Chapter 9: Observation, Analysis, and Prediction

Focus 2: The Tropical Forecasters' Perspective »
9F2.1 National Hurricane Center Forecasters (Audio Interviews)

Audio interviews were conducted with the NHC forecasters, Dr. James Franklin and Dr. Lixion Avila, on 9 June 2009. Here are edited versions of those interviews.

Click to view these interviews in Spanish.

Click to view these interviews in Spanish.

Dr. James Franklin, National Hurricane Center Forecaster

Dr. James Franklin

Question 1

How and when did you decide to become a tropical cyclone forecaster? What was your path? Did you do anything special in elementary or high school to further your goals?

Answer:

The path really began as a 6 year-old experiencing Hurricane Cleo when it went over my house, … but that's really what triggered my interest.

I didn't start out in forecasting at all. I was a researcher for 17 years, flying into storms with the hurricane research division of NOAA; did some operationally related research towards the end of that period when the GPS dropwindsonde got developed and spent some time looking at how hurricane winds behaved, the structure of those winds with height in the eye wall. That work caught the attention of some of the folks at the National Hurricane Center and they actually approached me about whether I had some interest in doing it. I hadn't really thought about it at all until they talked to me about it.

A lot of things in life I think are fortuitous. Being exposed to some hurricanes growing up; while I was in school at MIT, Bob Burpee who was at the Hurricane Research Division, was on sabbatical. I took his course on tropical meteorology, I guess I did well enough to have him approach me about a position.

All through high school, I had two main interests: one was meteorology the other was astronomy. Meteorology seemed to be the more interesting field, at least had more of an influence on people's lives and was a little bit more exciting. So that was the field I ended up taking. But I knew that going into college, that that's the kind of thing that I wanted to do, weather in general, I didn't know it was going to be hurricanes at the time.

I did spend a fair amount of my undergraduate time at MIT taking meteorology with folks like Fred Sanders, Pauline Austin, and spent some time with Fred, Kerry Emanuel going back and forth across the New England coastal front in Fred's Rabbit. My master's thesis was going to be on the New England coastal front. My good friend, Frank Marks, who was at MIT at the time, agreed to look over the collection of the data, while we went out and took the observations. Came back and found out the Frank had erased all the data instead of saving it and I was left with no thesis. Bob Burpee took me at the Hurricane Research Division anyway. A couple of years later, we came up with another thesis topic.

Question 2

What process do you use to forecast TC? What tools? Does your answer differ between track, intensity, size and rainfall forecasts?

Answer:

Track
Many years ago people could outfox the guidance at least in terms of track forecasting, but the numerical models have gotten so much better over the last 15, 20 years, in fact, our track forecasts are now about twice as good as they were 15 years ago. It's really very difficult for a forecaster to out integrate numerical models.

A very powerful technique in all of meteorology not just hurricanes is the idea of consensus. Get 3 or 4 or some group of skillful numerical models and you combine them. That's our goal; every time we sit down to make a forecast, that there is something that we can add to try and improve on that consensus. Is there a model that isn't initialized well? Do we recognize a situation when a certain model tends to perform well or tend to perform less well in? Very often your expectations for intensity play into the track forecast, but it all centers around the notion of a consensus and then trying to make moves in one direction or another from that.

One aspect of the forecast process has to do with presenting a consistent picture to our users. We have a very strong continuity constraint from one forecast to the next. We tend to make small increments on the forecast that came before us, improve what you inherited. I haven't talked about intensity so let me go on to that a little bit.

Intensity
Intensity forecasting is a little bit different then the track forecasting in the sense that guidance is not nearly so advanced. The forecast on the other hand does a better job than our intensity guidance. We have 4 intensity guidance models, 2 are dynamical, and 2 are statistical. The human forecaster can see a lot more of what's going on with the storm in terms of its convective structure and how the wind field is organized in terms of concentric eyewalls. None of the kinds of core factors is really well incorporated into our existing intensity guidance. By seeing what's going on in the core, that's where it is the forecaster really adds value to the guidance and is able to contribute a lot more to the process.

Size
We also forecast the size of tropical cyclones to issue these wind radii forecasts, the maximum extent of hurricane force winds in each of 4 quadrants around the center. So we are actually ahead of ourselves there in terms of what we provide relative to what we can do skillfully. So our error in even estimating that parameter is on the order of 50%. That a really tough one. About all that we have to help us with those are climatology and persistence models, the CLIPER model for example for wind radii. We have presumption that as a storm strengthens generally is it going to larger. After a storm is complete, we do what's called a best track, a post analysis of all the available data to create a permanent record, our best record of what happened with that particular storm: location, pressure, maximum sustained wind speed and the status, whether it's a tropical, subtropical, extra tropical. Until very recently we did not do a best track on the wind radius. Until you get to 2004, 2005, those are all operational estimates. They were not reviewed after the fact, those data certainly are not the same quality as the final best track intensity estimates. We've also got very few, sometimes no, real measurements of how large these radii really ought to be. From looking at the cloud shields, surface observations (these are very few), we have some additional tools to help us there. The AMSU, microwave instrument also gives us some size estimates.

Rainfall
Responsibility for rainfall forecasts lies with the Hydrometeorological Prediction Center. They are looking at model guidance, analogs, and previous storms. In our advisories now, we get the guidance for rainfall from HPC.

Question 3

Have ensemble model predictions changed your process?

Answer:

Ensembles are a big part of the tropical cyclone forecast process. Two primary methods would be to take a single model and run it repeatedly using slightly different initial conditions. That's one way. That has not been very successful in operational tropical cyclone forecasts. You can also form a consensus from different models.

When the members that make up an ensemble are independent you get a much better ensemble mean. We're also finding for intensity forecasting, if you form a consensus of the four main intensity models, it is much better on average than any of those four models individually. I think part of the reason for that is those models are very independent. They're not even the same type, you have two statistical models and you have two dynamical models and they're quite independent and it's that independence that really helps you. When members of an ensemble are independent, the errors tend to cancel, particularly when the synoptic situation is relatively simple, so we see this a lot in the eastern Pacific when the synoptic flows are simple. A lot of the errors in the models are random so if you can combine 3 or 4 of them you get a much better forecast then any individual one.

In the Atlantic the signal is not so strong. In the Atlantic we have stronger synoptic scale forcing, more complicated environments, midlatitude troughs coming down and a lot of other features in the environment and in that situation the different models are handling these features in different ways. The consensus concept in the Atlantic is a little bit less effective than it is in the east Pacific because there is more of a systematic component to how the models behave.

Question 4

What's your day like when there is a TC to forecast? How is it different if there are a number of TC? Is it any different if the TC are in different basins or in the same basin?

Answer:

Forecasting shifts are divided in to three: day, evening and midnight shifts. The day shift is responsible for 2 forecasts at 11:00 AM and 5:00 PM. The evening shift is responsible for the 11 PM advisory. The midnight shift is responsible for the 5:00 AM advisory. The day shift is longer, more tiring, much more hectic with more people floating around and more of a media presence.

The forecast cycle basically runs for 3 hours. The 11:00 AM advisory, for example, starts at 8:00 AM with an aircraft reconnaissance fix. You spend about 15 minutes analyzing the data from the reconnaissance aircraft, initial location, initial intensity, initial motion, initial size, all the parameters that are going to go into initializing the numerical model guidance. You update the best track and then submit guidance. We get it back and from about 8:30 AM and in this case up until 10:00 AM is spent making the forecast, looking at the models, the guidance, water vapor imagery, sea surface temperature all the different parameters that are important to track and intensity. By 10:00 AM you have your forecast prepared and we do a conference call with other weather service forecast offices as well as other agencies in the federal government. HPC will be on there, to talk about rainfall; the Storm Prediction Center will be on there, to talk about tornado threats; the Navy is there; Homeland Security is there; a whole host of agencies are there where we discuss the forecast, coordinate any issues that need to be coordinated, like where watches and warnings might be. That call can last from 3 or 4 minutes to a half an hour.

After the conference call is done we then prepare the forecast. We write our discussions and a summary of our rational behind the forecast. We talk about things like forecast uncertainty, why we've analyzed it to be the intensity that we have, why we've chosen the forecast that we have. That's a very important product for us used by emergency managers, the general public helps to provide some context for the forecast. In that last 45 minutes we're writing the discussion, we're preparing the public advisory.

If there are multiple storms you may have to do that for two and on occasion I've had to write even three advisories at once in that three hour period. We do have 2 forecasters on shift, but sometimes we may be working as many as 5 storms simultaneously between the Atlantic and east Pacific. There are times where you really have to compress it. It really depends on what kind of storms you have to deal with. If we have a significant landfall going on or imminent, within a couple of days of landfall, one of the forecasters will focus on that storm exclusively and leave 2 or even 3 to the other person if they are a less difficult forecast situation. We will try, when we have more than 3 systems, to bring in somebody to help us out. They are called hurricane support meteorologists; they have some experience in putting together advisory packages. They come in and help us with the least complicated of the systems that we're working on so that folks from the marine tropical forecasting unit, they're the folks that do the marine forecasting, or they might be from the technical support branch here at the hurricane center. They do have meteorology degrees, so some of them do have experience writing advisory packages. They're all Hurricane Center employees.

Dr. Lixion Avila, National Hurricane Center Forecaster

Dr. Lixion Avila (English)

Question 1

How and when did you decide to become a tropical cyclone forecaster? What was your path? Did you do anything special in elementary or high school to further your goals?

Answer:

I wanted to be a hurricane forecaster since I was a kid in Cuba and I was always asking questions to the farmers and to the fishermen. But, actually, I didn't start doing that until I was able to finish what you call pre-university degree in Cuba. I entered the Met Service in Cuba but I always wanted to be a hurricane forecaster. After that I went to the United States and I got my Masters and Ph.D. and I was lucky enough to enter the National Hurricane Center and I've been a forecaster for almost 25 years.

I got my degree in Cuba and I worked as a forecaster. But you take care of all the weather systems including hurricanes and I did that for about 5 years.

What I didn't know when I was in elementary school is that you needed a lot of math and physics to study meteorology but actually I love math and physics so it turned out to be good. That's one of the things that really got me by surprise. When you're a kid you don't know that you need those tools, you thought it was more like a fun thing to do than really study hard.

When I was a little kid, one of the things that I enjoyed was to wait for the afternoon thunderstorms, especially in the hurricane season. I grew up near the beach so there was some hills in between the ocean and the beach. Sometimes, I see the thunderstorms coming from south and sometimes I see the storms coming from the ocean. And it was really wonderful for me to enjoy the development of the clouds. And it was really interesting to see that. I am glad that my parents put up with that and they always pleased me with that craziness. But it turned out to be the best for me in the world.

Question 2

What process do you use to forecast TC? What tools? Does your answer differ between track, intensity, size and rainfall forecasts?

Answer:

I've been doing this since 1971. I have seen how the tools have changed. Tremendously! In the '70s we were plotting the surface data by hand. We were getting, maybe, one map a day of a forecast of 24 hours at 500 mb. The facsimile; we even got 1 satellite picture or two satellite pictures a day. And that was probably the case in most of the Caribbean countries and probably not very different in the United States.

We began to get more and more satellite pictures…What really changed the way of forecasting is the development of numerical models and computer science. And, nowadays, we can use all these sophisticated computer models and the physics and dynamics that goes into models to predict hurricanes.

First thing we do in the morning, is to look at the entire globe and all the satellite pictures and look at the different systems. Then we emphasize the system that we think is going to do something or not. We look at the analysis of what's going on through the computer models, of course, using the, also, the conventional data. Before I wasn't able to integrate in my head 5 days in advance, now the computer models are able to give me a better estimate, at least, what could happen, or the possibilities that could happen in terms of the distribution of high pressures and low pressures.

Nowadays we make a 5-day forecast and the weather pattern that is going to affect that cyclone today, if it's in the Caribbean, that weather system is, perhaps, in the western Pacific now, then move to the east. So we need to look at the whole pattern. Either track or intensity, you have to look at the whole thing because the intensity change is related to, perhaps, an upper trough coming. It is more difficult, of course, the forecast of the intensity.

Size
At this moment when we make a prediction, if we don't have data, we don't even know the size of the system. So what we do is try to extrapolate. But we don't have any tools to predict changes in size other than climatology. Perhaps some of the numerical models would give us the size of the storm when they are moving and becoming extratropical in higher latitude. The models are doing a little better, but in terms of the deep tropics the change and size, it's very difficult to predict. What we do is just basically use extrapolation and when we get new data that tell us that the size is larger, we adjust with the new data.

Rainfall
In terms of rainfall, it's even more difficult. Rainfall is probably the cause of most of the deaths, for example, in the region of the Caribbean. But it's the same in most of the tropics when you even have a small, weak tropical depression that could produce 20 inches of rain and produce a tremendous amount of damage. And, unfortunately, many people are killed by the rainfall, especially by flash floods and all this orographic rain that you have when you have a weak tropical depression. Those are very difficult, difficult processes, both the size and the rainfall.

You know, the intensity of a tropical cyclone has nothing to do with the rainfall. It's basically the speed and the amount of convection that the system has. You can have a hurricane that can produce less rain then a weak tropical depression.

There are some old rules I learned from the old-timers and they use the number 100 divided by the speed of the tropical cyclone and you get the amount of inches of rain. And it's so interesting to see that some models, like the GFDL and others, when you compare with that rule the numbers on average turn out to be very close. The bottom line is that it's the speed of the system what matters.

Question 3

Have ensemble model predictions changed your process?

Answer:

Here at the National Hurricane Center, what we have been using is another type of ensemble. What we call the "consensus", which is not just the ensemble of one model but the consensus of different models. And, actually, that turns out to be one of the best ways of making a forecast. It appears that, when you make the consensus of different models, you cancel all the biases of the models and you end up having a better forecast by using all these consensus models.

Yes, it changed the process and makes my life, I would say, a little bit easier. But, still, you have to understand why each particular model is doing what it's doing. You cannot use models as a black box, you have to understand what goes into each model.

In many cases here, when we do the verification, the forecaster turns out to be better than all the different independent models. Because we are using all the information and we process it and we get, as I mentioned, we understand the strength and weakness of each model and we try to improve on that.

Question 4

What's your day like when there is a TC to forecast? How is it different if there are a number of TC? Is it any different if the TC are in different basins or in the same basin?

Answer:

Things have changed too, throughout the years. I do remember when we were 5 hurricane specialists and I have to do almost all of them. And now, luckily, we have more specialists and we get help from other colleagues here at the National Hurricane Center. We are prepared to have simultaneous tropical cyclones. Of course, the pressure is tremendous, especially if you have a hurricane that is threatening land areas. If it's a strong hurricane, we have to take care of the media. But, our emphasis is to be able to get all the information and try to make the best forecast possible.

Do I get nervous? No. Do I get excited? Yes! I do get very excited. I have to be in control, but always, what I like to do is emphasize on the science. I don't want to be biased by any of my feelings and I want to do the best job possible. But, it's a very interesting day. It's a very interesting day when you have a tropical cyclone. I presume that it is the same with a doctor when they have somebody that they want to cure or they want to improve their health. I want to make the best forecast.

I've been offered to work for insurance companies. I have been offered to work for TV. But, there is something about being here at the National Hurricane Center; that I have the "power" to name a system. You might think it's not important, but for me, it kind of means that I'm on top of the science and I'm doing this. For me, it's one of the fun things that I get from the job.

Dr. James Franklin, National Hurricane Center Forecaster

Dr. James Franklin

Question 1

How and when did you decide to become a tropical cyclone forecaster? What was your path? Did you do anything special in elementary or high school to further your goals?

Click arrow on the control bar below to hear response.

Question 2

What process do you use to forecast TC? What tools? Does your answer differ between track, intensity, size and rainfall forecasts?

Click arrow on the control bar below to hear response.

Question 3

Have ensemble model predictions changed your process?

Click arrow on the control bar below to hear response.

Question 4

What's your day like when there is a TC to forecast? How is it different if there are a number of TC? Is it any different if the TC are in different basins or in the same basin?

Click arrow on the control bar below to hear response.
Dr. Lixion Avila, National Hurricane Center Forecaster

Dr. Lixion Avila (English)

Question 1

How and when did you decide to become a tropical cyclone forecaster? What was your path? Did you do anything special in elementary or high school to further your goals?

Click arrow on the control bar below to hear response.

Question 2

What process do you use to forecast TC? What tools? Does your answer differ between track, intensity, size and rainfall forecasts?

Click arrow on the control bar below to hear response.

Question 3

Have ensemble model predictions changed your process?

Click arrow on the control bar below to hear response.

Question 4

What's your day like when there is a TC to forecast? How is it different if there are a number of TC? Is it any different if the TC are in different basins or in the same basin?

Click arrow on the control bar below to hear response.
1. Tompkins, A. M., A. Diongue-Niang, D. J. Parker, and C. D. Thorncroft, 2005: The African easterly jet in the ECMWF Integrated Forecast System: 4D-Var analysis. Quart. J. Roy. Meteor. Soc., 131, 2861-2885.
2. Daley, R., 1993: Atmospheric data analysis. Cambridge University Press, 457.
3. World Meteorological Organization (WMO), 2008: Guide to meteorological instruments and methods of observation. 7th edition, WMO-No. 8, Geneva. [Available online at http://www.wmo.int/pages/prog/www/IMOP/publications/CIMO-Guide/CIMO_Guide-7th_Edition-2008.html]
4. --2003: Manual on the Global Observing System. WMO-No. 544, Geneva. [Available online at http://www.wmo.int/pages/prog/www/OSY/Manuals_GOS.html].
5. Parker, D. J., A. Fink, S. Janicot, J. Ngamini, M. Douglas, E. Afiesimama, A. Agusti-Panareda, A. Beljaars, F. Dide, A. Diedhiou, T. Lebel, J. Polcher, J. Redelsperger, C. Thorncroft, and G. Ato Wilson, 2008: The AMMA Radiosonde Program and its implications for the future of atmospheric monitoring over Africa. Bull. Amer. Meteor. Soc., 89, 1015-1027.
6. Rappaport, E. N., R. D. Knabb, C. W. Landsea, M. Mainelli, M. Mayfield, C. J. McAdie, R. J. Pasch, C. Sisko, S. R. Stewart, A. N. Tribble, J. L. Franklin, L. A. Avila, S. R. Baig, J. L. Beven II, E. S. Blake, C. A. Burr, J. Jiing, and C. A. Juckins, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395-419.
7. Caribbean Meteorological Organization, Caribbean Radar Network Project. [Available online at http://www.cmo.org.tt/Projects.htm].
8. Koh, T., C. Teo, 2009: Toward a mesoscale observation network in southeast Asia. Bull. Amer. Meteor. Soc., 90, 481-488.
9. World Meteorological Organization, 1995: Manual on codes, WMO-No. 306, Geneva.
10. Karbou, F., F. Rabier, J. P. Lafore, and J. Redelsperger, 2009: Global 4D-Var assimilation and forecast experiments using AMSU observations over land. Part II: Impact of assimilating surface sensitive channels on the African Monsoon during AMMA. Wea. Forecasting, 25, 20-36.
11. Marshall, J., J. Jung, J. Derber, M. Chahine, R. Treadon, S. Lord, M. Goldberg, W. Wolf, H. Liu, J. Joiner, J. Woollen, R. Todling, P. van Delst, and Y. Tahara, 2006: Improving global analysis and gorecasting with AIRS. Bull. Amer. Meteor. Soc., 87, 891-894.
12. Reale, O., W. K. Lau, J. Susskind, E. Brin, E. Liu, L. P. Riishojgaard, M. Fuentes, and R. Rosenberg, 2009: AIRS impact on the analysis and forecast track of tropical cyclone Nargis in a global data assimilation and forecasting system. Geophys. Res. Lett., 36.
13. Zapotocny, T., J. Jung, J. Le Marshall, and R. Treadon, 2007: A Two-season impact study of satellite and in situ data in the NCEP global data assimilation system. Wea. Forecasting, 22, 887-909.
14. World Meteorological Organization, 2009: Classification of surface observing stations within WIGOS: Classification for performance characteristics. WMO Commission for Instruments and Methods of Observation., [http://www.wmo.int/pages/prog/www/IMOP/meetings/WIGOS-PP-3/Doc-3-2.doc. (Accessed 6 Nov 2009).
15. Daly, C., W. P. Gibson, G. H. Taylor, M. K. Doggett, and J. I. Smith, 2007: Observer bias in daily precipitation measurements at United States cooperative network stations. Bull. Amer. Meteor. Soc., 88, 899-912.
16. Wang, J., H. J. Cole, D. J. Carlson, E. R. Miller, K. Beierle, A. Paukkunen, and T. K. Laine, 2002: Corrections of the humidity measurement error from the Vaisala RS80 radiosonde—Application to TOGA COARE data. J. Atmos. Ocean. Tech., 19, 981-1002.
17. Bock, O., M. -. Bouin, A. Walpersdorf, J. P. Lafore, S. Janicot, F. Guichard, and A. Agusti-Panareda, 2007: Comparison of ground-based GPS precipitable water vapour to independent observations and NWP model reanalyses over Africa. Quart. J. Roy. Meteor. Soc., 133, 2011-2027.
18. Nuret, M., J. Lafore, O. Bock, F. Guichard, A. Agusti-Panareda, J. N'Gamini, and J. Redelsperger, 2008: Correction of humidity bias for Vaisala RS80-A sondes during the AMMA 2006 observing period. J. Atmos. Ocean. Tech., 25, 2152-2158.
19. Uccillini, L. W., S. F. Corfidi, N. W. Junker, P. J. Kocin, and D. A. Olson, 1992: Report on the surface analysis workshop at the National Meteorological Center 25-28 March 1991. Bull. Amer. Meteor. Soc., 73, 459-471.
20. Associated Press, 2001: Floods’ damages at $146 million. [Available online at http://www.puertorico-herald.org/issues/2001/vol5n19/Media1-en.html].
21. Bringi, V., V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar Principles and Applications. Cambridge University Press, 636 pp.
22. Ryzhkov, A., S. Giangrande, and T. Schuur, 2003: Rainfall estimation with a polarimetric prototype of the operational WSR-88D radar (2003 - 31Radar). 31. AMS Int. Conf. on Radar Meteorology, Seattle, WA (USA), 5-12 Aug 2003, Vol. 31, American Meteorological Society.
23. Laing, A. G., 2004: Cases of heavy precipitation and flash floods in the Caribbean during El Nino winters. J. Hydrometeor., 5, 577-594.
24. Vincent, D. G., 1994: The South Pacific Convergence Zone (SPCZ): A review. Mon. Wea. Rev., 122, 1949-1970.
25. Streten, N. A., 1973: Some characteristics of satellite-observed bands of persistent cloudiness over the southern hemisphere. Mon. Wea. Rev., 101, 486-495.
26. Yasunari, T., 1977: Stationary waves in the Southern Hemisphere mid-latitude zone revealed from average brightness charts. J. Meteor. Soc. Japan, 55, 274-285.
27. Kodama, Y., 1992: Large-scale common features of subtropical precipitation zones (the Bai-u frontal zone, the SPCZ, and the SACZ). Part I: Characteristics of subtropical frontal zones. J. Meteor. Soc. Japan, 70, 813-836.
28. Kodama, Y., 1993: Large-scale common features of the subtropical precipitation zones (the Baiu Frontal Zone, the SPCZ and the SACZ). Part 2: Conditions of the circulation for generating the STCZs. J. Meteor. Soc. Japan, 71, 581-609.
29. Madden, R., P. Julian, 1971: Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702-708.
30. Madden, R., P. R. Julian, 1972: Description of global scale circulation cells in the tropics with 40–50 day period. J. Atmos. Sci., 29, 1109-1123.
31. Gottschalck, J., Q. Zhang, W. Wang, M. L'Heureux, and P. Peng, MJO monitoring and assessment at the climate prediction center and initial impressions of the CFS as an MJO forecast tool. [Available online at http://www.weather.gov/ost/climate/STIP/CTB-COLA/Jon_042308.htm].
32. Waliser, D. E., M. W. Moncrieff, 2008: The Year of Tropical Convection (YOTC) science plan: A joint WCRP - WWRP/THORPEX International Initiative.
33. Leroy, A., M. C. Wheeler, 2008: Statistical prediction of weekly tropical cyclone activity in the southern hemisphere. Mon. Wea. Rev., 136, 3637-3654.
34. Wheeler, M., K. Weickmann, 2001: Real-time monitoring and prediction of modes of coherent synoptic to intraseasonal tropical variability. Mon. Wea. Rev., 129, 2677-2694.
35. Sadler, J. C., 1967: The tropical upper tropospheric trough as a secondary source of typhoons and a primary source of trade-wind disturbances. HIG-67-12 and AFCRL-67-0203, 44-Hawaii Institute of Geophysics, University of Hawaii.
36. Sadler, J. C., 1976: A role of the tropical upper tropospheric trough in early season typhoon development. Mon. Wea. Rev., 104, 1266-1278.
37. Krishnamurti, T. N., M. Kanamitsu, R. Godbole, C. B. Chang, F. Carr, and J. H. Chow, 1975: Study of a monsoon depression (I). Synoptic structure. J. Meteor. Soc. Japan, 53, 227-239.
38. Sikka, D., 1977: Some aspects of the life history, structure, and movement of monsoon depressions. Pure Appl. Geophys., 115, 1501-1529.
39. Todd, M., R. Washington, 1999: Circulation anomalies associated with tropical-temperate troughs in southern Africa and the south west Indian Ocean. Climate Dyn., 15, 937-951.
40. Kodama, K. R., S. Businger, 1998: Weather and forecasting challenges in the pacific region of the National Weather Service. Wea. Forecasting, 13, 523-546.
41. Morrison, I., S. Businger, 2001: Synoptic structure and evolution of a Kona Low. Wea. Forecasting, 16, 81-98.
42. Caruso, S., S. Businger, 2006: Synoptic climatology of subtropical cyclogenesis. Wea. Forecasting, 20, 193-205.
43. Douglas, M. W., 1992: Structure and dynamics of two monsoon depressions. Part I: Observed structure. Mon. Wea. Rev., 120, 1524-1547.
44. Chen, T., S. Weng, 1999: Interannual and intraseasonal variations in monsoon depressions and their westward-propagating predecessors. Mon. Wea. Rev., 127, 1005-1020.
45. Garcia, O., L. Bosart, and G. DiMego, 1978: On the nature of the winter season rainfall in the Dominican Republic. Mon. Wea. Rev., 106, 961-982.
46. Garcia, O., 1997: Impacts of ENSO on Cuba. Proc. Contribution to “A Systems Approach to ENSO,” A Colloquium on El Niño–Southern Oscillation (ENSO): Atmospheric, Oceanic, Societal, Environmental, and Policy Perspectives, Boulder, CO, NCAR.
47. Giannini, A., Y. Kushnir, and M. A. Cane, 2000: Interannual variability of Caribbean rainfall, ENSO, and the Atlantic Ocean. J. Climate, 13, 297-311.
48. Wiin-Nielsen, A., 1991: The birth of numerical weather prediction. Tellus. Series A-B, Stockholm, Sweden, 43A-B, 36-52.
49. Zhu, Z., J. Thuburn, B. J. Hoskins, and P. H. Haynes, 1992: A vertical finite-difference scheme based on a hybrid s-y-p coordinate. Mon. Wea. Rev., 120, 851-862.
50. Kain, J. S., S. J. Weiss, D. R. Bright, M. E. Baldwin, J. J. Levit, G. W. Carbin, C. S. Schwartz, M. L. Weisman, K. K. Droegemeier, D. B. Weber, and K. W. Thomas, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931-952.
51. Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 2599-2617.
52. Reynolds, R. W., T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929-948.
53. Tolman, H. L., D. Chalikov, 1996: Source terms in a third-generation wind wave model. J. Phys. Oceanogr., 26, 2497-2518.
54. Parrish, D. F., J. C. Derber, 1992: The National Meteorological Center’s spectral statistical interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
55. Courtier, P., E. Andersson, W. Heckley, J. Pailleux, D. Vasiljevic, M. Hamrud, A. Hollingsworth, F. Rabier, and M. Fisher, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Part I: Formulation. Quart. J. Roy. Meteor. Soc., 124, 1783-1807.
56. Gauthier, P., C. Charette, L. Fillion, P. Koclas, and S. Laroche, 1999: Implementation of a 3D variational data assimilation system at the Canadian Meteorological Centre. Part I: The global analysis. Atmosphere-Ocean, 37, 103-156.
57. Talagrand, O., P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation, Pt. 1, theory. Quart. J. Roy. Meteor. Soc., 113, 1311-1328.
58. Zou, X., F. Vandenberghe, M. Pndeca, and Y. -. Kuo, 1997: Introduction to Adjoint Techniques and the MM5 Adjoint Modeling System. NCAR Tech. Note, NCAR, TN-435+STR, 110 [Available from NCAR, P.O. Box 3000, Boulder, CO 80307-3000.].
59. Kalnay, E., H. Li, T. Miyosho, S. Yang, and J. Ballabrera-Poy, 2007: 4-D-Var or ensemble Kalman filter? Tellus (Dyn. Meteor. Oceanogr.), 59, 758-773.
60. Lee, M., D. Lee, 2003: An application of a weakly constrained 4DVAR to satellite data assimilation and heavy rainfall simulation. Mon. Wea. Rev., 131, 2151-2176.
61. Bormann, N., J. Thepaut, 2004: Impact of MODIS polar winds in ECMWF's 4DVAR data assimilation system. Mon. Wea. Rev., 132, 929-940.
62. Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289-307.
63. Lilly, D. K., 1990: Numerical prediction of thunderstorms—Has its time come? Quart. J. Roy. Meteor. Soc., 116, 779-798.
64. Hall, T., H. E. Brooks, and C. A. Doswell III, 1999: Precipitation forecasting using a neural network. Wea. Forecasting, 14, 338-345.
65. Ganguly, A., R. Bras, 2003: Distributed quantitative precipitation forecasting using information from radar and numerical weather prediction models. J. Hydrometeor., 4, 1168-1180.
66. Ramirez, M., N. J. Ferreira, and H. F. de Campos Velho, 2006: Linear and nonlinear statistical downscaling for rRainfall forecasting over southeastern Brazil. Wea. Forecasting, 21, 969-989.
67. Dahamasheh, A., A. Hafzullah, 2009: Artificial neural network models for forecasting intermittent monthly precipitation in arid regions. Meteor. Applications, 16, 325-337.
68. Toth, Z., E. Kalnay, 1993: Ensemble forecasting at NMC: the generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330.
69. Toth, Z., E. Kalnay, S. M. Tracton, R. Wobus, and J. Irwin, 1997: A synoptic evaluation of the NCEP ensemble. Wea. Forecasting, 12, 140-153.
70. Molteni, F., T. Buizza, N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73-119.
71. Kobayashi, C., K. Yoshimatsu, S. Maeda, and K. Takano, 1996: Dynamical one-month forecasting at JMA. Proc. Preprints, 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 13-14.
72. Rennick, M. A., 1995: The Ensemble Forecast System (EFS). Models Department Tech. Note, 2-95, 19-from Models Department, Fleet Numerical Meteorology and Oceanography Center, 7 Grace Hopper Ave., Monterey, CA 93943.
73. Toth, Z., Y. Zhu, and T. Marchok, 2001: The ability of ensembles to distinguish between forecasts with small and large uncertainty. Wea. Forecasting, 16, 436-477.
74. Du, J., J. S. Tracton, 2001: Implementation of a real-time short-range ensemble forecasting system at NCEP: an update. Proc. 9th Conference on Mesoscale Processes, Ft. Lauderdale, FL, Amer. Meteor. Soc. Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/reference.html, 355-356.
75. Du, J., G. DiMego, Z. Toth, D. Jovic, J. Zhou, J. Zhu, J. Wang, and H. Juang, 2009: Recent upgrade of NCEP short-range ensemble forecast (SREF) system. Proc. 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, Amer. Meteor. Soc. Available online at http://ams.confex.com/ams/pdfpapers/153264.pdf.
76. Park, Y., R. Buizza, and M. Leutbecher, 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134, 2029-2050.
77. Houtekamer, P., H. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
78. Tippett, M., J. Anderson, C. Bishop, T. Hamill, and J. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485-1490.
79. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
80. Etherton, B. J., S. Aberson, 2005: Ensemble based data assimilation of observations of Hurricane Humberto (2005 - 9IOASAOLS). 9. AMS Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, San Diego, CA (USA), 8-14 Jan 2005, Vol. 9, American Meteorological Society.
81. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP - a comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
84. Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493-2525.
82. Wang, X., D. M. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116-5131.
83. Molinari, J., M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: a critical review. Mon. Wea. Rev., 120, 326-344.
85. Gallus, W. A., Jr, 1999: Eta simulations of three extreme precipitation events: Sensitivity to resolution and convective parameterization. Wea. Forecasting, 14, 405-426.
86. Davis, C. A., K. W. Manning, R. E. Carbone, S. B. Trier, and J. D. Tuttle, 2003: Coherence of warm-season continental rainfall in numerical weather prediction models. Mon. Wea. Rev., 131, 2667-2679.
87. Bukovsky, M. S., J. S. Kain, and M. E. Baldwin, 2006: Bowing convective systems in a popular operational model: Are they for real? Wea. Forecasting, 21, 307-324.
88. Betts, A. K., M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and Arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693-709.
89. Kain, J. S., J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
90. Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527-548.
91. Speer, M., L. Leslie, 2002: The prediction of two cases of severe convection: Implications for forecast guidance. Meteor. Atmos. Phys., 80, 165-175.
92. Done, J., C. A. Davis, and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the weather research and forecasting (WRF) model. Atmos. Sci. Lett., 5, 110-117.
93. Grabowski, W. W., X. Wu, and M. W. Moncrieff, 1996: Cloud-resolving modeling of tropical cloud systems during Phase III of GATE. Part I: Two-dimensional experiments. J. Atmos. Sci., 53, 3684-3709.
94. Lawrence, M. B., B. M. Mayfield, L. A. Avila, R. J. Pasch, and E. N. Rappaport, 1998: Atlantic hurricane season of 1995. Mon. Wea. Rev., 126, 1124-1151.
95. Merrill, R. T., 1988: Environmental influences on hurricane intensification. J. Atmos. Sci., 45, 1678-1687.
96. Velden, C. S., L. M. Leslie, 1991: The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea. Forecasting, 6, 244-253.
97. Pasch, R. J., J. Schauer Clark, 2009: Technical summary of the National Hurricane Center track and intensity models. [Available online at http://www.nhc.noaa.gov/modelsummary.shtml].
98. DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements to the statistical hurricane intensity prediction scheme (SHIPS). Wea. Forecasting, 20, 531-543.
99. Buizza, R., 2000: Chaos and weather prediction. [Available online at http://www.ecmwf.int/newsevents/training/rcourse_notes/GENERAL_CIRCULATION/CHAOS/Chaos6.html].
100. Buizza, R., T. N. Palmer, 1995: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 1434-1456.
101. Barkmeijer, J., R. Buizza, T. N. Palmer, K. Puri, and J. -. Mahfouf, 2001: Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc., 127, 685-708.
102. Jeffries, R. A., E. J. Fukada, 2002: Consensus approach to track forecasting. Proc. Extended Abstracts, Fifth Int. Workshop on Tropical Cyclones, Cairns, Australia, World Meteorological Organization, TP3.2.
103. Elsberry, R. L., J. R. Hughes, and M. A. Boothe, 2008: Weighted position and motion vector consensus of tropical cyclone track prediction in the western north Pacific. Mon. Wea. Rev., 136, 2478-2487.
104. Beven, J. L.,II, L. A. Avila, E. S. Blake, D. P. Brown, J. L. Franklin, R. D. Knabb, R. J. Pasch, J. R. Rhome, and S. R. Stewart, 2008: Atlantic hurricane season of 2005. Mon. Wea. Rev., 136, 1109-1173.
105. Goerss, J. S., 2007: Prediction of consensus tropical cyclone track forecast error. Mon. Wea. Rev., 135, 1985-1993.
106. Stanski, H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of common verification methods in meteorology. World Weather Watch Tech. Rept. WMO, Geneva, No.8, WMO/TD No.358, 144, http://cawcr.gov.au/bmrc/wefor/staff/eee/verif/Stanski_et_al/Stanski_et_al.html.
107. von Storch, H., F. W. Zwiers, 1999: Statistical analysis in climate research. Cambridge University Press, 484.
108. Jolliffe, I. T., 2003: Forecast verification. A practitioner's guide in atmospheric science. Wiley and Sons Ltd, 240 pp.
109. Wilks, D. S., 2006: Statistical methods in the atmospheric sciences. 2nd ed. International Geophysics Series, Vol. 59, Academic Press, 627 pp.
110. World Meteorological Organization WWRP/WGNE Joint Working Group on Forecast Verification Research, 2009: Forecast verification - issues, methods and FAQ. [Available online at http://www.cawcr.gov.au/projects/verification/].
111. Jolliffe, I. T., N. Jolliffe, 1997: Assessment of descriptive weather forecasts. Weather, 52, 391-396.
112. Jolliffe, I. T., 2003: How do I verify worded forecasts? [Available online at http://cawcr.gov.au/bmrc/wefor/staff/eee/verif/WordedForecasts.html].
113. Tuleya, R. E., M. DeMaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms. Wea. Forecasting, 22, 56-70.
114. Ebert, E. E., J. L. McBride, 2000: Verification of precipitation in weather systems: determination of systematic errors. J. Hydrol. (Amst.), 239, 179-202.
115. Roberts, N. M., H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78-97.
116. Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proc. Proceedings, ECMWF Workshop on Predictability.
117. Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560.
118. Hewson, T., 2007: The concept of 'Deterministic limit'. Proc. 3rd Intl. Verification Methods Workshop, Reading, UK, http://www.ecmwf.int/newsevents/meetings/workshops/2007/jwgv/workshop_presentations/T_Hewson.pdf.
119. Stephenson, D. B., B. Casati, C. A. T. Ferro, and C. A. Wilson, 2008: The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteor. Appl., 15, 41-50.
120. Puri, K., J. Barkmeijer, and T. N. Palmer, 2001: Ensemble prediction of tropical cyclones using targeted diabatic singular vectors. Quart. J. Roy. Meteor. Soc., 127, 709-731.
121. Veren, D., J. L. Evans, S. Jones, and F. Chiaromonte, 2009: Novel metrics for evaluation of ensemble forecasts of tropical cyclone structure. Mon. Wea. Rev., 137, 2830-2850.
122. Anwender, D., P. A. Harr, and S. C. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Case studies. Mon. Wea. Rev., 136, 3226-3247.
123. Harr, P. A., D. Anwender, and S. C. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Methodology and a case study of Typhoon Nabi (2005). Mon. Wea. Rev., 136, 3205-3225.
124. Trenberth, K. E., D. P. Stepaniak, and J. M. Caron, 2000: The global monsoon as seen through the divergent atmospheric circulation. J. Climate, 13, 3969-3993.
125. Webster, P. J., V. O. Magana, T. N. Palmer, J. Shukla, R. A. Tomas, M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, predictability, and the prospects for prediction. J. Geophys. Res., 103, 14451-14510.
126. Krishnan, R., C. Zhang, and M. Sugi, 2000: Dynamics of breaks in the Indian summer monsoon. J. Atmos. Sci., 57, 1354-1372.
127. Webster, P. J., 2005: The Hadley Circulation: Present, Past and Future. The Elementary Hadley Circulation, H. F. Diaz and R. S. Bradley, Eds., Kluwer Academic Publishers, 9-60.
128. Lindzen, R. S., A. V. Hou, 1988: Hadley circulations for zonally averaged heating centered off the equator. J. Atmos. Sci., 45, 2416-2427.
129. Yano, J., J. L. McBride, 1998: An aquaplanet monsoon. J. Atmos. Sci., 55, 1373-1399.
130. Pope, M., C. Jakob, and M. J. Reeder, 2008: Convective systems of the North Australian Monsoon. J. Climate, 21, 5091-5112.
131. May, P. T., J. H. Mather, G. Vaughan, C. Jakob, G. M. McFarquhar, K. N. Bower, and G. G. Mace, 2008: The tropical warm pool international cloud experiment. Bull. Amer. Meteor. Soc., 89, 629-645.
132. Troup, A. J., 1961: Variations in the upper troposphere associated with the onset of the Australian summer monsoon. Indian J. Meteor. Geophys., 12, 217-230.
133. Hendon, H. H., N. E. Davidson, and B. Gunn, 1989: Australian summer monsoon onset during AMEX 1987. Mon. Wea. Rev., 117, 370-390.
134. McBride, J. L., B. Gunn, G. Holland, T. Keenan, N. Davidson, and W. M. Frank, 1989: Time series of total heating and moistening over the Gulf of Carpentaria radiosonde array during AMEX. Mon. Wea. Rev., 117, 2701-2713.
135. Wheeler, M. C., H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917-1932.
136. Wheeler, M. C., J. L. McBride, 2005: Australian-Indonesian monsoon. Intraseasonal variability in the atmosphere-ocean climate system, Springer-Verlag (Heidelberg), 125-173 pp.
137. Davidson, N. E., K. J. Tory, M. J. Reeder, and W. Drosdowsky, 2007: Extratropical–tropical interaction during onset of the Australian monsoon: Reanalysis diagnostics and idealized dry simulations. J. Atmos. Sci., 64, 3475-3498.
138. Wheeler, M., G. N. Kiladis, and P. J. Webster, 2000: Large-scale dynamical fields associated with convectively coupled equatorial waves. J. Atmos. Sci., 57, 613-640..
139. Drosdowsky, W., 1996: Variability of the Australian summer monsoon at Darwin: 1957-1992. J. Climate, 9, 85-96.
140. Keenan, T., R. Carbone, 1992: A preliminary morphology of precipitation systems in tropical northern Australia. Quart. J. Roy. Meteor. Soc., 118, 283-326.
141. Pope, M., C. Jakob, and M. J. Reeder, 2009: Regimes of the north Australian wet season. J. Climate, 22, 6699-6715.
142. McBride, J. L., W. M. Frank, 1999: Relationships between stability and monsoon convection. J. Atmos. Sci., 56, 24-36.
143. Miller, D., J. M. Fritsch, 1991: Mesoscale Convective Complexes in the western Pacific region. Mon. Wea. Rev., 119, 2978-2992.
144. Chappel, L-C., and B.N. Hanstrum, 1998: A severe dry microburst over central Australia. Colleged papers of severe storms in Australia. 6th Australian Severe Storms Conference, Bureau of Meteorology, Western Australia, August 1998.
145. Wilson, J. W., R. E. Carbone, J. D. Tuttle, and T. D. Keenan, 2001: Tropical island convection in the absence of significant topography. Part II: Nowcasting storm evolution. Mon. Wea. Rev., 129, 1637-1655.
146. Carbone, R. E., J. W. Wilson, T. D. Keenan, and J. M. Hacker, 2000: Tropical island convection in the absence of significant topography. Part I: Life cycle of diurnally forced convection. Mon. Wea. Rev., 128, 3459-3480.
147. Chang, C., Z. Wang, J. McBride, and C. Liu, 2005: Annual cycle of southeast Asia-maritime continent rainfall and the asymmetric monsoon transition. J. Climate, 18, 287-301.
148. Haylock, M., J. McBride, 2001: Spatial coherence and predictability of Indonesian wet season rainfall. J. Climate, 14, 3882-3887.

A

Absolute angular momentum
For the atmosphere, the absolute angular momentum, per unit mass of air, is the sum of the angular momentum relative to the earth and the angular momentum due to the rotation of the earth.
Absolute vorticity
See Vorticity.
Absorber
Anything that retains incident electromagnetic radiation due its physical composition.
Absorption
The process by which incident radiant energy is retained by a material due to the material's physical composition.
Absorption band
A portion of the electromagnetic spectrum where radiation is absorbed and emitted by atmospheric gases such as water vapor, carbon dioxide, and ozone.
African easterly wave
A trough or cyclonic curvature maximum in the trade-wind easterlies. The wave may reach maximum amplitude in the lower middle troposphere.
Aggregation
The clumping together of ice crystals after they collide.
Anomaly
The deviation of a quantity over a specified period from the normal value for the same region. For example, El Niño is identified by sea surface temperature anomalies.
Atlantic Multidecadal Oscillation (AMO)
A natural oscillation of the North Atlantic SST between warm and cool phases. The SST difference between these warm and cool phases is about 0.5°C and the period of the oscillation is roughly 20-40 years (the period is variable, but is a few decades long). Evidence suggests that the AMO has been active for at least the last 1,000 years.
Attenuation
Any process in which the intensity of radiation decreases due to scattering or absorption.
Atmospheric Window
A portion of the electromagnetic spectrum where radiation passes through the atmosphere without absorption by atmospheric gases such as water vapor, carbon dioxide, and ozone.
Available potential energy (APE)
The portion of the total potential energy available for adiabatic conversion to kinetic energy. The total potential energy is a combination of the APE and the potential energy representing the mass distribution needed to balance the mean atmospheric motions.

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B

Backscatter
That portion of radiation scattered back toward the source.
Baroclinic
Dependence on the horizontal temperature contrast between warm and cold air masses., In a baroclinic atmosphere, the geostrophic wind varies with height in direction as well as speed and its shear is a function of the horizontal temperature gradient (the thermal wind equation).
Barotropic
The atmosphere has the same horizontal structure at all levels in the vertical. This is equivalent to the absence of horizontal temperature gradients.
Barotropic-Baroclinic Instability
Barotropic and baroclinic instability analyses are used to explain the growth of a small perturbation to the flow. A perturbation growing due to baroclinic instability draws its energy from the available potential energy (APE). A perturbation growing due to barotropic instability draws its energy from the kinetic energy of the background flow. A perturbation growing through both APE and mean kinetic energy conversion to kinetic energy of the growing system (intensifying the system) is developing through combined barotropic baroclinic instability.
Best track
As defined by the National Hurricane Center, it is a subjectively-smoothed representation of a tropical cyclone's location and intensity over its lifetime. The best track contains the cyclone's latitude, longitude, maximum sustained surface winds, and minimum sea-level pressure at 6-hourly intervals. Best track positions and intensities, which are based on a post-storm assessment of all available data, may differ from values contained in storm advisories. They also generally will not reflect the erratic motion implied by connecting individual center positions fixed during operations.
Beta (β) effect
Denotes how fluid motion is affected by spatial changes of the Coriolis parameter, for example, due to the earth's curvature. The term takes its name from the symbol β representing the meridional gradient of the Coriolis parameter at a fixed latitude. The asymmetric flows resulting from the interaction of the vortex with the changing Coriolis parameter is known as the β-gyres.
Beta (β) plane
An approximation of the Coriolis parameter in which f = f0 + βy, where β is a constant. The Coriolis parameter is assumed to vary linearly in the north-south direction. The term takes its name from the symbol β representing the meridional gradient of the Coriolis parameter at a fixed latitude.
Blackbody
An object that absorbs all incident radiation and emits the maximum amount of energy at all wavelengths.
Blended precipitation estimate
An estimate that is derived by combining low earth-orbiting microwave measurements, which have high resolution but low frequency, with the more frequently available geostationary IR.
Bow echo
An organized mesoscale convective system, so named because of its characteristic bow shape on radar reflectivity displays. Bow echoes are typically 20–200 km long and last for 3–6 hours. They are associated with severe weather, especially high, straight-line surface winds, which are the result of a strong rear-inflow jet descending to the surface.
Brightness temperature
The Planck temperature associated with the radiance for a given wavelength.

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C

Center
Location of the vertical axis of a tropical cyclone, usually defined by the location of minimum wind or minimum pressure. The cyclone center position can vary with altitude.
Cloud track winds
Winds derived from tracking movement of cloud elements using IR and water vapor images from geostationary satellites.
Conditional Instability of the Second Kind (CISK)
A theory for tropical cyclone development that relates boundary layer moisture convergence (driven by Ekman pumping) to the potential for tropical cyclone intensification. As the storm intensifies, the moisture convergence must increase, providing a feedback to the system. As with WISHE, CISK relies on the presence of an incipient disturbance.
Coordinated Universal Time (UTC)
Same as Zulu (Z) and Greenwich Mean Time (GMT).
Coriolis parameter, f
A measure that is twice the local vertical component of the angular velocity of a spherical planet, 2Ω sinφ, where Ω is the angular speed of the planet and φ is the latitude.
Cyclogenesis
The formation of a cyclone.
Cyclone
An closed circulation of low pressure, rotating counter-clockwise in the Northern Hemisphere and clockwise in the SH.
Cyclone Phase Space (CPS)
A concise, three-parameter summary of the structure of a storm. It can be used to describe the structure of any synoptic or meso-synoptic cyclone.

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D

Deposition
The process by which molecules are changed from the vapor phase directly to the solid phase, such as from water vapor to ice.
Doppler Effect
The apparent shift in the frequency and wavelength of a wave perceived by an observer moving relative to the source of the wave.
Doppler radar
Radar that uses the Doppler effect to detect radial velocity of targets based on the phase shift between the transmitted pulse and the received backscatter.
Dvorak Technique
a classification scheme for estimating the intensity of TCs from enhanced IR and visible satellite imagery. It is the primary method of estimating intensity everywhere, except the North Atlantic and North Pacific where aircraft reconnaissance is routine.

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E

Eddy angular momentum flux (EAMF)
Flux (net transport) of angular momentum into a circle centered on the storm. If EAMF is positive, the flow inside the circle will become more cyclonic; negative EAMF render the system less cyclonic (more anticyclonic). See Box 8-6 for a definition and discussion of angular momentum in tropical cyclones.
Ekman layer
Thin horizontal layer of water at top of the ocean that is affected by wind.  That layer has a force balance between pressure gradient force, Coriolis force and frictional drag.
Ekman pumping
The force balance determining the vector wind is modified by friction at the Earth's surface. The addition of friction changes the force balance to slow the winds and change their direction: winds now flow into a low and out of a high pressure system. Winds flowing into a low because of friction are forced upwards and out of the boundary layer. This process is known as Ekman pumping.
El Niño-Southern Oscillation (ENSO)
An oscillation of the ocean-atmosphere system in the tropical Pacific which affects global  weather and climate. El Niño, the warm phase of ENSO, is a quasi-periodic (2-7 years) warming of ocean surface waters in the equatorial and eastern tropical Pacific and an eastward shift in convection from the western Pacific climatological maximum. Changes occur in the tropical trade easterlies, vertical wind shear,  and ocean height. Cool ocean temperature anomalies are observed in the tropical western Pacific extending eastward into the subtropics of both hemispheres. "La Niña" refers to the less intense, anomalous  cool phase of ENSO. The Southern Oscillation refers to the atmospheric pressure difference between Darwin and Tahiti that is correlated with El Niño.
Electromagnetic (EM)
Energy carried by electric and magnetic waves.
Emission
The process by which a material generates electromagnetic radiation due to its temperature and composition.
Emissivity
The emitting efficiency of an object compared to an ideal emitter (or blackbody). A blackbody has an emissivity of one.
Emitter
Anything that radiates measurable electromagnetic radiation.
Empirical Orthogonal Function (EOF)
See Principal Component Analysis.
Energy
The capacity to do work or transfer heat. Measured in SI units as Joules.
Entrainment
The integration of unsaturated environmental air into the turbulent cloud-scale circulation. The antonym of entrainment is detrainment.
Explosive Deepening
A decrease in the minimum sea-level pressure of a tropical cyclone of 2.5 hPa hr-1 for at least 12 hours or 5 hPa hr-1 for at least six hours.
Extratropical

A term used to indicate that a cyclone has lost its “tropical” characteristics. The term implies both poleward displacement of the cyclone and the conversion of the cyclone’s primary energy source from the release of latent heat of condensation to baroclinic processes.

It is important to note that cyclones can become extratropical and still retain winds of hurricane or tropical storm force. Given that these dangerous winds can persist after the cyclone is classified as extratropical, the Canadian Hurricane Centre (for example) follows them as “Former hurricane XXX.”

Extratropical Transition (ET)
The evolution of a poleward-moving initially tropical cyclone resulting in an extratropical cyclone. In the process of this evolution the energy source of the storm shifts from latent heat release to baroclinic development.
Eye (of tropical cyclone)
The approximately circular area of light winds at the center of a tropical cyclone. It is surrounded entirely or partially by clouds in the eyewall.
Eyewall / Wall Cloud
The full or partial ring of thunderstorms that surround the eye of a tropical cyclone. The strongest sustained winds in a tropical cyclone occur in the eyewall.

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F

Field of View (FOV)
Generally associated with the ground resolution from the detector standard viewing location, field of view is the solid angle through which a detector observes radiation.
Fraction of Photosynthetically Active Radiation (FPAR)
An index that measures how much sunlight the leaves are absorbing.
Frequency
The number of recurrences of a periodic phenomenon per unit time. The frequency, v, of electromagnetic energy is usually specified in Hertz (Hz), which represents one cycle per second.
Fujiwhara Effect
The mutual advection of two or more nearby tropical cyclones about each other. This results in cyclonic rotation of the storms about each other.

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G

Gale Force Wind
A sustained surface wind in the range 17 m s-1 (39 mph, 63 km hr‑1 or 34 knot) to 24 m s-1 (54 mph, 87 km hr‑1 or 47 knot) inclusive, and not directly associated with a tropical cyclone.
Geostationary or Geosynchronous orbit
An orbit whose rotation period equals that of the Earth. The altitude of a geostationary orbit is approximately 35,800 km. Its orbit keeps it above a single point on the equator.
GOES
Geostationary Operational Environmental Satellite (operated by NOAA).
GOES Precipitation Index
An estimate of precipitation that uses 235K as the IR temperature with the best correlation to average precipitation for areas spanning 50-250 km over 3-24 hours.
GPS
Global Positioning System, a network of defense satellites established in 1993. Each satellite broadcasts a digital radio signal that includes its own position and the time, accurate to one billionth of a second. GPS receivers use the signals to calculate their position to with a few hundred feet.
GPS radio occultation
The technique by which satellite receivers intercept signals from GPS and infer the deviations in the signal's path caused by temperature and moisture gradients.
Gravity waves
Oscillations usually of high frequency and short horizontal scale, relative to synoptic- scale motions, which arise in a stably stratified fluid when parcels are displaced vertically. Gravity is the restoring force.
Greenwich Mean Time (GMT)
Mean solar time of the meridian at Greenwich, England, used as the basis for standard time throughout most of the world. Also referred to as Zulu (Z) and Coordinated Universal Time (UTC).

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H

Hadley Cells
Circulation cells in which air rises in the ITCZ, sinks into the subtropical highs, and returns to the equatorial low along the trade winds. George Hadley proposed a model (1735) of the global atmospheric circulation with rising motion at the equator, where there is surplus heating, and sinking motion at the poles, where there is net cooling. Hadley's model did not account for the Coriolis effect, which leads to average westerly motion in the mid-latitudes. The Hadley model does explain the circulation within 30 degrees of the equator.
Horizontal Convective Rolls
Lines of overturning motion with axes parallel to the local surface. These rolls result from a convective instability (high density over low density – often corresponding to cool air over warm) and can mix strong winds from above down towards the surface.
Hurricane
A tropical cyclone in which the maximum sustained surface wind (using the local time averaging convention) is at least 33 m s-1 (74 mph, 119 km hr-1 or 64 knot). The term "hurricane" is used for in the Northern Atlantic and Northeast Pacific; "tropical cyclone" east of the International Dateline to the Greenwich Meridian; and "typhoon" in the Pacific north of the Equator and west of the International Dateline.

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I

Inertial period
The time taken to complete one rotation. In the tropical cyclone this is calculated by dividing the circumference at the radius of interest (commonly, the radius of maximum winds) by the wind speed at that radius.
Infrared (IR)
Electromagnetic energy within the wavelength interval generally defined from 0.7 to 100 microns.
Irradiance
The energy per unit time incident upon a unit area of a given surface, measured in SI units as Wattsm-2.
Insolation
The incoming solar radiation that reaches the earth and its atmosphere.
Intensity
The peak sustained surface wind in the region immediately surrounding the storm center, or the minimum central pressure measured in the eye.
Intertropical Convergence Zone (ITCZ)
The zone where the northeast and southeast trade winds converge. It is marked by low pressure, rising motion, and thunderstorms, which occur with strong surface heating. Its latitudinal position shifts in response to the solar maximum and heating response of the surface. It is recognized in satellite images as a band of thunderstorms across the tropics. It is often, but not always, co-located with the zone of low pressure known as the "Equatorial Trough".
Intraseasonal
Varying on time scales shorter than one season.

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J

Joule
SI unit of energy equal to 0.2389 calories.

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K

Kelvin waves
At the equator, eastward propagating waves with negligible meridional velocity component and Gaussian latitudinal structure in zonal velocity, geopotential, and temperature, symmetric about the equator.

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L

Landfall
The intersection of the surface center of a tropical cyclone with a coastline. Because the strongest winds in a tropical cyclone are not located precisely at the center, it is possible for the strongest winds to be experienced over land even if landfall does not occur.
Leaf Area Index (LAI)
The ratio of green leaf area to the total surface area occupied by vegetation.
Longwave (LW)
Electromagnetic energy lying in the wavelength interval generally defined from 4.0 microns to an indefinite upper limit.
Low earth orbit (LEO)
An orbit that is located at an altitude generally between 200 and 1000 km.
Low earth orbit satellite
A satellite that has a low earth orbit. Most have paths crossing the poles and can provide synchronous observations (e.g., the NOAA series or Defense Meteorological Satellite Program systems). The TRMM is an LEO satellite that orbits between ±35º latitude.

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M

Madden-Julian Oscillation (MJO)
Tropical rainfall exhibits strong variability on time scales shorter than the seasonal. These fluctuations in tropical rainfall often undergo a 30-60 day cycle that is referred to as the Madden-Julian Oscillation or intraseasonal oscillation. The MJO is a naturally occurring component of the Earth's coupled ocean-atmosphere system that significantly affects the atmospheric circulation throughout the global tropics and subtropics.
Maritime Continent
The region of Southeast Asia that comprises many islands, peninsulas, and shallow seas (including countries such as Indonesia, Malaysia, Papua New Guinea, and the Phillipines and covers approximately 12°S to 8°N, 95°E to 150°E).
Meridional
North-south, crossing latitudes; by convention the meridional wind from the south is positive.
Mesoscale
Spatial scale of 100-1000 km and temporal scale of hours to a day; between synoptic and convective scale. Tropical clouds are most often organized into mesoscale systems.
Mesoscale convective complex (MCC)
A large, quasi-circular mesoscale convective system that produces heavy rainfall and severe weather. In some MCCs, a mid-tropospheric vortex forms and remains after the deep convection has dissipated.
Mixed Rossby-Gravity (MRG) Wave
A divergent Rossby wave, resulting from conservation of potential vorticity and buoyancy forcing. These waves propagated westward along the equator. Meridional velocity is symmetric about the equator. Zonal wind, temperature, and geopotential area antisymmetric about the equator.
Monochromatic
Of or pertaining to a single wavelength, or in practice, perhaps a very narrow spectral interval.
Monsoon
A term whose roots are from the Arabic for "season", it is a seasonal wind reversal. The monsoon has inflow to a surface heat low and an offshore flow from high pressure during the winter when the land cools relative to the ocean. The Indian monsoon is the most prominent but it has been recognized that that monsoon region extends from Southeast Asia to West Africa. The summer monsoon is a vital source of moisture; its arrival, duration, and amount of precipitation modulates the economies of these regions.
Monsoon Gyre
A closed, symmetric circulation at 850 hPa with horizontal extent of 25° latitude that persists for at least two weeks. The circulation is accompanied by abundant convective precipitation around the south-southeast rim of the gyre.
Monsoon Region
Refers to the combination of features including a monsoon trough, confluence zone, and the ITCZ.

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N

Nadir
The satellite viewing angle directly downward (viewing zenith angle = 0 degrees). Also used to refer to the sub-satellite point location.

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0

Ocean conveyor belt
The name given to summarize the pattern of global ocean currents. The surface ocean currents generally transport warm salty water polewards, out of the tropics. The water cools as it moves polewards, becoming increasingly dense (remember that salty water is more dense than fresh water). This water sinks in the North Atlantic and also in the Southern Ocean near Antarctica. The deep water currents transport the water around the globe until it rises to the surface again, once more part of the surface ocean currents.
Opaque
A physical description of a material which attenuates electromagnetic radiation.
Optical depth
A measure of the cumulative attenuation of a beam of radiation as a result of its travel through the atmosphere.

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P

Pacific Decadal Oscillation (PDO)
The PDO is a basin-scale pattern of Pacific climate variability; PDO climate anomalies are most visible in the North Pacific and North American regions, with secondary features in the tropics. The phases of the PDO persist for 20-to-30 years. Causes for the PDO have not yet been explained.
Planck's Law
An expression for the variation of monochromatic radiance as a function of wavelength for a blackbody at a given temperature.
Planetary Boundary Layer (PBL)
The layer of the atmosphere that extends upward from the surface to heights of 100 to 3000 m. The boundary layer is directly influenced by surface forcing such as friction, heating, and evapotranspiration.
Polar orbit
An orbit whose path crosses the polar regions. This type of orbit is located at an altitude generally between 200 and 1000 km, and can provide sun-synchronous observations.
Polar Orbiting Environmental Satellite (POES)
A satellite which has a polar orbit, such as the NOAA series or Defense Meteorological Satellite Program systems.
Potential evapotranspiration
A measure of the maximum possible water loss from an area under a specified set of weather conditions.
Potential Intensity (PI)
The largest possible intensity (maximum wind, minimum pressure) expected to be possible for a particular tropical cyclone.
Potential vorticity
A scalar measure of the balance between the vorticity and the thermal structure of the atmosphere.
Principal component analysis
A mathematical technique for identifying patterns in data by reducing multidimensional data to a smaller number of dimensions. A number of variables that are (possibly) correlated are transformed into a new coordinate system. The transformation identifies the components that account for variability in the data. The first principal component often accounts for the most of variability in the data. Also known as Empirical Orthogonal Function (EOF) analysis.

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Q

Quasi-Biennial Oscillation (QBO)
An oscillation in the lower stratospheric zonal winds averaged around the equator. It is typically diagnosed from the zonal winds between 30-70 hPa (although it is evident as high as 10 hPa). The QBO has a varying from about 24 to 30 months. The zonal winds change by about 40 m s-1 between the maximum easterly and maximum westerly phase.

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R

Radar (Radio Detection And Range)
An instrument that detects objects remotely by transmitting high-frequency pulses to the atmosphere and measuring the "backscatter" or echoed pulses from that object. Weather radar transmits microwave (mm-cm) pulses; the returned signal is interpreted to determine where it is precipitating.
Radiance
A measure of radiant intensity produced by a material in a given direction and per unit wavelength interval, measured in Watts/m 2 /steradian/micron. Monochromatic radiance is the most fundamental unit measured by satellite instruments.
Radiation
Energy transferred by electromagnetic waves.
Radius of Maximum Winds
The distance from the center of a tropical cyclone to the location of the cyclone's maximum winds. In well-developed systems, the radius of maximum winds is generally found at the inner edge of the eyewall.
Rapid Deepening
A decrease in the minimum sea-level pressure of a tropical cyclone of 1.75 hPa hr-1 or 42 hPa for 24 hours.
Recurvature
The poleward motion of a tropical cyclone taking it from the mean tropical easterlies to the midlatitudes westerlies. This change in the advection of the storm results in curvature in the storm track.
Reflection
The process by which incident radiation is scattered in the backward direction (backscattered).
Reflectivity
The fraction of incident radiation reflected by a material.
Relative vorticity
See Vorticity.
Remnant Low
Used for systems no longer having the sufficient convective organization required of a tropical cyclone (e.g., the swirls of stratocumulus in the eastern North Pacific).
Retrieval
The process or end result of a process where physical quantities such as water vapor, temperature, and/or pressure are extracted from measurements of total upwelling radiance to space; here involving the GOES sounder.
Riming
The formation of ice by the rapid freezing of supercooled water drops as they impinge upon an object such as an ice crystal or aeroplane wing.
Rossby Radius of Deformation
The Rossby radius is the critical scale at which rotation becomes as important as buoyancy, which allows an initial disturbance to be sustained. It is a function of the absolute vorticity, stability, and depth of the disturbance. When a disturbance is wider than LR, it will persist; systems that are smaller than LR will dissipate.
Rossby Wave
A planetary wave, resulting from conservation of potential vorticity. Gradients of potential vorticity provide a restoring mechanism to allow propagation of the waves. This text focuses on Rossby waves centered on the equator equatorial (n=1) Rossby waves.

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S

Saffir-Simpson scale
A scale that links the observed damage and the effects of wind, pressure and storm surge that could lead to such damage. Initial wind damage scale was defined by Herbert Saffir and later expanded by Robert Simpson to include storm surge.
Scattering
The process by which a material interacts with and redirects incident radiation (in any given direction).
Scatterometer
A radar that infers near-surface wind velocity by sending pulses of microwave energy to the ocean surface and measuring the backscatter from small-scale waves. Scatterometry wind retrievals can be ambiguous during rain, since rain creates additional backscatter and attenuates the radar beam.
Shortwave (SW)
Electromagnetic radiation generally defined as having a wavelength shorter than 4.0 microns.
Size
The mean radius of a tropical cyclone enclose by winds of at least 17 m s-1. Size may also be defined as the outer closed isobar of the surface pressure.
Solar declination angle
The angle between the rays of the Sun and the equatorial plane of the Earth. It is zero during an equinox and 23.5° during a solstice.
Southern Oscillation Index (SOI)
The normalized difference in sea level pressure between Darwin, Australia and Tahiti, French Polynesia.
Specific humidity
The mass of water vapor per unit mass of air (including water vapor), usually denoted by q and measured in units of grams per kilograms.
Spectral
A descriptor for radiometric quantities or measurements which have a limited wavelength range.
Split window
A pair of regions of the electromagnetic spectrum which are closely located in wavelength, but have slightly different attenuation characteristics. Used to denote the 11- and 12-micron regions in which greater water vapor attenuation at 12 microns causes slightly different brightness temperatures.
Stefan-Boltzmann Law
The energy emitted per unit area (from all wavelengths and represented by the area under the blackbody curve) is proportional to the 4 th power of the absolute temperature
Steradian
The unit of measure of solid angles, equal to the angle subtended at the center of a sphere.
Storm Surge
An abnormal rise in sea level accompanying a tropical cyclone or other intense storm, and whose height is the difference between the observed level of the sea surface and the level that would have occurred in the absence of the cyclone. Storm surge is usually estimated by subtracting the normal or astronomic high tide from the observed storm tide.
Storm Tide
The actual level of sea water resulting from the astronomic tide combined with the storm surge.
Subtropical Cyclone

A non-frontal low pressure system that has characteristics of both tropical and extratropical cyclones.

The most common type is an upper-level cold low with circulation extending to the surface layer and maximum sustained winds generally occurring at a radius of about 100 miles or more from the center. In comparison to tropical cyclones, such systems have a relatively broad zone of maximum winds that is located farther from the center, and typically have a less symmetric wind field and distribution of convection.

A second type of subtropical cyclone is a mesoscale low originating in or near a frontolyzing (dying frontal) zone of horizontal wind shear, with radius of maximum sustained winds generally less than about 50 km (30 miles). The entire circulation may initially have a diameter less than 160 km (100 miles). These generally short-lived systems may be either cold core or warm core.

Subtropical Depression
A subtropical cyclone in which the maximum sustained surface wind speed does not exceed 17 m s-1 (39 mph, 63 km hr‑1 or 34 knot).
Subtropical Storm
A subtropical cyclone in which the maximum sustained surface wind speed is at least 17 m s-1 (39 mph, 63 km hr‑1 or 34 knot).
Synthetic Aperture Radar (SAR)
Works like other radars except that it has very fine resolution in the azimuthal direction. It synthesizes the fine resolution normally achieved with a large antenna by combining signals from an object along a radar flight track and processing the signals as if obtained simultaneously from a single large antenna. The distance over which the signals are collected is known as the synthetic aperture.

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T

Trade Winds
Prevailing easterly winds flowing from the subtropical highs that affect equatorial and subtropical regions. Trade winds are mostly east to northeasterly in the Northern Hemisphere and east to southeasterly in the Southern Hemisphere. During the monsoon, easterly trades are replaced by mostly westerly winds.
Transmission
The process by which incident radiation propagates forward through a material.
Transpiration
The process by which water vapor enters the atmosphere through the stomata in the leaves of plants.
Thermocline
The inversion layer separating the near-surface warm waters from the colder, deeper layers of oceans and lakes.  It is about 1km deep and is thermally stratified. In the ocean, it also separates the fresher waters near the surface from the saltier waters below.
Tropical Cyclone
A warm-core non-frontal synoptic-scale cyclone, originating over tropical or subtropical waters, with organized deep convection and a closed surface wind circulation about a well-defined center. Once formed, a tropical cyclone is maintained by the extraction of heat energy from the ocean at high temperatures and heat export at the low temperatures of the upper troposphere. In this they differ from extratropical cyclones, which derive their energy from horizontal temperature contrasts in the atmosphere (baroclinic effects). Also see Hurricane.
Tropical Cyclone Season
The portion of the year having a relatively high incidence of tropical cyclones. Also known as "Hurricane Season" or "Typhoon Season".
Tropical Depression
A tropical cyclone in which the maximum sustained surface wind speed is not more than 17 ms-1 (39 mph, 63 km hr‑1 or 34 knot).
Tropical Disturbance
A discrete tropical weather system of apparently organized convection – generally 185 to 550 km (100-300 n mi) in diameter – originating in the tropics or subtropics, having a nonfrontal migratory character, and maintaining its identity for 24 hours or more. It may or may not be associated with a detectable perturbation of the wind field.
Tropical Storm
A tropical cyclone in which the maximum sustained surface wind speed ranges from 17 ms-1 (39 mph, 63 km hr‑1 or 34 knot) to 33 ms-1 (74 mph, 119 km hr-1, 64 knot).
Typhoon
See Tropical Cyclone and Hurricane.

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U

Ultraviolet (UV)
Electromagnetic radiation of shorter wavelength than visible radiation but longer than x-rays (approximately 0.03 to 0.4 microns)

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V

Visible
The region of the electromagnetic spectrum which is detectable to the human eye (approximately 0.4 to 0.7 microns).
Vorticity
The local rotation of the flow, calculated as the the curl (cross product) of the vector wind. Vorticity has units of inverse seconds (s-1).

“Relative vorticity” is the vorticity calculated for the observed winds. It is called “relative” since the winds are the flow relative to the Earth’s rotation.
The vertical component of the vorticity vector is most often used since it is much larger than the other vorticity components. This is because the horizontal winds in tropical cyclones are much greater than the vertical wind component.

“Absolute vorticity” is the vorticity calculated for the total motion of the atmosphere the combination of the observed winds and the Earth’s rotation.

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W

Walker Circulation
The east-west circulation cells that form along the equator in response to differential surface heating.
Warning
A warning that sustained winds exceeding the threshold for either tropical storm or tropical cyclone and associated with such a storm are expected in a specified coastal area in 24 hours or less.
Watch
An announcement for specific coastal areas that either tropical storm or tropical cyclone conditions are possible within 36 hours.
Wavelength
The distance a wave will travel in the time required to generate 1 cycle, denoted by λ. A length measured from the midpoint of a crest (or trough) to the midpoint of the next crest (or trough).
Wavenumber
The reciprocal of the wavelength, denoted by κ.
Water Vapor Channel (or water vapor IR channel)
A spectral band in which the radiance is attenuated by water vapor. This usually refers to the 6.7 micron channel in this module.
Weighting function
A mathematical expression representing the relative radiance contribution provided from a given level of the atmosphere (usually a function of atmospheric pressure).
Wind-Induced Surface Heat Exchange (WISHE)
A tropical cyclone development theory based on a conceptual model of a tropical cyclone as an atmospheric Carnot engine. Consistent with its Carnot engine roots, WISHE relates (i) fluxes of heat and moisture from the ocean surface and (ii) the temperature of the tropical cyclone outflow layer to the potential for continued storm development. The fluxes increase with surface wind speed providing a feedback to the system. As with CISK, WISHE relies on the presence of an incipient disturbance.
Wind profiler
Vertically pointing radar which operates on the same principle as horizontally-scanning Doppler radar; provides best measurements of vertical air motion inside convective storms
Wien's Displacement Law
The wavelength of maximum blackbody emission is inversely proportional to its absolute temperature.

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X

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Y

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Z

Zonal
East-west, crossing longitudes; by convention, the zonal wind from the west is positive.
Zulu (Z)
Used to represent the same clocktime at GMT and UTC. See Greenwich Mean Time (GMT), or Coordinated Universal Time (UTC)

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