainty. They also suggested that estimates of the predictability limit could be obtained using a simple parametric model. This reply clarifies the misunderstandings in Žagar and Szunyogh and demonstrates that experiments conducted by Zhang et al. are reasonable. In our view, the model error concern in Žagar and Szunyogh does not apply to the intrinsic predictability limit, which is the key focus of Zhang et al. and the simple parametric model described in Žagar and Szunyogh does not serve the purpose of Zhang et al.

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