How Visual Design of Severe Weather Outlooks Can Affect Communication and Decision-Making

Bibliographic Data

ID22332016
AuthorsMary Anne T Clive (0000-0002-4093-7055, GNS Science), Emma E H Doyle (0000-0002-2878-0972, Massey University), Sally H Potter (0000-0003-3785-474X, GNS Science), Chris Noble (c MetService, Te Ratonga Tirorangi, Wellington, New Zealand), N Chris (0000-0002-3203-1704, GNS Science), David Johnston (0000-0001-5114-5355, Massey University), David M Johnston (Massey University)
Year2023
Volume15
Issue4
Pages979-997
Publication date2023-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueWeather, Climate, and Society (JOURNAL)
Journal identifiersISSN: 1948-8327 • E-ISSN: 1948-8335
PublisherAmerican Meteorological Society (PUBLISHER • US)
DOI10.1175/wcas-d-23-0010.1
OpenAlexW4387675531
LanguageEN
References cited18

Multiday severe weather outlooks can inform planning beyond the hour-to-day windows of warnings and watches. Outlooks can be complex to visualize, as they represent large-scale weather phenomena overlapping across several days at varying levels of uncertainty. Here, we present the results of a survey ( n = 417) that explores how visual variables affect comprehension, inferences, and intended decision-making in a hypothetical scenario with the New Zealand MetService Severe Weather Outlook. We propose that visualization of the time window, forecast area, icons, and uncertainty can influence perceptions and decision-making based on four key findings. First, composite-style outlooks that depict multiple days of weather on one map can lead to biased perceptions of the forecast. When responding to questions about a day for which participants accurately reported there was no severe weather forecast, those who viewed a composite outlook reported higher likelihoods of severe weather occurring, higher levels of concern about travel, and higher likelihoods of changing plans compared to those who viewed outlooks that showed weather for each day on a separate map, suggesting that they perceived the forecast to underrepresent the likelihood of severe weather on that day. Second, presenting uncertainty in an extrinsic way (e.g., “low”) can lead to more accurate estimates of likelihood than intrinsic formats (e.g., hue variation). Third, shaded forecast areas may lead to higher levels of confidence in the forecast than outlined forecast areas. Fourth, inclusion of weather icons can improve comprehension in some conditions. The results demonstrate how visualization can affect decision-making about severe weather and support several evidence-based considerations for effective design of long-term forecasts

Climatology · Comprehension · Data science · Perception · Visualization · Climate Change Communication and Perception · Computer Science · Data Visualization and Analytics · Psychology · Species Distribution and Climate Change · Artificial Intelligence