1 Introduction
2 Background
2.1 Hurricane Models and Forecasts
2.2 Related Work
2.2.1 Visualizing Risk and Uncertainty
2.2.2 Crisis Informatics and Risk Communication
3 Data Collection and Analysis
3.1 2017 Atlantic Hurricane Season
3.2 Method
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Number of direct replies: All 281 tweets received at least one reply, with 108 also containing threaded replies (i.e., replies-to-replies within the conversation space).
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Corresponding 2017 hurricane(s): To determine this, we inspected the tweet text, date, and imagery. The majority of spaghetti plot forecasts were about Hurricane Irma (n = 162), followed by Maria (n = 53), Harvey (n = 25), Nate (n = 20), Jose (n = 18), and Lee (n = 2). (Some tweets pertained to more than one hurricane, so the sum is greater than 281.)
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Type of authoritative source who posted the tweet: Each authoritative account was exclusively categorized as Weather News (n = 57), Non-Weather News (n = 16), Weather Other (n = 2, a meteorology student and an independent meteorologist), and Weather Government (n = 1, NWS Houston).
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Media format of the spaghetti plot imagery: Tweets were categorized as containing still images (n = 274), video (n = 6), or animated gif (n = 1).
Participant (Twitter Handle) | Role & Organization | Region/Local Market |
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Eric Berger (@SpaceCityWx) | Independent meteorologist and journalist, Space City Weather | Houston, TX |
Greg Dee (@GregDeeWeather) | Meteorologist, WFTS-TV (ABC) | Tampa, FL |
Tim Heller (@HellerWeather) | Weather content consultant, Heller Weather (current); Chief meteorologist, KTRK-TV (during study timeframe) | Houston, TX |
John Morales (@JohnMoralesNBC6) | Chief meteorologist, WTVJ (NBC-6) | Miami, FL and Puerto Rico |
Brian Shields (@BrianWFTV) | Meteorologist, WFTV/ABC | Orlando, FL |
James Spann (@spann) | Chief meteorologist, WBMA-LD (ABC 33/40) | Birmingham, AL |
4 Findings
4.1 Building ‘Hurricane Literacy’
most people in hurricane prone areas are used to them and quite frankly they expect to see them and if they don’t find them from you they are going to go find it from somebody else.
4.2 Localizing Risk
4.3 Awareness of the Larger Region under Threat
I realize that people in South Texas—and I might have some followers down there, and I might have followers that live outside of my area, but there’s some meteorologists around the country who like to be everybody’s weatherman, and they want to post, you know, whatever the story is... I believe in serving my local community and so that is my focus.
In the television business we have a designated market area (DMAs) that define your market. My market…[has] like 23 counties. The digital world doesn’t stop at a county line. In the digital world you can reach anybody, so that is very appealing.
4.4 Communicative Responsibilities of Weather Authorities
4.4.1 The Responsibility of Interaction
…to be a part of that conversation...as a meteorologist, as a TV station, you’re part of a larger brand and I think there is a responsibility and a professional, almost, requirement to be in that conversation.
I do my best to look at everything and it takes time—you don’t even know how long it takes to look at this stuff.
I’ve got a hurricane that’s threatening South Florida…if I’ve got a threat to this market, then that means I’m on TV a lot and… carving out time for social media becomes challenging.
4.4.2 The Responsibility of Interpretation
otherwise I’m just regurgitating data for you and that’s not my job. My job as a meteorologist is to simulate the data the best I can and to figure out what my message is that I want to put out based on the data that I’ve looked at…
…because you’ll often read that even the [US National] Hurricane Center forecast discussion, ‘well we think this’ll happen but this could happen,’… and so just to kind of help people understand that it’s not obviously an exact science and so we explain to them that we think this could happen, but this could happen as well, and sometimes that works really well with the graphic.
Look at their handle… ‘weather stud.’ Now I can’t recall ever going into this account to figure out who this person is, but [this] person’s asking me a question that would probably not come from a layman.
You see them [spaghetti plots] everywhere—they’re on national newscasts, they’re on the cable networks, they’re on the local channels, they’re on all the social media feeds—but we have to be responsible when we [share them], and give some context to what they’re looking at.You can get the model plots anywhere, you can get them all over the place, but my job is to help you interpret what that data is actually showing.
4.4.3 The Responsibility of Maintaining Uncertainty
If it’s a weak disorganized tropical storm that’s going to be a flood threat, that line means absolutely nothing. You know you’ve really got to communicate, ‘this could create flooding 300 miles up the coast here, not that little dot.’ And everyone’s different, every hurricane’s different, every tropical storm is different, the impact is different.
When Harvey was going to make landfall…and you lived in Southeast Houston, we couldn’t say whether it was going to rain 10 in.…or 40 in. at your house—there’s enough uncertainty about the track and rainfall intensities that we just don’t know…so you need to be prepared for this scenario, but realize that something else may well happen. And it’s just part of like being real with people…not trying to be like uber hot shot forecaster who’s got it, ‘oh it’s going to be a hundred and twenty mile per hour storm going to hit Corpus Christi and go here…’ I mean it’s…look you know this could happen or this could happen.
I think when you see a bunch lines and you’re not sure which ones to look at you feel uncertain. I think it—almost the confusion that it generates is almost like the confusion you should feel when you try to forecast a hurricane.
They look for the worst case scenario from any map they can find even if they don’t understand it and they’re going to throw it out there, and those are the ones that get shared hundreds of thousands of times, and all the clicks and the likes and shares, and they learned the trick: the more outrageous the scenario, the better chance you’re going to get all the likes on Facebook.