% (applause)
% >> Thank you.
% It's a pleasure
% to be here tonight.
% When I did this two years ago
% I think it was snowing outside.
% So, the thunderstorms and things
% we may get later tonight
% seems a little bit more
% appropriate to the topic
% at hand.
% I'm an associate researcher at
% the Cooperative Institute for
% Meteorological Satellite
% Studies.
% We're a cooperating institute
% between NOAA, NASA and the
% university.
% So, a lot of our work is geared
% toward operations.
% We do research mostly with
% designs to kind of transition
% that research to a forecaster.
% I'll be talking a little bit
% about that in the context of
% hurricanes.
% This is just the names of the
% Tropical Cyclone Research Team.
% Almost everything I'm going to
% present to you tonight is other
% people's work.
% And they definitely get the
% kudos for putting a lot of this
% together.
% I get the privilege of
% presenting a lot of their work.
% We also collaborate with some
% people at the Naval Research Lab
% in Monterey, and the Hurricane
% Research Division in Miami.
% We have lots of collaborations
% around the country and around
% the world.
% Because weather is a world
% phenomenon, it's definitely
% something that you need--
% You need that versatility to get
% good science.
% Just a brief outline.
% I'll begin with some definitions
% so we can clarify some of the
% hurricane and tropical cyclone
% words.
% The bulk of my talk will be on
% satellites and tropical
% cyclones.
% I'll talk about how we use
% satellites to study them, what
% things we can detect from
% satellites and what kind of
% research we can do, and will be
% doing in the next few years.
% And then, I want to conclude by
% touching on this issue of global
% climate change and tropical
% cyclones.
% It's definitely been in the news
% for a long time, and it's
% another kind of hot area of
% research.
% There is a scientist in our
% group that has published some
% work in it, so it's quite
% relevant to our group.
% This is our Web page.
% We've just updated it for this
% hurricane season.
% So, if you've been on it before,
% it's a little bit different,
% you've probably noticed.
% Please go to the link and check
% it out.
% It's very useful.
% Particularly when there is a
% storm, you can get all kinds of
% information about the
% hurricanes.
% And a lot of what I'll show you
% tonight is on our Web site.
% Though some of the stuff,
% particularly the older storms
% may not be directly on there.
% But if for some reason you want
% an animation, please contact me
% and I'd be happy to show you
% where some of those animations
% are.
% So, basic definitions.
% The term "tropical cyclone"
% tends to be used a bit loosely.
% But typically, it's sort of used
% to describe an organized cluster
% of storms.
% Essentially, it's used from
% anything from an organized
% cluster of storms, which might
% make a tropical depression, to
% something as intense as a
% hurricane.
% And usually, the qualifier of
% these different distinctions are
% made by the speed of the winds.
% So, a tropical depression is a
% tropical cyclone with winds less
% than 39 miles per hour.
% There has to be some
% organization, some structure,
% which I'll show you in a minute.
% A tropical storm is when the
% winds are a little bit faster.
% In the North Atlantic and the
% North-East Pacific, we use the
% term "hurricane" for anything
% with winds greater than 74 miles
% per hour.
% A tropical cyclone, I want to
% make a contrast with the storms
% we would get over Wisconsin,
% which meteorologists would call
% an extra-tropical cyclone or a
% mid-latitude cyclone.
% They're very different in terms
% of sort of the energy involved,
% and the way they form, and how
% strong they are, etc.
% They tend to have cold fronts
% and warm fronts.
% These are features that you do
% not see in tropical cyclones.
% So what are the hurricane
% threats?
% I'll touch on this a little bit.
% You may be quite familiar with
% these, particularly after the
% past few years.
% Storm surge is the primary
% threat to life.
% The storm surge is an elevated
% water level, above the mean
% tide.
% And what causes storm surge is
% that we've got this giant storm
% with lots of wind, and it's just
% pushing all of the ocean water
% onto the coast.
% As it makes landfall, this water
% kind of piles up and raises the
% water level.
% And then on top of that elevated
% storm surge, you would get
% increased waves.
% So that would be an additional
% damage on top of the storm
% surge.
% When the United States makes
% evacuation decisions, it's based
% on this threat of storm surge,
% We don't evacuate for winds.
% They evacuate because they
% believe that people are in
% danger of the storm surge.
% The winds are, of course, a
% particularly dangerous threat.
% And ironically, if you evacuate
% people and they evacuate too
% late, the winds can be even more
% dangerous.
% Because if you have a bunch of
% cars on the highway and the
% winds are very bad, then it's
% not a very good scene.
% The other threat that many
% people don't even consider is
% fresh water flooding from rain.
% Hurricanes have a lot of rain in
% them.
% And the even more dangerous
% thing about the fresh water
% flooding is it can happen from
% very, very weak storms.
% Tropical storms or tropical
% depressions can make landfall.
% And if they move very, very
% slowly, they can pour lots of
% rain in an area and cause all
% sorts of damage and all sorts of
% casualties.
% So it's definitely something
% that needs to be considered.
% And then, to make matters worse,
% when hurricanes make landfall,
% they often spawn tornadoes, so
% you have both threats to worry
% about.
% But tornadoes is for a different
% talk.
% When we talk about hurricane
% intensity, we often hear
% reference to the Saffir-Simpson
% scale.
% That scale is based on the wind
% speed of the hurricane.
% You have everything from a
% tropical storm, as I mentioned,
% which has winds from 39-74 miles
% per hour.
% What you're looking at is a
% montage of a satellite image of
% Hurricane Dean.
% We'll be seeing quite a bit from
% Hurricane Dean.
% These are satellite images taken
% at various different times,
% put on the same image.
% You can see, for example, the
% tropical depression and tropical
% storm.
% You get some sort of clustering
% in the clouds, but it's not
% particularly symmetric.
% It's not particularly well
% organized.
% As it gains in strength, in this
% particular case, it gets a
% little bit bigger.
% That's not really directly
% related to the intensity.
% But it gets a little bit more
% structure.
% You're starting to see some of
% these kind of rain bands,
% these kind of wedges of cloud
% out here.
% Then, right before Hurricane
% Dean made landfall, when it was
% a Category 5, you really get to
% see the tight circulation.
% And if the red line weren't
% there, you'd see the clear eye
% that forms within the hurricane.
% This just kind of gives you a
% sense of what these storms look
% like in a satellite image.
% I'll go into that in a little
% bit more detail.
% Again, we've kind of had a taste
% of satellites and tropical
% cyclones.
% That's kind of the briefest
% picture of how they're useful.
% How do we use satellites?
% What do they do?
% You get a chance to see my
% brilliant attempts at drawing in
% Power Point here.
% Satellites detect radiation.
% And this radiation comes in
% various forms.
% But all satellite images that
% you see are radiation that's
% been interpreted by scientists.
% So just a few examples, and a
% lot of what we'll see tonight,
% is you have solar radiation that
% hits clouds, scatters off of
% the clouds and is beamed back
% to the satellite.
% That's a visible radiation.
% So that would be what the clouds
% look like if you were to stand
% in the space shuttle and see it
% from space.
% Clouds also emit radiation.
% They tend to emit primarily in
% the infrared.
% So you'll often see infrared
% images.
% You'll see several of those
% tonight.
% Infrared is sensitive to the
% temperature of the clouds, so
% high clouds that are much, much
% colder--
% As the cloud gets higher,
% it's much, much colder,
% so you can detect cloud height
% from satellite images.
% You also have atmospheric
% emission.
% When we talk about the
% greenhouse effect or global
% warming, it's because you have
% carbon dioxide, for example, in
% the atmosphere.
% That's emitting radiation.
% You can use satellites to figure
% out how much carbon dioxide
% there is, or how much
% water vapor there is
% in the atmosphere.
% And of course, you have land
% emission and sea surface
% emission.
% Many of you have probably seen
% maps of Wisconsin or of the
% planet, and a lot of them are
% satellite-derived images, where
% they looked at the visible light
% coming off the ground.
% So, anything that you see is
% someone's algorithm or
% someone's clever trick for
% converting this radiation into
% something that we can understand
% and conceptually use.
% Now, why do we use satellites
% for tropical cyclones?
% Why is this kind of an obvious
% pair?
% Well, this is a plot of sort of
% conventional observations that
% would go into a forecast model.
% And I'll talk a little bit about
% that.
% But most of the observations are
% cluster.
% Each blue mark represents one
% observation.
% So it would be clustered over
% the continents.
% These little streaks that you
% see here are airplanes.
% So every time you get up in an
% airplane, they're sending
% observations of temperature and
% moisture.
% They're going into forecast
% models and they're used by
% weather forecasters.
% You also have some buoys over
% the ocean, and there are some
% ship reports as well.
% But hurricanes form here or in
% the western pacific, and there's
% not a lot of observations there.
% If we include what we can get
% from satellites, the coverage is
% much, much better.
% This is isn't even accurate.
% The blank spots are because this
% particular modeling center
% doesn't use some observations.
% But you get the basic picture.
% You get global coverage of the
% planet from satellites.
% And you get that very, very
% frequently.
% You can get it as frequently as
% every 30 minutes with some types
% of satellites.
% So it's a natural fit to look at
% something that happens in the
% middle of the ocean with
% satellites, because basically
% there's no other way to do it.
% I wanted to talk a little bit
% about an application of
% satellites.
% This is data that I use, and
% members of my group use quite
% often.
% I'll show you several things
% that we derive from this.
% There are cloud track winds or
% atmospheric motion vectors.
% I'll try to keep the acronyms to
% a minimum, but AMV is one that
% may come up a few times.
% What these are, conceptually
% they're quite simple.
% If you look at the way a cloud
% moves over a given period of
% time, you can back out what the
% wind is doing.
% So for example, if you have a
% cloud, you have three images
% that are 30 minutes apart.
% So you have three satellite
% images of clouds.
% Let's say that the cloud is here
% at 11:30.
% And then at noon, the cloud is
% here.
% And at 12:30, the cloud is here.
% And you know the distance
% between each of these points,
% you can calculate what the wind
% is.
% You've got a change in distance
% over a change in time.
% And you have, from these three
% images, you have two measures of
% the wind, where the cloud is.
% Conceptually, it's actually
% quite simple.
% And many, many years ago, it was
% done by hand, by people looking
% at screens and calculating this.
% Now fortunately, it's done by
% computers.
% But the very, very difficult
% part is figuring out the height
% assignment, figuring out where
% in the atmosphere that cloud is.
% That's quite tricky in a lot of
% circumstances, and is in fact
% sort of a very active area of
% research, is trying to prove
% that height assignment.
% There's also a quality control
% issue, which is some of the work
% that I do.
% That's figuring out once you
% have this wind, how good is it?
% Is there a computer problem
% that's caused this wind to be
% bad, or are we doing something
% wrong conceptually.
% It's something that you have to
% figure out.
% And it's research that we do
% every day.
% So what does this give you?
% It gives you a picture.
% This is a picture of sort of one
% day's worth of these atmospheric
% motion vectors from satellites
% all over the planet.
% Now, the satellites that are
% primarily used for this data
% type are what we call
% geo-stationary satellites.
% They're satellites that are
% rotating at the same speed as
% the earth.
% That allows you to basically
% take a video camera of the
% clouds.
% You see the clouds moving
% because the satellite is in the
% same position relative to the
% earth.
% The second primary type of
% satellite is the polar orbiting
% satellite.
% That satellite moves up along,
% would often stay parallel a
% longitude line and cross over
% the poles.
% And we've actually fairly
% recently been deriving these
% atmospheric motion vectors from
% polar orbiting satellites.
% So that's just not contained in
% this plot here.
% (inaudible)
% There's quite a few.
% And I wouldn't have an accurate
% count.
% These are the primary
% geo-stationary satellites that
% you see up here.
% And then there are probably a
% dozen or so, at least, polar
% orbiting satellites.
% There's probably more than that.
% That's just weather satellites.
% There are probably thousands of
% communication satellites.
% I don't know if anybody has a
% number.
% But quite a few.
% And they're from many different
% countries.
% There's a very, very active
% international collaboration
% between the United States,
% Europe, China, Japan.
% It's pretty exciting stuff.
% So now we've talked a little bit
% about the satellite data.
% We have to look into how
% hurricanes are forecasted, and
% in fact, how anything is
% forecasted in the weather.
% You have to start out with
% observations.
% If you don't know what's going
% on now, you're never going to be
% able to figure out what's going
% to happen in the future.
% For hurricanes, the primary
% types of observations are, of
% course, satellite observations.
% And in the Atlantic and in the
% Gulf of Mexico, we have
% aircraft.
% And when a hurricane is close to
% landfall, scientists fly into
% the storms very regularly and
% get very, very accurate
% measurements of the storm's
% intensity, it's size, it's
% position.
% That's sort of the primary tool
% if you've got a landfalling
% hurricane.
% But if the hurricane is far off
% into the ocean, or if it's way
% out into the Pacific, where
% there are no aircraft
% reconnaissance, then satellites
% are basically the only thing you
% have, unless the storm happens
% to pass over a buoy.
% Of course, there are ship
% observations, but ships
% generally try to avoid the
% storms, so they're certainly not
% a primary tool.
% So once we have the
% observations, what do we do with
% them?
% Well they go sort of in two
% ways.
% One, they go to big, powerful
% computer models, which is
% something I'll talk about in a
% little more detail later.
% And they go to the big, powerful
% forecaster brain.
% The forecasters in the National
% Hurricane Center, that probably
% is an understatement.
% They are the best in the world
% at what they do, and have lots
% of experience studying
% hurricanes.
% They look at the observations
% and get a sense of, you know,
% based on their experience,
% what's going on and what it's
% going to do.
% And then of course, they look at
% these computer models and have
% the very difficult task of
% figuring out which computer
% model is doing a good job and
% which one to believe.
% And particularly for figuring
% out track forecasting, the
% computer model is their primary
% tool.
% If you're trying to make
% a two or three day forecast,
% the computer model is really
% all you have.
% But there are many, many
% computer models that are
% running, so they have to figure
% out which one to use, what to
% believe.
% Then of course, with all this
% information, they produce the
% forecast.
% So what are forecasters
% interested in?
% Of course,
% they're interested in...
% These are perhaps obvious
% questions, but they make kind of
% a nice organizational structure
% to talk about the products that
% we do.
% None of the scientists at the
% University here in our group
% make forecasts themselves.
% But as I was saying, we provide
% products and guidance for
% scientists, for the forecasters
% to make their own decisions.
% Where is the storm?
% This is kind of the most basic
% question that you have.
% And before satellites were
% invented, this was definitely
% not a trivial problem.
% People were surprised by
% hurricanes, unless a ship happen
% to have found it, or that sort
% of thing.
% There was not a lot of
% information as to where they
% were.
% But because of satellites, we
% aren't surprised by a storm's
% existence.
% Now, figuring out the exact
% position of the hurricane can be
% tricky, even with the imagery,
% because sometimes the clouds
% kind of obscure where the storm
% center is, and you need to use
% other tools to figure it out.
% But the imagery is definitely
% the primary tool.
% This is a high resolution image
% of Hurricane Katrina.
% And you get a very, very clear
% sense of the storm.
% You can see the very, very tight
% circulation, the
% counterclockwise motion of these
% clouds, and of course the very,
% very menacing eye.
% You can see some of the cloud
% structure, even inside the eye.
% It's an incredible image.
% But from this image, you get,
% you know, the center of the
% storm is probably there.
% And that problem, at least, is
% solved from this particular
% image.
% What I'm going to show you now
% is an animation of Hurricane
% Katrina.
% This is a combination of visible
% and infrared.
% It's kind of blended together,
% because the visible images are
% not very useful at night.
% The infrared is the primary
% tool.
% So it blends this together.
% What you'll see is Katrina will
% move into the gulf.
% And as it strengthens, the eye
% will clear out and you'll see it
% develop a strong eye and make
% landfall.
% It's kind of a quick animation,
% so I'll replay it.
% So from the satellite image,
% you can get a sense of the
% lifecycle of the storm, how it's
% strengthening, how it's
% weakening, and of course, where
% it is, how it's moving.
% That's kind of the most basic,
% fundamental question that we can
% answer with satellites.
% And it's very powerful.
% The next question is how strong
% is it.
% Again, the primary tool for
% forecasters would be sending an
% airplane into the storm to
% actually measure its wind speed,
% measure its central pressure.
% But if you can't do that, these
% two satellite techniques are
% fairly well-established and give
% you an estimate of storm
% intensity.
% The first one I'm going to talk
% about is the AMSU.
% The AMSU instrument is a
% microwave instrument.
% Microwave is nice because it is
% not sensitive to clouds.
% What it's sensitive to is a lot
% of the structure inside the
% hurricane.
% In this particular application,
% it's sensitive to the sinking
% motion inside the eye.
% In the eye, you have this clear
% region.
% And if you have sinking air,
% that tends to produce clear,
% clear skies.
% And it also is a warming
% process.
% When you have air that sinks, it
% warms.
% And that's what a satellite is
% measuring.
% It turns out that that sinking
% is directly related to how
% intense the storm is.
% So the more sinking, the more
% warming you have, the stronger
% the storm.
% This is just kind of schematic
% in some ways, of Hurricane Rita.
% These contours here represent
% sort of the warming that you can
% see from the satellite.
% When it's fairly meek at 40
% miles an hour, you're not seeing
% very much warming.
% There's kind of a single contour
% of temperature.
% At its most intense, you get a
% lot of the warm air.
% And it's a fairly linear
% relationship.
% There's a lot of complications
% and details to get it right,
% but conceptually it's not
% that complicated.
% The other technique that we've
% done here at Wisconsin is the
% Advanced Dvorak Technique.
% The slide is a little bit busy.
% The Dvorak Technique itself is
% again, quite simple.
% What Dvorak did, I believe in
% the late '70s, was he looked at
% hundreds and hundreds of
% satellite images, and looked at
% the intensity of those storms
% from the satellite images.
% He basically did pattern
% matching.
% He said a storm that looks like
% this, that's this strong.
% And a storm that looks like
% this, that's this strong.
% So the technique is a fairly
% complicated look-up table.
% Right now, it's being practiced
% and used operationally at many
% centers around the world.
% They have a scientist whose task
% it is to look at those images
% and make an assessment on its
% intensity based on the Dvorak
% Technique.
% It's very, very low tech, and I
% think that's what's really neat
% about it.
% What we've done at Wisconsin is
% of course made it higher tech,
% and we've automated it.
% This is work that Tim Olander
% has been doing for a long time.
% He's taken that subjectivity out
% of the process.
% So he looks at this satellite
% image, and from that satellite
% image, through a fairly lengthy
% process, detects what the
% intensity is.
% This particular storm
% has 921 millibars.
% I'm trying to see which storm he
% has here.
% It's not --
% But what he's plotted here
% is sort of a timeline
% of Hurricane Keith.
% This is mean sea level pressure
% along here.
% So the lower the pressure,
% the stronger the storm.
% And you can see how they can
% assign an intensity estimate at
% essentially every time we have
% a satellite image,
% so every 30 minutes.
% Aircraft recon goes in maybe
% every three hours.
% So while the aircraft is much
% more accurate, we get much more
% frequent measurements of the
% air.
% Here is just some, the ADT
% is statistics based on its
% performance.
% You can see it's not--
% The errors are a little bit--
% In this particular case, they're
% actually a little bit lower than
% the official forecast, but we
% don't need to get into that
% detail.
% The next question is where
% is it going and when will it
% get there.
% This is essentially the track
% forecast.
% How do you figure out the track?
% I'm going to talk a little bit
% about it directly, the satellite
% steering flow, which is a direct
% product that we produce.
% And then I want to talk a little
% bit about satellite data
% assimilation, which is this
% process of getting data into
% computer models.
% Because track forecasting is
% primarily done through computer
% models.
% It's worth talking about.
% This is a satellite derived
% steering current.
% If you remember, we talked about
% the atmospheric motion vectors
% earlier.
% What we've done is take those
% atmospheric motion vectors and
% looked at how they change
% vertically in the atmosphere,
% and average them over a depth of
% the atmosphere.
% If you could imagine that a
% hurricane is a quirk in the
% stream, and the stream is the
% atmosphere, if you kind of
% average what the wind is doing
% vertically, you get a sense of
% what direction the hurricane
% will move.
% Keep in mind, this is a
% snapshot.
% It's sort of an instantaneous
% measurement of the steering
% current at a given time.
% So the flow could very much
% change.
% And the hurricane itself can
% actually change the flow itself.
% So it's complicated, but in
% terms of a quick picture, this
% gives you a very nice picture of
% what the flow is doing.
% The arrows you see here, the
% streamline show the wind
% direction of the steering
% current.
% And the reds and some of the
% whites that you can see are fast
% winds.
% The yellows and blues are quite
% slow winds.
% So what I'm going to do is
% animate this.
% What you'll notice is that
% reasonably, Dean kind of follows
% the direction of the flow
% throughout the time period.
% And you also get a nice sense of
% the atmosphere changing and what
% it looks like, what the flow is
% looking like around the
% continental United States.
% You can see the clockwise
% circulation here that's pushing
% the storm westward.
% So it's a very, very useful
% tool.
% I like looking at this a lot,
% particularly when I'm looking at
% say, the National Hurricane
% Center, when they put out their
% forecast discussion, they're
% describing what the atmosphere
% is.
% They talk about ridges and
% troughs, and things like that.
% I can throw this up here and go,
% okay, yeah.
% I can see this is a trough here.
% This is a ridge.
% And here is, you know, you've
% got a high pressure center here.
% You get a very quick picture of
% what the atmosphere is doing.
% I find this very, very helpful.
% So back to this flow chart here.
% As I mentioned, this process of
% data assimilation comes in
% right here, where we bring
% observations into these computer
% models.
% And doing it is definitely not a
% trivial process.
% What I want to try to do is give
% you some conceptual idea of
% what's going on.
% And I do this for a couple
% reasons.
% One of them, as I mentioned, is
% it's fundamental to
% understanding track forecasting.
% The other reason is this is a
% lot of the work that I've been
% involved with, and I've always
% wanted to find ways to explain
% this to my parents.
% So I'm going to try it out on
% you guys, and please let me know
% whether the explanation is
% confusing or clear, and we'll
% see how it goes.
% Let's pose a question.
% Your friend leaves to Madison
% from Chicago at 12:00.
% The question that we're trying
% to figure out is when does that
% friend arrive.
% So the forecast variable.
% What we're trying to predict in
% this particular problem is the
% arrival time in Madison.
% The forecast model, I'll call it
% the Google model, for the
% obvious image.
% And this model says that your
% friend will drive the 146 miles
% at 60 miles an hour.
% Okay, that's the estimate.
% It's a very simple model.
% That makes the math work out
% easier.
% So based on the Google model,
% the prediction is at 2:26pm,
% you'll be eating Michael's
% Frozen Custard with your friend.
% Distance, speed of travel,
% fairly simple.
% Now, at 12:30pm, 30 minutes into
% our forecast, the Google model
% would predict that your friend
% has traveled 30 miles.
% But your friend calls.
% Fortunately we have cell phones,
% or I'd have to think of an
% entirely different example.
% He tells you that he was stuck
% in traffic and has actually only
% traveled 15 miles.
% This is much more than likely
% than him actually getting the 60
% miles an hour.
% So you have to adjust your model
% prediction.
% If you assume that he will drive
% 60 miles per hour from the point
% that he called you on, then you
% have a new arrival time, at
% 2:41, which is 15 minutes later
% than your original prediction.
% And that, in its essence, is
% what data assimilation is.
% You have a forecast, you set
% that forecast forward, and then
% you correct the forecast with
% new information.
% Then you come up with a new
% forecast.
% (inaudible)
% Yes, but this is a very simple
% model.
% Yes, you're right.
% But let's say from now on, it
% goes away.
% You're right.
% You could adjust your model.
% There's one other sort of
% subtlety that I want to try to
% introduce.
% At 1:00pm, your friend calls
% again.
% This time he tells you he's
% traveled 20 miles, but he's
% previously forgotten to tell you
% that his odometer isn't very
% accurate.
% Now you've got an additional
% problem.
% You have to account for an
% uncertainty in your observation.
% We'll call this observation
% error for perhaps obvious
% reasons.
% So now the question is, you've
% been given an observation that
% he's traveled 20 miles.
% Your question is, is your model
% more accurate than your
% observation?
% This in fact goes into your
% point.
% If you fully trust your model,
% okay, if you say he's going to
% be driving at 60 miles an hour,
% then you have to assume that he
% has already traveled 30 miles in
% 30 minutes.
% And the only conclusion is that
% the odometer is wrong.
% So your observation error is
% solely the reason why he tells
% you he only traveled 20 miles
% and not 30.
% If you fully trust your
% observation, then you know, the
% odometer is perfect, then
% obviously the only explanation
% is that he didn't go 60 miles an
% hour and your model is wrong.
% And of course, the truth is
% probably somewhere in between
% those two extremes.
% And this is essentially the
% problems that you deal with when
% you're doing data assimilation
% in terms of the weather,
% which is what I want to
% talk about very briefly here.
% So, in the atmospheric case,
% the model is very complex,
% mathematical equations that
% represent the physical processes
% of the atmosphere.
% It's much more complicated than
% the simple distance equals rate
% times time sort of question.
% The model variables, the things
% you're trying to predict are
% things like temperature,
% pressure, moisture and wind
% around the globe.
% So you've got a lot more models
% and you've got to do it three
% and four dimensionally.
% The problem is much, much more
% complex.
% But this question of error is
% still very, very crucial.
% And again, another area of
% active research is estimating
% these errors.
% How good is your model?
% How good is your individual
% observation?
% You have to do that for every
% single observation that you
% have.
% You have to make this decision.
% I mean, it's done automatically,
% but that's conceptually what's
% going on.
% If you fully trust your model,
% and your observation differs,
% then you eject the observation.
% Vice versa if your observation
% is perfect.
% Then if you go back to the image
% that I showed you with the types
% of observations that we have,
% you can see how satellite data
% assimilation is crucial to this
% problem.
% It gives us, again, nearly
% global coverage of the
% atmosphere.
% So how does this work in
% practice?
% Typically, when you have these
% global numerical weather
% depiction models, they run every
% six hours or so.
% So, a model that started 7:00pm
% Central Daylight Time, would
% produce a ten-day forecast.
% Six hours into that forecast,
% they would take in new
% observations and they would
% correct that six-hour forecast
% and produce a new model that
% would do another ten-day run.
% And you would continue this
% cycle over and over again.
% So you get ten-day forecasts,
% but each one is, in principle,
% a little bit better than the
% previous one.
% That's in principle how data
% assimilation works.
% Of course, the details are very,
% very messy.
% But conceptually you can
% understand that.
% So we do this data assimilation
% process.
% We run these models out and we
% get track forecasts.
% How good are the track
% forecasts?
% These are track errors from the
% actual forecasters.
% This is sort of the value added
% of the forecaster looking at the
% models and making their own
% decisions.
% What we have here, along the
% black, this is the track error
% in nautical miles.
% This is the year of the
% forecasts.
% And these values here are the
% average errors for 72 hours,
% a 48-hour forecast, and 24-hour
% forecasts.
% You can see that the short-term
% forecasts are generally better
% than the long-term forecasts.
% But what the trend lines here
% show is that in general, the
% forecasts have been improving.
% The trend continues past 2002.
% This is just the latest image
% that they had from the Web site.
% In terms of track forecasting,
% we've definitely been getting
% better.
% I'd say the primary reason for
% that improvement is that our
% computer models and our use of
% satellite data and other
% observations has gotten better.
% That's the good news.
% This is the intensity error.
% This is the error in wind speed
% forecasts, again in year.
% You notice that the trend is
% pretty much flat.
% It's gotten a little bit better.
% But in general, not so good.
% It's a much, much more difficult
% forecast to make.
% And it's certainly where lots of
% active research goes into.
% Which leads to sort of the final
% question in our forecast.
% How strong will it be?
% Again, we don't provide
% intensity forecasts, but we do,
% this is one particular tool that
% I know is used by hurricane
% forecasters to figure out
% intensity.
% Then I'm going to show something
% from a MIMIC plot, which I'll
% explain.
% It's more of a research tool,
% something that will give us
% more information into the
% structure of a hurricane.
% It's also probably the coolest
% animation that we have.
% So I have to show you that.
% First of all, what is wind
% shear?
% This is a plot of wind shear.
% Wind shear is the change in wind
% in the atmosphere with height.
% If you take the wind at the
% upper levels of the atmosphere,
% take the wind at the lower
% levels and subtract the two,
% that's how you calculate wind
% shear.
% So we go back to the atmospheric
% motion vectors that I talked
% about and we produce these plots
% from that data.
% It turns out that hurricanes are
% very, very sensitive to wind
% shear.
% They don't like strong wind
% shear.
% So a forecaster will look at
% this and say the hurricane is
% moving into a region of low wind
% shear so it's likely to
% strengthen.
% And that's what they would use a
% plot like this for.
% Again, the color scheme is very
% similar.
% The reds and whites show very,
% very strong wind shear.
% The dark colors show very, very
% weak shear.
% And the arrows show the
% direction of the shear itself.
% This is Hurricane Dean, again.
% We're going to watch.
% If you remember, Hurricane Dean
% strengthened as it moved into
% the gulf and against the
% Yucatan.
% As you'll watch, it will
% strengthen in an environment of
% low shear in this animation.
% It pretty much stays in the
% blues almost the entire time.
% This particular case.
% I know I've been told this is a
% very, very useful tool, and
% something the forecasters look
% at all the time to make some of
% these forecast decisions.
% Yes?
% (inaudible)
% In this case, what you're
% interested in is the environment
% around it.
% And in fact, I believe what they
% do here is they try to remove
% the wind caused by the hurricane
% from these plots.
% But yeah, the hurricane is
% definitely interacting with the
% environment, so that is
% relevant.
% I think that it's the
% environment that they care
% about, not the actual storm.
% Yes?
% (inaudible)
% Yeah, definitely.
% Some of that may be the removal
% that I talked about.
% But yeah, it definitely seems to
% have flown that way.
% I'm not familiar enough with
% what was going on weather-wise
% to explain why.
% But it definitely seemed to be
% in a very favorable environment.
% It's also in an area of very,
% very warm sea surface
% temperatures, so there's more to
% it than the shear.
% The shear is a primary issue.
% The final satellite product that
% I'm going to be showing here is
% the MIMIC product.
% This is unique in lots of
% different ways.
% What the MIMIC product uses is
% microwave imagery.
% If you remember, the microwave
% instrument is only available on
% polar orbiting satellites.
% So you only get an image if the
% satellite happens to pass over
% the storm.
% If you have even a few
% satellites, you might get a pass
% every hour and a half, every
% three hours.
% You may not get a full pass.
% You may get the satellite
% passing over at the side of the
% storm.
% So you get these little
% snapshots.
% It makes it very, very
% difficult to analyze what's
% going on.
% So what Tony Wimmers has done is
% he's found a very clever
% technique to blend the
% successive images and create an
% animation.
% How he described it in a talk a
% few weeks ago is that with a
% little bit of work, he makes
% polar orbiting satellites look
% like geo-stationary satellites.
% And as you'll see, that's pretty
% much what it looks like.
% It's not quite as accurate.
% He's doing some mathematical
% calculations and some
% assumptions, but it's a fairly
% powerful technique.
% That's the technique behind what
% you're going to see.
% What are you looking at with the
% microwave?
% As I said, the microwave is
% sensitive to the thunderstorms
% and some of the ice particles
% inside the storm, these things
% that are present in very, very
% deep, intense thunderstorms.
% So it gives you a very good
% sense of the strength of the
% storm and the structure of that
% strength, where in the storm is
% it strong.
% The yellows and the reds
% are these regions of very, very
% intense thunderstorms.
% I'm going to play this once and
% let you look at it.
% Then we'll talk a little bit
% about what you've seen and I'll
% run it again.
% It's kind of a complicated
% image.
% One thing to keep in mind is
% that this image is following the
% storm.
% So it can be a little
% disconcerting to see the land
% moving and the storm isn't.
% So it's a storm-centered image,
% so it's a little strange.
% Okay, now I can ask you.
% Anybody notice anything
% interesting in that animation?
% Anything?
% I can talk a little bit.
% Let me back up a little bit.
% So, hurricanes tend to have
% these very, very strong
% thunderstorms just outside of
% the eye of the storm.
% That's what you're basically
% looking at here, these very,
% very strong thunderstorms
% surrounding the eye.
% What did you notice?
% What happened to the eye as that
% animation went through?
% I'll play it once more.
% What happened to its sides?
% Well, lots of things happened,
% I guess is the question.
% What I've seen is that, you see
% there's kind of this inner eye.
% And very, very weakly at this
% point, there's sort of an outer
% eye.
% Watch as I run it again, watch
% the way that outer eye kind of
% develops and strengthens.
% Then watch what happens to the
% eye inside it.
% Often, not always, but often
% when storms get very, very
% intense, they develop this
% secondary eye wall, this second
% band of thunderstorms.
% When that happens, the inner eye
% tends to collapse.
% Then the outer eye wall becomes
% the eye wall.
% When this happens, the intensity
% of the storm tends to change.
% It often weakens.
% I'll show it once more so you
% can see it.
% Again, it develops very
% strongly, and then the inner eye
% wall slowly collapses.
% In this case, it actually
% intensifies a bit in this
% particular case.
% This is Hurricane Ivan from
% 2004.
% Why is this significant?
% Well, if forecasters can find a
% way to understand this process,
% which they don't understand very
% well right now, then they get
% some sense of, at the very
% least, when it may change.
% Often, when the secondary eye
% wall formation process happens,
% the storm weakens.
% So if they have some
% understanding of why this
% happens, then they might be able
% to predict an intensity change
% from it.
% There's lots of other things you
% can get from these images.
% In fact, my colleague was
% involved in a lawsuit with
% Hurricane Katrina because his
% images could give information
% about intensity that other
% instruments could not.
% It's a very, very powerful tool.
% He's just published it a few
% months ago.
% It's definitely a cutting edge
% product that he's produced.
% Now I want to move away from the
% short-term forecasting realm and
% talk very briefly about global
% climate change and tropical
% cyclones.
% What's behind global climate
% change?
% Well, the earth is warmed by the
% sun and the atmosphere.
% If we didn't have an atmosphere,
% it would be unbearably cold on
% the planet.
% The oxygen, the nitrogen--
% More importantly,
% the carbon dioxide
% and the water vapor
% in the atmosphere emit radiation
% to keep up warm.
% If we increase the atmospheric
% CO2, you would increase this
% atmospheric component of
% warming, and hence the theory
% behind global warming.
% Now, the big question.
% What will warming do to the
% planet?
% It's a bit uncertain.
% There's been lots of active
% research.
% And there are very, very
% intelligent people making very,
% very educated guesses.
% But nobody is really certain.
% The system is just very, very
% complex.
% Why would this affect tropical
% cyclones?
% Tropical cyclones can be thought
% of as engines.
% The warm, moist air from these
% tropical oceans fuels them.
% Water vapor, when it condenses
% into liquid water, releases
% energy.
% It's a very, very powerful
% energy source.
% That's the fuel that drives
% hurricanes, which is why when
% they move on land, they weaken.
% They don't have that really,
% really rich water vapor air.
% And they give off cool, dry air
% aloft as exhaust.
% That sort of follows the engine
% model of a hurricane.
% Our tropical sea surface
% temperatures have warmed
% by about 0.2 degrees Celsius.
% This is from a paper by Bister
% and Emanuel.
% I'm sure there are other numbers
% floating around.
% But they've been warming by
% a little bit over the past
% 50 years.
% If you increase the temperature
% of the ocean surface, you'll
% increase the storm's potential
% fuel.
% If you warm the ocean, you're
% going to increase the amount of
% evaporation that you have.
% And you get more of that water
% vapor into the atmosphere, more
% fuel for the storms.
% That's the theory.
% You increase the storm's
% potential fuel.
% This is just a plot of the
% number of Atlantic hurricanes
% and major hurricanes from
% 1945-2005.
% I could add 2006 and 2007, and
% it would drop down significantly
% from 2005.
% But the point is still the same.
% First of all, you notice that in
% both of these plots, it's very,
% very noisy.
% There are some years where there
% are a lot of storms and some
% years where there's a few
% storms.
% But you can play some tricks.
% This is just an average, a
% smoothing technique.
% I just took a little bit of an
% average.
% You can see, when you smooth it
% out a little bit, that there are
% some trends.
% It looks like there was a
% minimum in the '70s, and an
% increase in the '90s and 2000.
% So these trends are something
% you can look at in the Atlantic.
% Several scientists have.
% The question is, is this
% increased frequency in the
% Atlantic part of natural
% variability, is it a sign of
% global warming, or is it both?
% Well, the answer is really is
% that it's uncertain.
% Globally, there has been little
% change in tropical cyclone
% frequency.
% So just looking at the Atlantic,
% you get one story.
% Looking at the globe, it's a
% much more mixed picture.
% Climate modeling studies have
% been ambiguous.
% They've done lots of studies
% where they've run climate models
% and sort of counted the number
% of hurricanes that is produced.
% Some studies have shown an
% increase.
% Some have not.
% And sort of the fundamental
% problem is there's a short
% historical record.
% We've only been studying
% hurricanes, looking at them,
% really, for maybe 100 years,
% and really well with satellites
% for even shorter than that,
% 20-30 years.
% There's a big data problem.
% Peter Webster was one of
% the primary scientists
% in his 2005 paper in "Science."
% He showed a global increase
% in Category 4 and 5,
% the very, very intense storms,
% between 1970-2004.
% But he did not find an increase
% in the global storm total.
% So, the total number of storms,
% he didn't find an increase,
% just with the very, very intense
% ones.
% Emanuel published in his 2005
% paper in "Nature," he found a
% relationship between a quantity
% of power dissipation.
% Essentially, he looked at the
% maximum winds of the hurricanes
% and cubed that wind.
% He found a relationship between
% that quantity over the lifetime
% of the storm and increased sea
% surface temperatures.
% He looked at both the Atlantic
% and the Pacific.
% So he found this relationship.
% Both of these studies kind of
% suffered from the same problem.
% They relied on the National
% Hurricane Center's Best Track
% dataset.
% This is historical dataset.
% You can go online and download
% it yourself.
% And it's a great dataset, but
% it's not very well suited to the
% task of detecting trends.
% Why?
% The observation network has
% changed throughout the dataset.
% When they created it, they chose
% the best possible estimate of
% the hurricane's intensity and
% position at the time.
% So of course, that estimate, and
% the skill of that estimate, is
% much, much different.
% Hopefully, much, much better now
% that it was 20, 30 or 40
% years ago.
% You could be detecting a trend
% in the data that isn't physical,
% but rather is a symptom of the
% way we measure hurricanes.
% What's the solution?
% Well, in part, satellites.
% Jim Kossin, who's a colleague,
% produced or did some research
% with some colleagues at the
% National Climatic Data Center.
% He published this in
% "Geophysical Research Letters."
% What he did was he derived a
% globally consistent dataset,
% using reprocessed satellite data
% from 1983-2005.
% His paper really only addresses
% the problem in a limited basis,
% but he basically degraded the
% 2005 satellite data to be the
% same quality as the 1983 data.
% Then he came up with a very
% simple method for estimating
% hurricane intensity from the
% satellite data.
% So he sacrificed accuracy for
% consistency.
% His research came up with two
% conclusions.
% He calculated that there was a
% significant intensity increase
% in tropical cyclones in the
% Atlantic basin.
% In that particular result, he
% agreed essentially with Emanuel
% and Webster.
% But he calculated no significant
% trend in the global dataset.
% So looking at the globe, he said
% there was no statistical trend
% from the algorithm that he
% produced.
% If you have a statistics
% background, or you know, it's
% worth taking a look at the
% paper.
% It's quite interesting.
% My favorite part is he basically
% throws that last sentence in as
% almost an afterthought, even
% though it really is quite a big
% deal.
% It's an interesting study.
% Of course, this doesn't end the
% search for the answer, but it's
% definitely one approach.
% There have been other scientists
% that are trying other things,
% looking at ship data and trying
% to simulate today what it was
% like if we only had ships today,
% and trying to see if they could
% detect a trend that way.
% But there's various other tricks
% that scientists are trying to
% address this problem.
% But it's something that
% definitely has to be addressed.
% I want to conclude by quoting
% the Inter-Governmental Panel on
% Climate Change, the Nobel Prize
% winning group that have studied
% the state-of-the-art of climate
% science.
% I'll read some of this to you.
% "There is observational evidence
% for an increase of intense
% tropical cyclone activity in the
% North Atlantic since about 1970,
% correlated with increases of
% tropical sea surface
% temperatures."
% So, it agrees fairly well with
% what we've talked about.
% "There's also suggestions of
% increased intense tropical
% cyclone activity in some other
% regions where concerns over data
% quality are greater."
% It's a very politicized
% statement.
% You can sort of see them trying
% not to offend anybody.
% It's kind of interesting.
% "Multi-decadal variability and
% the quality of tropical cyclone
% records prior to routine
% satellite observations in about
% 1970 complicate the detection of
% long-term trends in tropical
% cyclone activity.
% There is no clear trend in the
% annual number of tropical
% cyclones."
% So it very much fits this
% research that's been going on.
% I think it says it with lots of
% big words, but in kind of a nice
% way.
% That's basically the conclusion
% of my talk.
% For more information, please see
% our Web site.
% I also very much recommend the
% NOAA's Hurricane Research
% Division FAQ list.
% The easiest way to find it is
% just Google "HRD FAQ".
% That's how I do it.
% I can never remember it.
% It should bring you right there.
% And of course, feel free to
% contact me if you have any
% information.
% Of course, I forgot to bring
% business cards.
% Thank you very much.
% (applause)
% Yes?
% (inaudible)
% Yes, he asked if there is a
% trend in the Atlantic, a
% positive trend in the Atlantic
% and no significant global trend,
% then is there, say, a negative
% trend globally.
% That's a good question.
% I suppose that's a possibility.
% It's also possible that just--
% You have a lot more storms, say,
% in the west Pacific than you do
% in the Atlantic, so maybe that
% just, sort of the signal to
% noise has just increased so much
% when you add more neutral cases.
% I don't remember.
% I'm sure Jim Kossin's work dealt
% with that, but I don't remember
% the actual answer.
% So it could be either one.
% Yes?
% (inaudible)
% That's a fabulous question
% that I wish I knew.
% It's changed a lot in the past
% ten or 15 years.
% Certainly, like the microwave
% for example, which--
% I should know my satellite
% history better.
% The microwave instruments that,
% if we had them, were very, very
% low resolution, so that's one
% thing.
% And I believe the geo-stationary
% satellites were also no where
% near the quality they are today.
% So even if the numbers had
% stayed the same, which they
% haven't, we definitely have more
% of them, the quality and our
% understanding of them have
% improved.
% (inaudible)
% Right.
% No, that's exactly what, sort of
% what Jim Kossin's work tried to
% address.
% And the criticism of Emanuel's
% and Webster's work.
% That's exactly.
% If you don't have as good a
% detection system, and you
% don't--
% We also didn't send airplanes
% in as frequently as we do now.
% So it's likely that we missed
% the very, very intense storms
% that we had.
% All of those issues that you
% mentioned are significant.
% Yes?
% (inaudible)
% Well, this is not our
% government.
% This is a worldwide body of
% scientists and politicians.
% (inaudible)
% Sure.
% (inaudible)
% I would say that, I don't know
% this for certain, but I would
% say the Bush administration had
% very, very little, or did not
% have a great impact.
% There may be some people who
% know the process, but this is
% completely independent of, I'm
% sure there were government
% scientists there, but this is
% independent of the Bush
% administration.
% This is a worldwide body of the
% best scientists, probably a lot
% of university scientists, which
% I don't believe had particular
% pressure.
% Most of the news stories have
% been NOAA, NASA scientists
% directly speaking out.
% I would think there may be some
% sort of international political
% pressure in some of the
% statements here, but I would
% guess that it's not all that
% significant.
% Someone may correct me.
% (inaudible)
% I would say the IPCC is the
% best, is the state of the art.
% Their report is the
% state of the art climate science
% in the world today.
% Yes?
% (inaudible)
% It would certainly be, 2006
% and 2007 were both, I think,
% slightly below average years,
% in terms of numbers.
% I mean, 2005 was extraordinary.
% In terms of the 30-year trends
% that people are calculating,
% I'm not entirely sure.
% Most of this activity happened
% in 2005, for sort of obvious
% reasons.
% Whether two extra data points
% will change some of these
% results, I don't know.
% Yes?
% (inaudible)
% Yes.
% (inaudible)
% That's a great question.
% I don't know the answer.
% I certainly don't know enough
% about how the climate changes.
% There is evidence that the sea
% surface temperatures are also
% increasing.
% They may be increasing less
% than the air temperature.
% They're definitely connected
% and related.
% I don't know.
% Yeah?
% (inaudible)
% Yes.
% (inaudible)
% In terms of?
% (inaudible)
% Mm-hmm.
% (inaudible)
% Well, I think there's...
% (inaudible)
% I'm not an expert in the
% feedbacks, but I know, sort of
% the simplest I've heard is that
% if you have ice melt.
% For example, if the icecaps
% melt, then certainly in
% Antarctica, for example, if the
% ice melts, then you get less
% solar radiation that's scattered
% back to the earth.
% So you get a feedback in terms
% of the solar radiation that
% would've been reflected back
% into space is now absorbed by
% the ground and heats the planet
% in that sense.
% If you're looking at the changes
% in the mid-latitudes, it gets
% fairly complicated as to these
% feedbacks.
% Which I'm not an expert in
% climate science at all.
% Yeah, they're quite complicated.
% It's actually worth finding the
% Web site and looking.
% They've got a fairly consistent
% report.
% I think they're various levels
% of complexity of the IPCC
% report.
% That's the place to go if you
% have questions about sort of the
% state of the art climate.
% They've got a lot of numbers and
% they talk about all these
% issues.
% It's not an area that I'm an
% expert in.
% Yes?
% (inaudible)
% Are you talking about the
% intensity estimate?
% It doesn't directly measure it.
% What it...
% Okay.
% (inaudible)
% For that, it measures lots of
% different things.
% That's a good question.
% For the AMSU intensity, what
% I believe it's measuring is it's
% measuring oxygen molecules that
% are warming in the upper levels.
% So that as this air sinks in the
% eye it warms.
% And what the satellite is
% sensitive to is the oxygen in
% the upper levels that's warming.
% That's what it's detecting.
% It's taking that radiation and
% statistically relating it to
% wind speed.
% In the MIMIC algorithms, which
% are also microwave, I believe
% what it's sensitive to are the
% ice crystals and the ice
% particles that are very
% prevalent in the intense
% thunderstorms of the hurricane.
% So it's the same sort of
% instrument, but used in very
% different ways.
% Yes?
% (inaudible)
% Yes.
% (inaudible)
% I don't think it's that
% sophisticated.
% It looks at--
% It find the center of the storm.
% It can measure whether there's a
% clear eye, or whether you've got
% sort of a curved band.
% And Dvorak himself had very
% specific intensity estimates for
% all these various shapes of the
% storm.
% And it also has a lot of
% different rules, like it looks
% at the history of the storm and
% says, well you can only change
% it by this much from the
% previous estimate.
% So it's relatively simple.
% I think it's a bunch of sort of
% yes/no questions.
% There's some complexity in
% figuring out those features.
% But yeah, it's nothing more
% complicated.
% Yes?
% (inaudible)
% I absolutely wish I had seen it.
% I'm embarrassed to say that I
% haven't.
% But from what I've heard, he did
% sort of overstate some of the
% impacts of hurricanes.
% Off-base may be too strong a
% word.
% I would say you have to be very,
% very careful when you make,
% particularly somebody in the
% public eye, and they make
% conclusions of science that may
% not be scientifically sound.
% Because it then hurts your other
% arguments that are probably very
% scientifically sound.
% So I would say, you know, he
% definitely commands a very large
% audience and has a lot of
% impact.
% So if he made statements that
% weren't true, maybe off-base is
% not too strong a word.
% I would say, you know, you
% always have to judge between
% getting too complicated and
% being obviously truthful and not
% misleading.
% And I think there are ways to do
% that without exaggeration.
% He probably could've found a
% cleaner way, but I have not seen
% the movie, so I shouldn't
% entirely speak on it.
% Yes?
% (inaudible)
% Yes.
% I believe when you go--
% The geo-stationary satellites
% are very, very far out in the
% atmosphere, something like
% 36,000 kilometers up.
% In order to use the microwave,
% you need a very, very large
% antenna.
% So it's essentially an
% engineering problem.
% There was a meeting, I think
% this spring of people trying to,
% proposing to put microwave
% satellites on geo-stationary.
% It would be a fabulous
% instrument to have, but I think
% it's an engineering problem.