PODCASTS   ABOUT PODCASTS   ABOUT RSS

Research in NOAA

Interview with David Stensrud
November 8, 2007

The following may contain unintelligible or misunderstood words due to the recording quality.)

BARRY REICHENBAUGH: This is Barry Reichenbaugh with the NOAA Research Communications Office, and I'm in Norman, Oklahoma, at the National Severe Storms Laboratory with Dave Stensrud. Dave, can you tell me what it is you do here?

DAVID STENSRUD: Technically I'm a research meteorologist.  My main focus of my research is to improve forecasts of severe weather events, including things like damaging winds, perhaps hail, tornados, even winter weather, in terms of blizzards or freezing rain events.

BARRY REICHENBAUGH: Well, let's build on that a little bit. My first question has to do with the types of research done here to improve forecasts and warnings. Can you talk a little bit about that?

DAVID STENSRUD: Yes. Forecasts -- perhaps you could say they're fundamental tool that we use for producing forecasts of severe weather, is what's called a numerical model. In its most basic essence, a numerical model is just a computer program, but it happens to be a very sophisticated computer program because it has equations in it that let us know how the fluid -- because the atmosphere is a fluid -- evolves over time.

But those equations have parts that we have to approximate.  And approximations introduce errors. And so one of our tools is basically trying to find out how best to use these imperfect models to give better and better forecasts for severe weather events.

BARRY REICHENBAUGH: Could you describe NSSL's work regarding ensemble modeling, and what is ensemble modeling?

DAVID STENSRUD: Ensembles are just a group of forecasts created by a computer that are valid over the same time period. Ensembles are very helpful because we've known for about 40 years now, in particular, that the atmosphere is very sensitive to errors in our initial picture of the atmosphere. So to start a model up, you have to actually have a picture of what the atmosphere looks like right now.

And so we, you know, launch weather balloons at Weather Service offices across the country. We have satellite data. Other countries also launch balloons and we have surface observations. Even when you fly back home, your aircraft may actually have instruments on it that'll sample the temperature and moisture of the atmosphere.

And so first thing we do is collect all these very diverse and different data sets and try to mold them into a picture of what the atmosphere is like at this moment in time. But that picture's imperfect, and those imperfections lead to basically forecasts being incorrect at some future point in time.

And so an ensemble will take our initial picture of the atmosphere and will sort of adjust it, or you could say shake it a bit.  We know it's an error by a certain amount on average, and so we can adjust those conditions and get a variety of different initial pictures that all are pretty close to what we think is true.

And from each one then we can make a forecast using a model. And that gives you more of a probabilistic approach to actually looking at what's going to happen for tomorrow.

BARRY REICHENBAUGH: Can you go a little bit into what you mean by a probabilistic approach? I understand you're talking about ranges here, but for the benefit of the nonscientist in terms of probabilities, what are you getting at with that?

DAVID STENSRUD: Well, you can look at it perhaps from a gambling perspective, where you know the odds may be of -- if you're holding two tens and you're playing blackjack, you might know the odds of the dealer actually getting 21, for example. And so probabilistic forecasts are dealing with the odds of different events happening.

I think we're most familiar with precipitations forecasts being done on probabilities. 20 percent chance of rain for tomorrow.  But there also might be a 20 percent chance that tomorrow's weather is going to be sunny and highs may be in the 70s, and there's an 80 percent chance that it's going to be cloudy and maybe highs only reach the low 60s.

And of course if you forecast like we do nowadays on TV, you might get, The high tomorrow is going to be 60.  But you're not telling people it also might be very nice and it might be 70 degrees. And so we're trying to give the full information, everything that we know, because then people can make decisions.

It's always more helpful if you know the odds of something happening. And in particular, for example, if you're a power company in Phoenix, Arizona, and it's summertime, and so a demand for power for air conditioning is pretty high. And maybe on a given day there's a 50-50 chance of it being 110 versus 95. And that's a big difference, then, in power demand for air conditioning, those two temperature forecasts.  But if they know the odds of each one happening, they can make a decision how to save money, in essence, depending on betting on the odds.

BARRY REICHENBAUGH: The National Severe Storms Lab has long been known for radar research. And I'm wondering if you could talk a little bit about how radar data may be used in these models that are created.

DAVID STENSRUD: Yes. NSSL has a long history in looking at radar data from a perspective of pretty much real-time operations and making warning decisions. So you look at the radar data. You diagnose it. You look for various patterns and then you can tell, based on what the radar tells you, if you have severe thunderstorms. And then you can make a persistence forecast, which is that this storm will last a while. And so based on track, you can predict what town it'll go over in the next 20 minutes or so.

What we'd like to do is be able to make a bit more a short-range forecast. So if we can get the radar data into the model, then we can actually use the model then to not only just look at what the storm is right now, but how it might change over the next 20 or so minutes. And by doing that, we actually might extend lead times for warnings for tornados or hail or damaging winds, because we not only take a persistence view of it, we can also then put it in the model and hopefully the model will give us information to improve the warning.

BARRY REICHENBAUGH: Can you describe briefly what this Hazardous Weather Testbed is and then how has that been helping NSL's overall research efforts?

DAVID STENSRUD: The Hazardous Weather Testbed is a collaboration between all the NOAA units in Norman. It's designed to test out new forecasting and warning techniques in a real-time environment, so we actually have forecasters participating in this, but it's not actually in their operations yet.

And so it's a way to introduce these new ideas to forecasters and get their impressions on how useful it is, how valuable it is, and how excited they are about this technology and what it might do to make their job hopefully easier, but also produce better forecasts and warnings.

And so what we do is every year we get together with various forecasters and we look at some of the new ideas that are out there and decide on a few to bring in and then test. And then each spring, in a six- to eight-week period, each week we bring in people from across the United States. They can be from different forecast offices. They could also be from various universities or from other national centers.  And we bring them in and we sit them down and we go through and start using these new tools and have input into how they're doing and try to then use them actually as they were intended.

So if they're a forecast tool, we'll go ahead and make a forecast in the morning based upon what the tool is telling us. And then we'll evaluate it the next day to see how well it did in actually helping to produce a good forecast. And through this eight-week period then you get a whole range of opinions, new ideas that come in, and it's actually been so well received that we're actually getting international visitors as well. We've had forecasters from Canada and from the United Kingdom in the last couple years that have participated regularly.

BARRY REICHENBAUGH: Let's get big picture. Can you talk to me a little bit about the ultimate goal of NSL's forecast and warning research here?

DAVID STENSRUD: Our ultimate goal is to improve the forecasts of severe weather for the public. And part of that involves improving these tools that we have, like numerical models. Part of it involves ways to actually take model data, because models provide a load of data, a huge amount of data. And nowadays it's harder and harder for forecasters to digest all of the data. So you have to find smart ways to pull out the important pieces of information and then let the rest of it just fall to the floor because you just can't handle all of it.

And we would love to be able to make day-two or day-three predictions, so predictions for tomorrow and the day after as accurate as we do predictions for today. And we always know that they're going to be imperfect because that's just the way the atmosphere is. It's a very hard system to predict with any degree of perfection. But I think we can get better and better as time goes by.

BARRY REICHENBAUGH: What got you into this field? Did you have an early interest in science in your life, or can you talk to that a little bit?

DAVID STENSRUD: I've always loved science. As far back as I can remember, I've always been intrigued by it. Initially I started off as an undergraduate in college as a Physics major. But physics, I found, when it got into quantum mechanics, it just wasn't my area of interest.

I like the classical physics, what we would call Newtonian physics or physics of fluids and in motion. I always had a love of the atmosphere, and it just so happened that the university I went to, University of Wisconsin, Madison, has a meteorology program. And so once I realized that my love was really classical physics, I shifted into meteorology and have loved it ever since.

BARRY REICHENBAUGH: And how did you end up in this particular career path?

DAVID STENSRUD: Well, there's a bit of serendipity involved.  I was a Master of Science student at Penn State, which is where I got my advanced degrees. And I met a nice young woman who I ended up marrying.  And basically we were in a place where staying there for a Ph.D. at that point wasn't really a good choice for us. And so I started looking for a job, and I ended up sending a resume here to NSSL and got a response and came down, interviewed, and they were nice enough to offer me a job.  And since then it's just been a wonderful fit that I really do feel that NSSL is a great place to work. The opportunities and the excitement here for meteorology are quite outstanding. And I've been very blessed by being here.

BARRY REICHENBAUGH: Now, you're located here on the university campus, and I'm wondering if you could talk a little bit about what you say to someone who's interested in a career path like yours.

DAVID STENSRUD: Well, I would say you need to have pretty good skills in math and in science to be a meteorologist. A lot of people don't realize the amount of math that's involved, in particular. And so having backgrounds in mathematics and in physics, and nowadays even chemistry is getting more important. And of course, there's always this foundation that goes on of computer programming that most of us have to deal with because it's there every day. To really make any kind of prediction, you end up using computers quite a bit.

But if you have those skills -- I mean, meteorology, I think, is a very exciting science. And one of the things I love about it is the clear public benefits that you get. I mean, you're helping people to make decisions and certainly forecasters are helping to save lives by what they do every day. And that's quite a reward for any career.

BARRY REICHENBAUGH: All right. Well, thanks very much.

DAVID STENSRUD: You're very welcome.