How to reduce artificial boundaries in severe weather warnings

If you’ve been around here a while, you’ve seen me have opinions about the shapes of so-called “storm-based warnings”. Years ago, the National Weather Service changed the shape of tornado and severe thunderstorm warnings. Instead of issuing warnings based on the county, warnings are arbitrary polygons fitted to the threatened area. The idea is that by shaping warnings to the actual threat, the public gets a more accurate warning.

The reality is a little messier. Warnings are still frequently communicated to the public on a county basis. Worse, the warnings themselves are sometimes shaped to a county line. This is sometimes done to prevent a tiny sliver of a county to be included in a warning. Other times, it’s the result of a boundary between the responsibility areas of different NWS Forecast Offices.

Last week gave a great example close to home. The NWS office in Northern Indiana issued a tornado warning on the edge of their forecast area. Because the adjacent office didn’t issue a warning for that storm, the resulting shape was comically bad.

A tornado warning (red) shaped by the boundary (blue) between the IWX and IND forecast areas.

To be clear: I don’t blame the forecasters here. It was a judgment call to issue or not issue a warning. The real problem is that the artificial boundary does the public a disservice. Most of the general public probably does not know which NWS office serves them. Bureaucratic boundaries here only add confusion.

One solution is for the offices to coordinate when issuing warnings near the edge of their area. That doesn’t hold up well in the short time frame of severe weather, especially if an office is understaffed or over-weathered. Coordination takes time and minutes matter in these situations.

My solution is simpler: allow (and encourage) offices to extend warnings beyond their area. Pick a time frame (30 minutes seems reasonable) and allow the warning to extend as far into another office’s area as it needs to in order to contain the threat at that time. Once the threat is entirely into the new area, allow that office to update the warning as they see fit.

This allows offices to draw warnings based on the actual threat. It buys some time for additional coordination if needed, or at least gives a cleaner end to the warning. It does mean that some local officials will need to have a relationship with two NWS offices, but if they’re on the edge they should be doing that anyway.

The downside is that it increases the effort in verifying warnings because you can no longer assume which office issued the warning. And it could lead to some territorial issues between offices. But the status quo provides easier bureaucracy by putting the burden on the public. That’s not right.

Sidebar: what about issuing warnings at the national level?

Another solution would be for a national center to issue warnings. This is already the case for severe weather watches, after all. While it would solve the responsibility area problems, it would also reduce the overall quality of warnings. Local offices develop relationships with local officials, spotters, etc. These relationships help them evaluate incoming storm reports, tailor warnings to local conditions and events, etc. While a national-level warning operation would clearly provide some benefit, warning response is ultimately a very personal action that benefits from putting the warning issuance as close to the public as possible.

In defense of the call-to-action

Dr. Chuck Doswell, one of the most well-known and respected severe weather researchers, wrote on his personal blog:

Personally, I believe telling people what to do, say via “call to action” statements (CTAs) is not a good idea.  What people need to do depends on their specific situations, about which we as forecasters know nothing! 

The latter part of his statement is true, as are his assertion that people need to develop their plans well ahead of time. But I strongly disagree that call-to-action statements are not important. 

Dr. Doswell is thinking like someone who has devoted his life to severe weather for decades. That makes sense, but it is not a mindset shared by the general public. Fundamentally, he misunderstands the purpose of call-to-action statements: they’re not for teaching people what to do, they’re for reminding people what to do.

In the middle of an emergency, it’s very easy to forget what you know. That’s why people train for scenarios repeatedly – to have responses be reflexive, not cognitive. Call-to-action statements serve to remind people in an emergency of the general principles of severe weather safety. The education about those principles and specific implementations must be addressed ahead of time.

Measuring hurricanes and tornadoes

Today marks the beginning of the 2017 Atlantic hurricane season, which runs through the end of November. As you may be aware, we measure hurricane intensity by measuring the wind speed. We categorize hurricanes into one of five levels on the Saffir-Simpson scale. In use since 1971, the scale is widely known, but does it serve the public well?

The United States has not seen a landfall from a “major” (category 3 or above) hurricane since Hurricane Wilma in 2005. But that doesn’t tell the whole story. The original Saffir-Simpson scale included effects from storm surge and flooding. However, the Saffir-Simpson Hurricane Wind Scale in use today excludes those; it is solely a measure of wind speed. So even though the U.S. has avoided major hurricanes, it has not avoided major damage. Consider that two of the three costliest hurricanes in U.S. history were not major hurricanes. Sandy wasn’t even technically a hurricane.

More recently, Hurricane Matthew caused a great deal of devastation in the Carolinas and Virginia. Matthew could have caused massive damage along the Florida Atlantic coast, but remained just far enough out to sea. And the damage further north was primarily due to inland flooding, not the near-shore wind and surge. By the time Matthew reached the Carolinas, it was “just” a Category 1 storm. As a result, many in the public did not recognize the serious threat it posed.

The National Hurricane Center in particular, and the weather industry in general, are working to improve hazard communication. The public, after all, doesn’t really care about the wind speed per se, but the effects of that wind (and rain). Last fall, several meterologists on discussed this on Twitter:

The discussion turned to the idea of real-time rating of tornadoes. NOAA researchers found that weather radar velocity data can be used to estimate the ultimate Enhanced Fujita Scale rating of a tornado. While not operational yet, it will be a big benefit to the public if it is further developed.

The ideal situation would combine the impact focus of the EF scale with the real-time rating used for hurricanes. Hurricanes are much easier to evaluate in real time for a variety of reasons, so they have a head start. Now if we can just start measuring hurricanes correctly.

3-4 week forecasts are coming, or not

With a few exceptions, most operational public forecasts only go out to 7-10 days. This is due in large part to the fact that the skill of models gets progressively worse the further out you go. However, a recent article in the Journal of Climate suggests that models may have sufficient skill for forecasts in the 3-4 week range.

I have not read the full paper (yet), but the abstract suggests that this isn’t as great as it might sound at first. The resolution, according to the abstract, is one degree of latitude at 38 degrees. By my calculations, that’s a grid spacing of about 69 miles or 111 kilometers. The grid used by the Global Forecast System (GFS) model is 28 kilometers. Shorter-range and regional models use even smaller grids.

Grid size matters because it effects the scale of weather phenomena that can be modeled. Larger-scale features such as low pressure systems can be captured at this scale, but much gets lost. So you wouldn’t use this forecast to schedule the exact time of your picnic four weeks from now, but it might at least help you pin down a day.

Of course, this is all dependent on the NOAA budget. The Weather Research and Forecasting Innovation Act of 2017, which was passed by Congress and signed by the President, requires additional efforts to improve forecasts at this range. However, the proposed budget from the White House cuts this effort.

Terminate Investment in Mid-Range Weather Outlooks: NOAA requests a decrease of $5,000,000 to terminate all development, testing, and implementation of experimental products to extend operational weather outlooks, including temperature and precipitation outlooks, from 16 days to 30 days.”

The Washington Post‘s Capital Weather Gang blog and Professor Cliff Mass both have excellent writeups on the misalignment between the Weather Research and Forecasting Innovation Act and the proposed budget. The Weather Research and Forecasting Innovation Act of 2017 was a good bill, hopefully the budget adjusts to meet it. If not, the state of American weather forecasting is set to take a dramatic hit.

Motivations for storm chasing

Maybe I’m not the right person to write this post. Or maybe I would have been had I written it during a time when I was active. (It’s almost six years since the last time I went storm chasing, how much longer can I pretend that it’s a thing I do?) But here on Blog Fiasco, I get to make the rules, and Rule #1 is “Ben gets to write about whatever the hell he feels like writing about.”

At any rate, it seems that storm chasers have one thing in common: we/they really like to criticize the motivations of others. The most common target are the chasers who get in extremely close in order to get the perfect shot for TV. They take risks that most of us won’t (whether or not those risks are justified are left as an exercise for the reader). As a result, they’re dismissed as merely thrill-seekers by the “serious” chasers.

He’s in it for the money, not the science.

As my friend Amos said, “there’s no single explanation for chasing. It’s like trying to count all the reasons tourists visit Paris.” “Serious” chasers like to think they’re doing it for some altruistic reason. That could be scientific research, warning the public, or whatever. These things definitely happen, and they’re very good reasons for participating in an activity, but I doubt it’s what primarily motivates people.

Warning can be done by stationary (or nearly stationary) spotting, which also probably means you’ve developed some kind of relationship with the local authorities or NWS office. Some kinds of scientific research can only happen in situ, but that also requires a degree of discipline that many don’t want. Storm chasing is a very boring hobby that involves sitting on your butt in a car for hours on end in the hopes of seeing something interesting. It takes more than a sense of civic duty for most people.

I used to think I was doing it as a learning exercise or in order to serve the public. At some point I realized I was kidding myself. I chased (and hope to chase again) because I enjoy the thrill of the hunt. Can I figure out what the atmosphere is doing? Can I stay ahead of a dangerous beast while keeping myself safe? I’ll absolutely report severe weather I see, and I’ll share pictures with the NWS and any researchers, but that’s not the primary motivation. Now to get myself back out there…

Climatune: correlating weather and Spotify playlists

I don’t remember how I stumbled upon it, but I recently discovered Climatune, a joint effort between Spotify and Accuweather that presents a playlist to match the current weather. According to Accuweather’s blog post on the topic, the playlists were developed based on an analysis of 85 million streams across 900 cities. This is an incredibly interesting project, even if the Lafayette playlists don’t seem to vary much.

What I like about projects such as Climatune is the reminder that we are still animals affected by our surroundings. When I worked at McDonald’s, we noticed anecdotally that sales of the Filet-o-Fish apparently increased on rainy days. I regret that I never ran daily sales reports to test this observation. I suppose in either case, there was an effect. Either customers ordered more, or we were more aware of the sales.

Correlating weather with other data is hardly a new concept. The most common example is probably comparing Chicago crime reports to the temperature. But researchers have investigated mood-weather correlation from Twitter posts. Several studies have examined the effects of weather on the stock market.

These sorts of studies can be hard, since it’s hard to control for all of the possible factors. But even if we can’t draw statistically sound conclusions, it’s fun to look at the connections. And if weather isn’t your thing, Spotify also has a site that makes a custom playlist that fits the cook time of your Thanksgiving turkey.

A quick look at the Weather Research and Forecasting Innovation Act

When I first heard that Congress had passed a bill it called the “Weather Research and Forecasting Innovation Act“, I was a little concerned. The majority has a history of being hostile to science (and one former senator was hostile to the National Weather Service in particular), and the title of the bill just screams “legislative doublespeak”. But I’ve read skimmed the bill and there doesn’t seem to be much to object to.

Much of the bill is of a “duh, we’re already working on that” nature; it requires the National Weather Service to conduct research to improve forecasts and warnings. One think I liked is that it specifically called out communication of forecasts and warnings as an area of improvement. It requires the current system to be examined, with necessary changes made where the current system is unsatisfactory. The current watch/warning/advisory system leaves a lot to be desired.

This bill is probably most notable for what it doesn’t say. On the positive side, it does not proscribe specific metrics (e.g. “the average lead time for a tornado warning must be 60 minutes”). It seems clear that meteorological experts were consulted for this bill. 

On the other hand, the only time “climate” is mentioned is when the full name of the COSMIC satellite program is given. There’s nothing in the bill that specifically precludes the NWS from conducting research related to climate change, but I couldn’t help reading into  the stated focus or short-term and seasonal weather. Legislation isn’t written in a vacuum, and the plain fact is that the current administration and Congress aren’t big supporters of climate change research or mitigation.

The main area of concern for me is the budget. A few programs have specific dollar amounts assigned, but it’s not clear to me if those are new appropriations or a directive to use the existing budget to that purpose. Certainly the main budget will have an impact on how this bill, should it be signed, is implemented. Given the initial reports about the Trump administration’s first budget, I remain solidly pessimistic. But returning to the provisions of this specific bill, it requires a lot of reporting, much of which appears to be new. The reporting, and even the substantive efforts, could end up being an unfunded mandate.

I can’t predict what the outcome of this bill will be. It got bipartisan support and I haven’t heard of any major complaints from my friends within the Weather Service. That in itself is encouraging; it should at least do no harm. If backed with sufficient funding, this may lead to improved forecasts for a variety of weather hazards. This, of course, is the stated mission of the National Weather Service: to protect life and property.

The most dangerous part of storm chasing is the road

People who have never gone storm chasing don’t always believe me when I say it’s a very boring hobby. Hours of driving can lead to…steady rain. Or blue skies. Or any number of outcomes that were probably not worth the time and money invested. They see movies or “reality” TV shows and assume it’s constantly a dangerous, edge-of-your-seat thrill ride.

Well it is dangerous, as we were reminded last week. Three chasers were killed in an automobile accident after one driver apparently ran a stop sign and hit another vehicle. I had never heard of the driver in question, so I can’t begin to speculate about his approach. Chances are he was a safe and conscientious driver most of the time. But it only takes one time. In that picture I saw that purports to be the fatal intersection, the stop sign is several feet away from the road. It’s easy to miss if something else grabs your attention.

Any chaser without a “close call” story is full of it (or just has a really bad memory). Distracted driving is dangerous, and chasing — especially near the storm — is an exercise in distracted driving. Maps, radar, radios, storm structure, cameras. All of these things compete for attention, but still you have to watch the road. Even with someone in the passenger seat, it can be hard to focus on the task at hand.

Truth be told, I’m surprised there haven’t been more deaths due to road accidents. The tornado isn’t the dangerous part.

January was warm, but in a weird way

Did last month feel warm to you? January was unusually warm in central Indiana, but in a subtle way. The highest temperature recorded at the Purdue University Airport (KLAF) was 63°F — just four degrees warmer than last year. And the lowest temperature — -4°F — was a degree colder than last year. But what if last year was warm, too?

Let’s compare January 2017 to the climate normals (1981-2010). The average daily high was 5.4 degrees above normal, and the average daily low was 5.9 degrees above normal. The average temperature for the month was 5.6 degrees above normal. That’s noticeably warm, but not necessarily outrageous.

What makes January 2017 stand out is the extended stretch of warmer temperatures. More specifically, how it just wasn’t freezing for much of the month. January 2017 was right on target for number of days with a low below zero (3), but it had five fewer days than normal with a low below 32°F. In fact, Lafayette set a new record for consecutive hours in January with a temperature above freezing.

Chart of 1995 and 2017 above-freezing streaks.

Streaks of January hourly temperatures at or above freezing at KLAF. Chart from Iowa Environmental Mesonet.

Since records began at KLAF in 1972, only two years have had a 10-day or longer above freezing streak. In 1995, we had a 10-day streak. This year it was 10.9 days. It just didn’t get cold for the second half of the month. Four of the last 10 Januaries had at least 5 days with a high temperature above 50°F. Four of the last 10 years also had at least one January day above 60°F. This year’s 2 is bested by 3 in 2008.

So if you were thinking to yourself “wow, January was really warm” but the high temperatures didn’t look all that off, rest assured that it was. It just forgot to January.

Continue reading

Weather forecast accuracy is improving

ForecastWatch recently issued a report on the accuracy of weather forecasts from 2010 through June 2016 (PDF here). While many readers will focus on who was more accurate, what stood out to me was how forecast accuracy has improved. Meteorologists have long “enjoyed” a reputation for inaccuracy — often more due to perception than fact. But those in the know are aware that skill is increasing.

Forecast accuracy over time

ForecastWatch’s U.S. analysis shows a clear — if small — improvement in the average accuracy since 2010.

Average U.S. forecast accuracy from 2010 – June 2016.

The chart above shows the average for all of the forecast sources Forecast Watch analyzed. To be frank, World Weather Online is a stinker, and brings the averages down by a considerable margin. Examining the best and worst forecast shows more interesting results.

Best and worst U.S. forecast accuracy from 2010 – June 2016.

Forecasts get less skillful over time, thanks to subtle inaccuracies in the initial conditions (see also: butterfly effect). That’s obvious in both graphs. What this second chart shows is that the best 6-9 forecast is now roughly as skillful as the worst 3-5 forecast was in 2010. And the best 3-5 day forecast is in the middle of the 1-3 day forecast skill from just a few years ago.

Forecasts are definitely improving. This is due in part to better modeling — both more powerful computers and also the ability to ingest more data. Research and improved tooling helps as well.

Forecasts still bust, of course, and forecasters hate bad forecasts as much as the public does. As I write this, forecasters in North Carolina are dealing with an inaccurate snow forecast (winter weather forecasting sucks due to reasons I explained in a previous post). Missed forecasts can cost money and lives, so it’s good to see a trend of improvement.

Forecast accuracy in your city

The ForecastWatch report breaks down by broad regions: United States, Europe, and Asia/Pacific. But weather is variable on much smaller scales. The ForecastAdvisor tool compares forecasts at the local level giving you the ability to see who does the best for your city. As of early January 2017, AccuWeather had the most accurate forecasts for Lafayette, Indiana, but they only place fourth when considering the past year.