2013 severe weather watches

Greg Carbin, Warning Coordination Meteorologist at the Storm Prediction Center, recently updated his website to include maps of 2013 severe thunderstorm and tornado watches. I always like looking at these, because they highlight areas of increased and diminished severe weather threat. It’s important to not read too much into them though. As with hurricanes, it’s not always the frequency of events that makes a year memorable. 2013 was a below- or near-normal year for watches in the areas of Illinois and Indiana that were hit by a major tornado outbreak on November 17.

Tornado (left) and severe thunderstorm (right) watch count (top) and difference from 20 year average (bottom) by county. Maps are by the NOAA Storm Prediction Center and in the public domain.

Speaking of hurricanes, the quietness of the 2013 Atlantic hurricane season is evident in the below-average tornado watch count along the entire Gulf coast. Landfalling hurricanes are a major source of tornado watches for coastal states, so an anomaly in watches is often reflective of an anomaly in tropical activity. Preliminary tornado counts for 2013 are the lowest (detrended) on record. It’s not surprising, then, that the combined severe thunderstorm and tornado watch counts are generally below normal.

Severe weather watches (left) and departure from normal (right) by county. Maps are by the NOAA Storm Prediction Center and are in the public domain.

As you’d expect, Oklahoma and Kansas had the largest number of watches. What’s really interesting about the above map is the anomalously large number of watches in western South Dakota, western Montana, and Maine. Indeed, western South Dakota counties are comparable to Kansas in terms of raw watch count. Of course, that doesn’t mean the watches verified, but it’s an interesting note. Looking back through past years, the last 4 years have been anomalously high in western South Dakota. Is this an indication of a population increase, forecaster bias, or a change in severe weather climatology?

Online learning: Codecademy

Last week, faced with a bit of a lull at work and a coming need to do some Python development, I decided to work through the Python lessons on Codecademy. Codecademy is a website that provides free instruction on a variety of programming languages by means of small interactive example exercises.

I had been intending to learn Python for several years. In the past few weeks, I’ve picked up bits and pieces by reading and bugfixing a project at work, but it was hardly enough to claim knowledge of the language.

Much like the “… for Dummies” books, the lessons were humorously written, simple, and practical. Unlike a book, the interactive nature provides immediate feedback and a platform for experimentation. The built-in Q&A forum allows learners to help each other. This was particularly helpful on a few of the exercises where the system itself was buggy.

The content suffered from the issue that plagues any introductory instruction: finding the right balance between too easy and too hard. Many of the exercises were obvious from previous experience. By and large, the content was well-paced and at a reasonable level. The big disappointment for me was the absence of explanation and best practices. I often found myself wondering if the way I solved the problem was the right way.

Still, I was able to apply my newly acquired knowledge right away. I now know enough to be able to understand discussion of best practices and I’ll be able to hone my skills through practices. That makes it worth the time I invested in it. Later on, I’ll work my way through the Ruby (to better work with our Chef cookbooks) and PHP (to do more with dynamic content on this site) modules.

Feeling stupid at work

This post is inspired in large part by my friend Ed Finkler’s Open Sourcing Mental Illness campaign.

In my new job, I’m faced with a lot of deep, technical challenges. Sometimes they’re of a nature I haven’t seen before. When they come quickly, it gets pretty easy to feel down. When I’m physically sick, it gets even worse. And it compounds. There are days that I feel downright stupid and completely unqualified for my job.

Then there are days when I solve a problem well, master a new skill, or otherwise validate my professional existence. Those days feel pretty awesome. I like having those days.

In the past few months I’ve had many of both of those days. Lately, they’ve trended toward the good instead of the bad, but I don’t take that to be a sign of a permanent state. I’ve said before — and I honestly mean — that if you never feel stupid in your job then you’re not in the right job. The important thing is to try to minimize and recover quickly from the stupid days.

It helps to know that it’s okay to feel stupid. That’s part of the reason why I’m writing this. I’ve found that sharing my frustration with a trusted coworker who can provide meaningful encouragement helps the recovery process. It also helps to remind yourself of why you’re awesome. Reading Chris Hadfield’s book helped me a lot, too. Sure, I’ll never be an astronaut, but I still know how to work through a problem. I can solve smaller pieces until the larger problem is fixed, and I can bring myself to ask for help when needed. That’s enough to make me as successful as I need to be.

Never believe year-long forecasts

On my to-do list, this post is titled “Chad Evans, you son of a bitch.” Though the specifics are about the failings of a specific local TV meteorologist, the broader lesson is that weather forecasts longer than about a week aren’t worth the time it takes to make or read them. AccuWeather’s 45-day forecasts have caught some flack for being awful, as everyone expected they would be. Less attention has been paid to verifying the long-range forecasts from WLFI meteorologist Chad Evans.

I decided to take a look at the September 2011 forecast to see how it fared (there’s probably a forecast from September 2012, but I’m too lazy to search for it). As the graphs below show, it’s hard to beat climatology for long-range forecasts. Interestingly, there’s not a noticeable drop in skill over time with temperatures. The precipitation forecast does seem to get worse over the life of the forecast, with the exception of a lucky break in the summer.

Forecast and climatology monthly average temperatures.

Forecast and climatology monthly average temperature errors.

 

Forecast and climatology precipitation total errors.

Forecast and climatology precipitation total errors.

Mr. Evans was smart enough not to include day-by-day specifics, except for Christmas. This year, he claimed  claimed to be 4-0 on his white Christmas forecasts. The forecast called for 1″ or more of snow on Christmas morning. Unfortunately, there was none. Several inches fell the week before, but warm and rainy weather the weekend prior took care of that. Speaking of snowfall, 10″ was forecast for January 2014. In six days, we’ve already passed that, and the snow continues to fall as I write.

In the first two months of the most recent annual forecast, the temperature errors aren’t awful, but the precip forecasts miss the mark pretty hard (though the direction of the error was right in both cases). As the year progresses, you’d expect to see the skill diminish.

Nov Dec
Tmax 9 1
Tmin 5 8
Tavg 3.9 2.1
Precip .73″ (25%) .95″ (38%)
Forecast absolute error

And that’s really the point here: seasonal (or longer) outlooks are really bad at giving specific information. You can sometimes make use of them for trends, but even then they’re not very reliable. I can’t fault a forecaster for busting a forecast, I’ve had plenty of busts. But presenting skill-less forecasts to the public is a disservice to the public and to the reputation of the meteorology profession.