Preparing for winter weather in advance of winter
This video ("Montreal buses, cars, police cruiser and even salt truck slide down hill," CBC-TV) showing the consequences of an icy road in Montreal reminded me it's time for my annual pre-winter post on snow.
Location: Côte du Beaver Hall toward Viger Street, Montreal
I have a bunch of posts over the years about how cities should adopt a more general "maintenance of way" agenda appropriate for urban areas, with special attention to walking, biking, and transit, since those modes dominate, although most systems preference motor vehicle traffic both in terms of snow clearance and lighting. The basic point is that cities where a majority of trips are conducted not by automobiles need to better prioritize these modes in terms of maintenance generally and winter snow clearance specifically. One of the things that always comes up is snow clearance on trails, especially trails on park land.
In particular it means how we do snow clearance, maintenance of bike lanes, and night time lighting of sidewalks and walking paths. DC is committed to snow clearance on its trails. This is less true of the suburban jurisdictions.
-- "A "maintenance of way" agenda for the walking and transit city," 2010
-- "Snow reminds us of the necessity of a "maintenance of way" agenda," 2013
-- "Testimony on the Winter Sidewalk Safety Amendment Act of 2011," 2011
-- "Level of service and maintenance requirements in planning #2: winter maintenance of bike paths," 2012
-- "Night-time safety: rethinking lighting in the context of a walking community," 2014
-- "Planning for Winter Weather," 2015
-- "Who knew?: there is a Winter Cycling Federation and annual conference," 2015 -- the upcoming Congress is in Montreal, February 8th-10th, 2017
Better practice through better weather data? Last week, Dr. Gridlock ran a story, "Highway departments preview their snow cleanup plans," discussing the late in the day snows last year and over recent years that crippled the evening commute, extending it by many hours. From the article:
Among commuters, there are dates that live in infamy. One of them is Jan. 20, 2016.It reminded me of a recent article in the New York Times Magazine, "Why Isn't the U.S. Better at Predicting Extreme Weather?," which discusses how the National Weather Service/NOAA is so underfunded by Congress that it can't afford to buy the best software or the computers necessary to run the models.
The highway departments in the D.C. region know that date, too, because many of their road crews shared the experience with the commuters. When highway officials talk about winter storm preparations, they use “Jan. 20” as shorthand for a certain set of inputs and outcomes they know everyone in their circle will react to with a shudder.
For those who have the good fortune to be mere casual observers of the daily commute, this wasn’t the date of last winter’s major event, the blizzard, which swept into the region a few days later. Jan. 20 was the day of the dusting, when the afternoon commute was crushed by a light coating of snow.
One of the points made by the person featured in the article is that the "micro-areas" that the NWS provides weather forecasts for are too big, and it is possible, with the right software and computing power, to provide much better forecasts for smaller areas than is typical currently. From the article:
While Mass is the most outspoken on the subject, many experts insist that if the Weather Service wants to meaningfully improve its predictions, it must employ a technique called ensemble forecasting. The basic premise is either to tweak the physics equations or to make repeated changes to a model’s variables: You might bump up the temperature slightly, for example, and then run the model again. After a half-dozen or so reruns, you get a set, or “ensemble,” of forecasts that can be compared with one another. When all the forecasts in an ensemble agree, it’s a reasonably sure bet that the predictions will pan out.Reading Dr. Gridlock, I was thinking that the first thing that the MWCOG TPB should do is working with all the relevant transportation authorities, contract independently of the NWA for such detailed weather forecasting.
Nobody I’ve spoken to doubts the superiority of ensembles. Yet they haven’t been widely adopted in the United States at the resolution required to forecast localized, or “mesoscale,” events — specifically, thunderstorms, flash floods and tornadoes — because high-resolution ensembles require more computing power than the National Weather Service can currently provide. Higher-resolution ensembles translate to greater accuracy in the same way that HDTVs are clearer than analog sets. I met with a scientist at the National Center for Atmospheric Research in Boulder who showed me a prototype mesoscale ensemble for the United States. But at the moment, he can’t exploit its full potential because the supercomputing cluster at the Weather Service simply couldn’t handle the load. ...
In 1995, Mass founded the Northwest Regional Modeling Consortium in Seattle to create better local forecasts. At the time, the National Weather Service used models with resolutions of 80 kilometers, or about 50 square miles. In Washington State, a sample that large might encompass topography ranging from 10,000-foot-high snowcapped volcanoes all the way down to the Pacific Ocean; the resulting forecast, forced to produce sweeping overgeneralizations, renders itself essentially useless. “My first test was at 27-kilometer resolution, and then I started doing it higher and I found something absolutely magical,” Mass says. “We could actually forecast the local weather.”
Jeff Renner, the chief meteorologist for Seattle’s NBC television affiliate KING 5, who retired in April, told me, “It really allows us to see some beautiful detail.” KING pays more than $20,000 a year to buy model data from the consortium, which it incorporates into its on-air forecasts. “[Mass] was doing a much better job than the Weather Service, where you were basically getting vanilla ice cream,” Renner says. With the consortium, “it was like walking into Ben & Jerry’s.”
It could help reduce the kinds of forecasting problems that contribute to inadequate preparation for snow events, which then lead to major commute snafus.