With winter winding down, communications-strategy-rethinking season
begins. As surely as the snowflakes melt in the spring, there is a storm
to have angst over. This time, of course, it’s the blizzard that
wasn’t… in the big cities.
New Jersey Governor Christie has the answer: Blame the meteorologists! As if we could somehow suddenly conjure up better science. You would think that someone who has lived in New Jersey as long as the governor would understand the rain-snow line dilemma. If an unforecastable difference in the storm track means somewhere between debilitating and nuisance, you’d better err on the side of shutting things down. Unless, of course, you don’t mind the New Jersey Turnpike looking like the Long Island Expressway did in 2013. Thousands of stranded people in stuck cars and trucks, and a cleanup nightmare that took days to resolve.
On the other hand, the National Weather Service has confidence in their science, but can’t decide on the best approach to putting up warnings. Should they be aggressive? Should they wait until the last minute? It’s a conundrum in search of a plan.
It seems to me that the National Weather Service is in a no-win box as long as they stick with the current paradigm of putting up warnings if the forecast weather meets certain thresholds. If X amount of snow is going to fall in Y amount of time with Z wind and T temperature, we put up a Blizzard Warning. No amount of social-science research is going to better inform the decision of when or whether to put up the warning. It comes down to the confidence of the forecaster that the weather will meet the threshold, with consideration of the amount of time left until the event starts. Nobody wants drivers or trains trapped in a blizzard. Obviously in the case of Winter Storm Stella earlier this month, the NWS forecasters had sufficient confidence and concern for the potential consequences to pull the trigger on a Blizzard Warning for New York and Boston.
The onus is on the forecasters. Be sure your confidence, concern, and crystal ball are working perfectly, or you’ll hear from the governor. But there’s another way. Since confidence underlies the process, why not let confidence set the thresholds? Call it Plan CCC for Critical Confidence Calculation.
National Weather Service websites, the Capital Weather Gang, and others already publish boom or bust odds. The Mount Holly, New Jersey local National Weather Service Forecast Office has a page with the odds of every imaginable possible snow or ice accumulation the storm might produce. The odds for wind and temperature thresholds could easily be added, if they had a mind to. They could easily overwhelm us with enough stats that we couldn’t possibly have any confidence in the forecast. It’s approaching that now.
So why not generate the odds that the weather will reach the Winter Storm Warning threshold, or the Blizzard Warning threshold, or the threshold for anything you want. Just tell the computer and voila, you’ve got a map with contours that show where there’s a 10%, 20%, etc. chance of the Blizzard Warning being required.
Get buy-in in advance from everybody involved, including the loudmouth politicians, on what the odds should be when the trigger is pulled, and the NWS is largely out of the blame game. If the governor is happy to shut down the turnpike if there is at least a one-in-five chance of thousands of his residents getting stranded, set the threshold at 20%. It’s a simple risk-tolerance exercise puts decision-makers’ skin in the game.
Even if the NWS can’t get a consensus, at least the numbers would be public. Between the social scientists and inclusive outreach, it would be doable.
In practice, of course, it is not quite this simple. What happens if the forecaster believes the models are underplaying or overplaying the risk? How are close calls handled? If the threshold is X and the X line is dancing around New York City, what’s the move? But at least this system starts with a baseline risk that key players are involved in setting. With the warning underpinned by a risk they agreed on, when the time runs out, issue the warning.
The communications from the politicians would change as well. Now we hear, “The National Weather Service says we’re getting two feet of snow so we’re shutting down the city.” The new paradigm would result in something like, “The storm threat has reached the level where we can’t take a chance on people being stranded and infrastructure being damaged, so were closing down.”
The bottom line: use the science to get the monkey off the forecasters’ backs.
New Jersey Governor Christie has the answer: Blame the meteorologists! As if we could somehow suddenly conjure up better science. You would think that someone who has lived in New Jersey as long as the governor would understand the rain-snow line dilemma. If an unforecastable difference in the storm track means somewhere between debilitating and nuisance, you’d better err on the side of shutting things down. Unless, of course, you don’t mind the New Jersey Turnpike looking like the Long Island Expressway did in 2013. Thousands of stranded people in stuck cars and trucks, and a cleanup nightmare that took days to resolve.
On the other hand, the National Weather Service has confidence in their science, but can’t decide on the best approach to putting up warnings. Should they be aggressive? Should they wait until the last minute? It’s a conundrum in search of a plan.
It seems to me that the National Weather Service is in a no-win box as long as they stick with the current paradigm of putting up warnings if the forecast weather meets certain thresholds. If X amount of snow is going to fall in Y amount of time with Z wind and T temperature, we put up a Blizzard Warning. No amount of social-science research is going to better inform the decision of when or whether to put up the warning. It comes down to the confidence of the forecaster that the weather will meet the threshold, with consideration of the amount of time left until the event starts. Nobody wants drivers or trains trapped in a blizzard. Obviously in the case of Winter Storm Stella earlier this month, the NWS forecasters had sufficient confidence and concern for the potential consequences to pull the trigger on a Blizzard Warning for New York and Boston.
The onus is on the forecasters. Be sure your confidence, concern, and crystal ball are working perfectly, or you’ll hear from the governor. But there’s another way. Since confidence underlies the process, why not let confidence set the thresholds? Call it Plan CCC for Critical Confidence Calculation.
National Weather Service websites, the Capital Weather Gang, and others already publish boom or bust odds. The Mount Holly, New Jersey local National Weather Service Forecast Office has a page with the odds of every imaginable possible snow or ice accumulation the storm might produce. The odds for wind and temperature thresholds could easily be added, if they had a mind to. They could easily overwhelm us with enough stats that we couldn’t possibly have any confidence in the forecast. It’s approaching that now.
So why not generate the odds that the weather will reach the Winter Storm Warning threshold, or the Blizzard Warning threshold, or the threshold for anything you want. Just tell the computer and voila, you’ve got a map with contours that show where there’s a 10%, 20%, etc. chance of the Blizzard Warning being required.
Get buy-in in advance from everybody involved, including the loudmouth politicians, on what the odds should be when the trigger is pulled, and the NWS is largely out of the blame game. If the governor is happy to shut down the turnpike if there is at least a one-in-five chance of thousands of his residents getting stranded, set the threshold at 20%. It’s a simple risk-tolerance exercise puts decision-makers’ skin in the game.
Even if the NWS can’t get a consensus, at least the numbers would be public. Between the social scientists and inclusive outreach, it would be doable.
In practice, of course, it is not quite this simple. What happens if the forecaster believes the models are underplaying or overplaying the risk? How are close calls handled? If the threshold is X and the X line is dancing around New York City, what’s the move? But at least this system starts with a baseline risk that key players are involved in setting. With the warning underpinned by a risk they agreed on, when the time runs out, issue the warning.
The communications from the politicians would change as well. Now we hear, “The National Weather Service says we’re getting two feet of snow so we’re shutting down the city.” The new paradigm would result in something like, “The storm threat has reached the level where we can’t take a chance on people being stranded and infrastructure being damaged, so were closing down.”
The bottom line: use the science to get the monkey off the forecasters’ backs.
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