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Estimating Tropical Cyclone Intensity from Infrared Image Data

MIGUEL F. PINEROS

College of Optical Sciences, The University of Arizona, Tucson, Arizona

ELIZABETH A. RITCHIE

Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

J. SCOTT TYO

College of Optical Sciences, The University of Arizona, Tucson, Arizona

(Manuscript received 20 December 2010, in final form 28 February 2011)

ABSTRACT

This paper describes results from a near-real-time objective technique for estimating the intensity of

tropical cyclones from satellite infrared imagery in the North Atlantic Ocean basin. The technique quantifies

the level of organization or axisymmetry of the infrared cloud signature of a tropical cyclone as an indirect

measurement of its maximum wind speed. The final maximum wind speed calculated by the technique is an

independent estimate of tropical cyclone intensity. Seventy-eight tropical cyclones from the 2004–09 seasons

are used both to train and to test independently the intensity estimation technique. Two independent tests are

performed to test the ability of the technique to estimate tropical cyclone intensity accurately. The best results

from these tests have a root-mean-square intensity error of between 13 and 15 kt (where 1 kt ’ 0.5 m s21) for

the two test sets.

1. Introduction

Tropical cyclones (TC) form over the warm waters of

the tropical oceans where direct measurements of their

intensity (among other factors) are scarce (Gray 1979;

McBride 1995). In general, the primary sources of ob-

servations for these intense vortical weather systems are

from satelliteborne instruments (e.g., Ritchie et al. 2003;

Velden et al. 2006b). Although these instruments provide

many observations, including winds at various levels of

the atmosphere and temperature and humidity sound-

ings, among others, none of these include direct measure-

ments of the maximum wind speed or minimum sea level

pressure intensity of a tropical cyclone.

Because of the lack of direct in situ measurements of

tropical cyclone intensity, several techniques have been

developed to estimate the intensity based on indirect

factors. The most-used technique in operation to

estimate the intensity of tropical cyclones was developed

by V. Dvorak in the 1970s during the early years of

satellites (Dvorak 1975). In this technique, an analyst

classifies the cloud scene types in visible and infrared

satellite imagery and applies a set of rules to calculate

the intensity estimate. The original Dvorak technique is

subjective, is time intensive, and relies on the expertise

of the analyst, but it is still used as the primary intensity

forecasting tool in many tropical cyclone forecasting

centers around the world (e.g., Velden et al. 1998, 2006b;

Knaff et al. 2010). Velden et al. (1998) introduced the

difference of temperature between 1) the warmest pixel

temperature near the eye of the tropical cyclone and 2)

the coldest of the warmest pixel temperatures found on

concentric rings around the center. This modification is

known as the objective Dvorak technique, and, although

the intensity is objectively calculated, the location of the

eye of the tropical cyclone must still be determined by an

expert or by using external sources. Olander and Velden

(2007) developed the advanced Dvorak technique (ADT),

which introduces new procedures in making an intensity

estimate from satellite-based imagery rather than sim-

ulating the original Dvorak technique. One of the most

Corresponding author address: Miguel F. Pineros, PAS Bldg.,

Rm. 542, P.O. Box 210081, The University of Arizona, Tucson, AZ

85721-0081.

E-mail: [email protected]

690 W E A T H E R A N D F O R E C A S T I N G VOLUME 26

DOI: 10.1175/WAF-D-10-05062.1

� 2011 American Meteorological Society

important improvements of the ADT consists of the in-

troduction of regression equations to estimate the tropical

cyclone intensity. Kossin et al. (2007) recently described

a new satellite-based technique in which the radius of

maximum wind, the critical wind radii, and the two-

dimensional surface wind field are estimated from in-

frared (IR) imagery. This technique uses 12-h mean IR

imagery and best-track position data to estimate the

two-dimensional wind fields, which are compared with

aircraft wind profiles. In addition to visible and infrared

imagery, techniques for estimating the intensity of a tropi-

cal cyclone have also been developed on the basis of mea-

surements from the Advanced Microwave Sounding Unit

(AMSU; Spencer and Braswell 2001; Demuth et al.

2004). Some of these techniques have been combined to

enhance the TC intensity estimation (e.g., Velden et al.

2006a).

A different approach for characterizing the dynamics

of tropical cyclones was described in Pineros et al.

(2008). In that study, a method to quantify the axisym-

metry of a tropical cyclone from remote-sensing data

was introduced. Using 30-min-resolution geostationary

infrared imagery, the gradient of the brightness tem-

peratures was calculated, and the departure of that

gradient from a perfectly axisymmetric hurricane was

determined. A single value that quantified that depar-

ture from asymmetry was calculated, and a time series

was built and correlated with the best-track intensity

estimates from the National Hurricane Center (NHC).

The technique proved to be quite successful because the

organization of the clouds about the vortex, including

the cirrus shield, is directly tied to the kinematic orga-

nization of the vortex, including the organization of the

eyewall, rainbands, and tangential winds.

In this paper, an improvement of the tropical cyclone

intensity estimation technique described in Pineros et al.

(2008) is presented. In the next section, a brief review of

the method is presented and the improvement of the

technique is introduced. Results are shown in section 3.

Conclusions are discussed in section 4.

2. Method

The study incorporates the 2004–09 North Atlantic

Ocean hurricane seasons (Franklin et al. 2006; Beven

et al. 2008; Franklin and Brown 2008; Brennan et al.

2009; Brown et al. 2010). The data used in this study are

longwave (10.7 mm) IR satellite imagery from the Geo-

stationary Operational Environmental Satellite-12

(GOES-12). The data are available at ;4-km spatial

resolution, but we found previously that reducing the

resolution does not particularly influence the results but

does decrease the computational time considerably.

These images are cropped to cover an area from 48 to

348N and from 1058 to 288W over the northern Atlantic

basin and are resampled to a spatial resolution of 10 km

per pixel. Although the period of interest is from 2004 to

2009, tropical cyclones that had the majority of their

trajectory outside the footprint of the cropped satel-

lite image were excluded from the study. This included

Hurricane Vincent (2005) and Tropical Storms Beryl

(2006), Chantal (2007), Ten (2008), and Grace (2009).

As a result, a total of 15 147 half-hourly images from

2004 to 2009 were analyzed, covering the life cycle of 36

tropical storms and 42 hurricanes.

All samples that were located over land (center

passed over continents and large islands) were removed

from the database for consistency. Observations show

that tropical cyclones that make landfall rapidly decay at

a rate that is inconsistent for overocean tropical cy-

clones. Thus, a different set of parametric curves will be

required for landfalling TCs and is a topic of future

work. For now, all overland samples are simply removed

from the training set.

The original technique to determine the axisymmetry

of a cloud cluster using the deviation angle is illustrated in

Fig. 1 (Pineros et al. 2008). First the gradient of the IR

image at every pixel (in vector form) is calculated. Figure

1a shows the pseudo-IR image for an idealized hurricane.

The associated IR gradient field is shown in Fig. 1b. Next,

choosing a reference or center pixel, the deviation of the

IR gradient vector in a pixel from a radial extending from

the center pixel is determined and stored. This calculation

of the deviation angle is repeated for every pixel within

350-km radius of the center pixel. Next, the distribution

of the deviation angles is plotted (Fig. 1c) and the vari-

ance of that distribution (the deviation-angle variance or

DAV) is determined. The higher the variance of the an-

gle distribution is, the more disorganized is the cloud.

The lower the variance is, the closer to pure axisymme-

try is the cloud pattern. Figures 1d and 1e show the same

sequence as in Figs. 1a–c but for a single snapshot of

Hurricane Rita (2005). The calculation is repeated using

every pixel in turn as the reference center. The variance

values are then plotted back into the reference pixel lo-

cation to create a ‘‘map of DAVs’’ (Pineros et al. 2010)

that corresponds to the original IR image. In Pineros et al.

(2010), the map of variances was used to detect tropical

cyclogenesis. In this study, the map of variances is used to

estimate the tropical cyclone intensity by developing

a parameterized curve fitting that relates the DAV values

with a parameterized function.

The original DAV technique used a fixed 350-km ra-

dius for calculation. Here, we improve the system by

using eight different maps for each image in the training

set at radii varying from 150 to 500 km in steps of 50 km.

OCTOBER 2011 P I N E R O S E T A L . 691

Figure 2 shows an example of three images and their

400-km maps for Hurricane Dean (2007). For each

analysis radius, a time series of the minimum DAV in

the sequence of maps associated with a given tropical

cyclone over its life cycle was constructed (the DAV

signal). A single-pole low-pass filter (impulse response:

e2kt) with a cutoff frequency of 0.01p radians per sample

(filter time constant of 100 h) was applied to smooth the

signal and provide a better correlation with the best-

track intensity estimates, which are only available every

6 h but are interpolated to 30 min.

Next, the filtered DAV signals were mapped to the

NHC best-track intensity records to obtain a parametric

curve between the variance and maximum wind speed

for each of eight radii of analysis. Because the filtered

DAV signal is created using 30-min imagery but the

best-track intensity estimates are available only every

6 h, there is considerably more structure in the DAV

signal. The oscillations present in the filtered DAV

signal include diurnal and semidiurnal frequencies, as

well as some smaller-scale components, and they make it

difficult to relate the DAV to the best-track intensity.

Thus, one intensity value in the best track could have

several associated DAV values. To overcome this prob-

lem, the median of all DAV values associated with a

single best-track intensity estimate was used to create

the data scatterplot. A sigmoid was then fit to the rough

DAV–intensity scatterplot so that the final parametric

curve was described by a continuous equation:

f (s2) 5160

1 1 exp[a(s2 1 b)]1 25 (kt), (1)

where a and b are two parameters to fit from the input

data and s2 is the filtered DAV value. Note that the es-

timated wind speed f(s2) is bounded between 25 and

FIG. 1. (a) Brightness temperature image of an ideal vortex. (b) Gradient vectors of the central section in (a). (c) Distribution of

deviation angles for the ideal vortex in (a). (d) Hurricane Rita, 1415 UTC 21 Sep 2005 (intensity: 130 kt and 932 hPa). (e) Distribution of

deviation angles in (d), with variance 5 593 deg2.


185 kt (where 1 kt ’ 0.5 m s21) in Eq. (1). Although we

considered several different polynomial functions, the

sigmoid was chosen for this application because it is

bounded at both ends of the intensity range, thus avoiding

the possibility of obtaining unrealistically high or low

intensity estimates. Last, the parametric curve for the ra-

dius with the minimum sum of squares of error over the

training set was chosen as the final intensity estimator

parametric curve. The specific optimum radius depends

on the training data used, and we typically see values be-

tween 300 and 400 km. Once the system is trained, DAV

maps are computed for the testing storms with this single

optimum value. This process was repeated with two dif-

ferent training sets, and the resulting two testing data-

sets, to measure its effectiveness as an intensity estimate.

3. Results

For the first test, the intensity estimation parametric

curve was calculated using a training set of 50 tropical

cyclones (70% of the available data) randomly chosen

from the period 2004–08. Only samples with intensities

above tropical storm strength were considered because of

uncertainty in the best-track database at lower intensities

(D. Brown, NHC, 2010, personal communication). The

remaining tropical cyclones were used as an independent

test set. This set included five tropical cyclones from

2004, seven from 2005, two from 2006, and six from

2008. Figure 3 shows the two-dimensional histogram of

the filtered DAV samples and best-track intensity es-

timates for this random set. The best-fit sigmoid curve

for this set is shown as a solid line and was obtained

FIG. 2. Map of the variances for Hurricane Dean (2007) with a 400-km radius: (a) 1215 UTC 14 Aug—35 kt, 1004 hPa, and a map

minimum value (MMV) of 1727 deg2; (b) 0015 UTC 16 Aug—60 kt, 991 hPa, and an MMV of 1548 deg2; (c) at 0015 UTC 18 Oct—125 kt,

944 hPa, and an MMV of 1250 deg2.

FIG. 3. Two-dimensional histogram of the 300-km filtered DAV

samples and best-track intensity estimates using 20 deg2 3 5-kt bins

for 70% of the tropical cyclones randomly chosen from the period

2004–08. The curved line corresponds to the best-fit sigmoid curve

for the median of the samples.


using a radius of 300 km. The root-mean-square error

(RMSE) for the testing set of tropical cyclones was

14.7 kt (Fig. 4).

The second test consisted of training the intensity es-

timator with all tropical cyclones from the years 2004–08

and then using the eight tropical cyclones in the dataset

from 2009 as the independent test set. Figure 5 shows the

two-dimensional histogram and the best-fit parametric

curve, which was obtained using a radius of calculation

for the variance of 350 km. The total RMSE for the 2009

test set was 24.8 kt. The increase in RMSE was entirely

due to just two cases: Tropical Storms Ana and Erika.

Although these tropical cyclones were only weak trop-

ical storms, the cloud structure associated with each

showed high levels of axisymmetry (Fig. 6), resulting in

a DAV that was very low and thus an estimated intensity

that was too high. For these two cases the technique over-

estimated the intensity by more than 250% (Fig. 7). The

likely cause for this overestimate is the dislocation of the

systems’ centers from the very circular cloud masses by

environmental vertical shear. Work is on going to reduce

the overestimates of intensity caused by these kinds of

very circular, but displaced, systems. Until this is accom-

plished in an automated way, it will be necessary to su-

pervise the results of the intensity estimator to avoid this

particular kind of error. The RMSE for the six tropical

cyclones of 2009 excluding Tropical Storms Ana and

Erika is 12.9 kt (see Fig. 8). Table 1 summarizes the curve

parameters and the radius selected for these results.

4. Discussion

a. DAV time series

Although the filtered DAV signals are negatively cor-

related with the best-track intensity estimates (Pineros

et al. 2008), the oscillations present in the signals produce

some dispersion of DAV–wind speed samples, shown in

Figs. 3 and 5. This dispersion is unavoidable because the

best-track intensity estimates from the NHC that are

used as the reference are available only every 6 h as com-

pared with the 30-min resolution of the DAV estimates.

For example, Fig. 9 shows the 350-km DAV signal and the

wind speed for Hurricane Jeanne (2004); the open circles

indicate a diurnal oscillation (23.5 h apart). These fluc-

tuations in the DAV signal in comparison with the lower-

temporal-resolution best-track estimates decrease the

correlation with the best-track intensity. Figure 10 shows

the dispersion produced by the DAV–wind speed samples

of Jeanne in the two-dimensional histogram of Fig. 5. The

open circles shown in Fig. 9 are also plotted in Fig. 10.

Although we have mitigated this problem to some degree

by smoothing the DAV signal and fitting the sigmoid curve

to the data to produce our final DAV–intensity relation-

ship, this mismatch in the temporal resolution between

the DAV signal and the best-track intensity estimate will

always be a limiting factor on the agreement between the

DAV and the best track.

FIG. 4. Intensity estimates and best-track intensities for 20 tropical cyclones (30% of the dataset) randomly chosen from 2004 to 2008, and

the remaining 70% used to obtain a and b. The RMSE is 14.7 kt.

FIG. 5. Two-dimensional histogram of the 350-km filtered DAV

samples and best-track intensity estimates using 20 deg2 3 5-kt bins

for tropical cyclones from the period 2004–08. The curved line cor-

responds to the best-fit sigmoid curve for the median of the samples.


b. Intraseasonal and interannual variability

Previous work by our group (Demirci et al. 2007) has

demonstrated that interannual and intraseasonal vari-

ability of the atmospheric circulation patterns can also

be a limitation on how well an automated technique

can estimate or predict tropical cyclone behavior. To

investigate whether there is sensitivity to either seasonal

or annual variations, the DAV–intensity curves were

recalculated based on training by year and training by

month over the 5-yr period of 2004–08. The fitted curves

for individual years and for individual months over

the 5-yr period are very similar to the overall training

curve for the entire period, suggesting that seasonal and

FIG. 6. Two weak tropical cyclones in 2009 that were removed from the analysis because of their high level of

axisymmetry: (a) Tropical Storm Ana, 0615 UTC 12 Aug (intensity: 30 kt and 1006 hPa), and (b) Tropical Storm

Erika, 0615 UTC 1 Sep (intensity not reported).

FIG. 7. Intensity estimates of two weak tropical cyclones in 2009 that were removed from the analysis: Tropical

Storms (a) Ana and (b) Erika.


interannual variations in the North Atlantic basin do not

appear to affect the DAV intensity curve. The greatest

departure from the general intensity curve for any of the

annual curves is less than 10 kt (data not shown). A sim-

ilar result was obtained for the months of July–October,

where there are enough samples for the system to be stable

(data not shown). Thus, we conclude that there is no sig-

nificant value added in developing different parametric

curves for individual seasons or for different months.

c. Physical reasons for the overall robustnessof the method

Similar to the Dvorak technique that has proven to be

so successful for more than 30 years, this technique has

a physical foundation for its success. This foundation is

based on the premise that the cloud patterns and their

similarity to a perfectly annular pattern are directly re-

lated to the organization of the secondary circulation,

which includes the eyewall and rainband patterns. The

organization (and strength) of the secondary circulation

is then directly related to the size and intensity of the

tropical cyclone primary wind field. Thus, as a general

rule, the symmetric organization of the observed cloud

patterns is an indirect indicator of the intensity of the

primary wind circulation.

d. Rapid intensification

The cutoff frequency of the low-pass filter applied to

smooth the DAV signal determines its transient response,

which in turn increases as the bandwidth decreases

(Priemer 1991). Thus, decreasing the cutoff frequency of

the filter to reduce the fluctuations of the DAV signal so

as to obtain DAV–wind speed samples that are more con-

centrated around the curved line in Fig. 5 simultaneously

increases the technique’s error for rapid-intensification

tropical cyclones. The rapid intensification of Hurricane

Wilma (2005) is an example of how this trade-off can de-

crease the DAV performance. In the case of Wilma, the

low-pass filter that is applied to smooth the DAV signal

results in a response in the DAV signal that is behind the

actual intensification of Wilma (Fig. 11). The rate of in-

tensification and final intensity of Wilma are actually very

well modeled by the DAV parametric curve. However,

the starting time of the rapid-intensification phase is late,

and the time difference between the maximum values of

both signals is around 35 h. Implementing another fil-

ter configuration such as a finite impulse response, which

typically has lower transient responses, or utilizing a higher

cutoff frequency for rapid intensification cases might solve

this problem. This will be a subject for future study.

e. Real-time implementation

The first step in the process is to convert the imagery

from a natural Earth coordinate system to a Cartesian

FIG. 8. Intensity estimates and best-track intensities for 2009, using tropical cyclones from the period 2004–08 to obtain a and b. The

RMSE is 12.9 kt.

TABLE 1. Estimator parameters calculated for two training sets.

Training set

50 tropical cyclones (70%)

randomly chosen from

2004 to 2008

70 tropical cyclones

from 2004 to 2008

a (deg22) 0.002 655 0.002 697

b (deg2) 1008 1110

Radius (km) 300 350FIG. 9. Best-track intensity and 350-km DAV signal for Hurricane

Jeanne (2004). The two open circles are 23.5 h apart.


projection that removes the differences in pixel resolution

that are due to Earth’s curvature. A standard software

package developed by the University of Wisconsin—

Madison, known as the Man–Computer Interactive Data

Access System (McIDAS; see online at http://www.ssec.

wisc.edu/mcidas/software/about_mcidas.html) was used

to compute the projection change. Processing the DAV

maps of an IR image takes less than 1 min on a 2.3-GHz

Intel Core i7 computer with 8 gigabytes of memory using

the Linux CentOS 5.5 operating system and running the

technique’s program with the software package Matlab

7.10 (in no-display mode).

Once the parameterized wind speed estimator is ob-

tained, a shell script can be executed every hour to com-

pute the projection change and calculate the DAV values

of the image using the radius chosen in the development

of the estimator. The process takes less than 4 min for

a single image. The DAV signal is obtained by adding one

sample every time that one image is processed. Although

these tasks can be automatically executed at a specific time

by programming a Linux script, the user should manually

start and stop the job.

5. Conclusions

This paper describes improvements to a completely

objective technique developed in Pineros et al. (2008,

2010) to characterize the intensity of a tropical cyclone.

The technique quantifies the axisymmetry of a tropical

cyclone with the deviation-angle-variance metric as an

indirect estimate of the intensity from infrared imagery

alone. In this paper, a set of parametric curves relating

DAV to maximum wind speed that are the result of the

technique were tested on two independent sets of trop-

ical cyclones: one set randomly selected from the 2004–

08 period to be used as the testing set and the other set

comprising eight tropical cyclones from 2009. These

tests produce an RMSE that is between 13 and 15 kt

after two obviously bad cases are removed from the

testing set. Although part of the remaining 13–15-kt

error is probably due to the DAV signal oscillations that

do not occur in the smoothed best-track intensity esti-

mates, other factors that may help to reduce the overall

FIG. 10. Histogram of Fig. 4. The red points are the 350-km DAV best-track samples for

Hurricane Jeanne (2004). The two open circles from Fig. 9 are plotted to pinpoint the dis-

persion produced from the DAV oscillations.

FIG. 11. Estimated intensity results for Hurricane Wilma (2005).

The RMSE is 31 kt.


RMSE include binning cases by environmental factors

such as the environmental vertical wind shear and sea

surface temperatures; these are the topics of future work.

The potential of the DAV technique to quantify the

axisymmetry of a tropical cyclone (and thus to charac-

terize its dynamics) has been demonstrated in this and

previous papers. This characterization of the tropical cy-

clone dynamics with a single parameter is what makes it

possible to estimate robustly the intensity of the tropical

cyclone. The testing results presented in this paper sug-

gest that the intensity estimates produced by this tech-

nique are reasonably accurate, and, in its current version

as an independent estimate of tropical cyclone intensity,

the DAV technique is a complement to other estimates of

tropical cyclone intensity. Future work includes running

real-case simulations of the life cycle of tropical cyclones

using a full-physics, high-resolution mesoscale model to

calculate the DAV signal and compare it with the syn-

thetic maximum surface wind data from the same sim-

ulations at the same temporal resolution. From these

models the technique can be calibrated to estimate in-

tensity without the need to filter the signal to improve

the correlation with the lower-resolution best-track re-

cords. In addition, although initial results indicate that

there is little interannual or intraseasonal variability in

the DAV–intensity parametric curves, additional analysis

of these potential variations that might reduce the RMSE

will be undertaken. Furthermore, the physical processes

behind the robustness of the DAV–intensity relationship

will be studied using the high-resolution simulation out-

put to understand better, and thus improve, the DAV–

intensity relationship for intensity estimation.

Acknowledgments. Tropical cyclone best-track data

were obtained from NOAA’s National Hurricane Cen-

ter Internet site (http://www.nhc.noaa.gov). This study

has been supported by the Office of Naval Research

NOPP program under Grant N00014-10-1-0146.

REFERENCES

Beven, J. L., and Coauthors, 2008: Atlantic hurricane season of

2005. Mon. Wea. Rev., 136, 1109–1173.

Brennan, M. J., R. D. Knabb, M. Mainelli, and T. B. Kimberlain,

2009: Atlantic hurricane season of 2007. Mon. Wea. Rev., 137,4061–4088.

Brown, D. P., J. L. Beven, J. L. Franklin, and E. S. Blake, 2010:

Atlantic hurricane season of 2008. Mon. Wea. Rev., 138, 1975–

2001.

Demirci, O., J. S. Tyo, and E. A. Ritchie, 2007: Spatial and spa-

tiotemporal projection pursuit techniques to predict the ex-

tratropical transition of tropical cyclones. IEEE Trans.

Geosci. Remote Sens., 45, 418–425.

Demuth, J. L., M. DeMaria, J. A. Knaff, and T. H. Vonder Haar,

2004: Evaluation of Advanced Microwave Sounding Unit

tropical cyclone intensity and size estimation algorithms.

J. Appl. Meteor., 43, 282–296.

Dvorak, V. F., 1975: Tropical cyclone intensity analysis and

forecasting from satellite imagery. Mon. Wea. Rev., 103,

420–430.

Franklin, J. L., and D. P. Brown, 2008: Atlantic hurricane season of

2006. Mon. Wea. Rev., 136, 1174–1200.

——, R. J. Pasch, L. A. Avila, J. L. Beven, M. B. Lawrence, S. R.

Stewart, and E. S. Blake, 2006: Atlantic hurricane season of

2004. Mon. Wea. Rev., 134, 981–1025.

Gray, W. M., 1979: Hurricanes: Their formation, structure and

likely role in the tropical circulation. Meteorology over

Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological So-

ciety, 155–218.

Knaff, J. A., D. P. Brown, J. Courtney, G. M. Gallina, and J. L. Beven

II, 2010: An evaluation of Dvorak technique–based tropical

cyclone intensity estimates. Wea. Forecasting, 25, 1362–1379.

Kossin, J. P., J. A. Knaff, H. I. Berger, D. C. Herndon, T. A. Cram,

C. S. Velden, R. J. Murnane, and J. D. Hawkins, 2007: Esti-

mating hurricane wind structure in the absence of aircraft

reconnaissance. Wea. Forecasting, 22, 89–101.

McBride, J., 1995: Tropical cyclone formation. A Global View

of Tropical Cyclones, R. Elsberry, Ed., WMO (Tech. Rep.

TCP-38), 63–105.

Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak

technique: Continued development of an objective scheme to

estimate tropical cyclone intensity using geostationary in-

frared satellite imagery. Wea. Forecasting, 22, 287–298.

Pineros, M. F., E. A. Ritchie, and J. S. Tyo, 2008: Objective mea-

sures of tropical cyclone structure and intensity change from

remotely sensed infrared image data. IEEE Trans. Geosci.

Remote Sens., 46, 3574–3580.

——, ——, and ——, 2010: Detecting tropical cyclone genesis from

remotely-sensed infrared image data. IEEE Geosci. Remote

Sens. Lett., 7, 826–830.

Priemer, R., 1991: Introductory Signal Processing. World Scientific,

574 pp.

Ritchie, E. A., J. Simpson, T. Liu, J. Halverson, C. Velden,

K. Brueske, and H. Pierce, 2003: Present-day satellite tech-

nology for hurricane research: A closer look at formation and

intensification. Hurricane! Coping with Disaster, R. Simpson,

Ed., Amer. Geophys. Union, 249–891.

Spencer, R., and W. D. Braswell, 2001: Atlantic tropical cyclone

monitoring with AMSU-A: Estimation of maximum sustained

wind speeds. Mon. Wea. Rev., 129, 1518–1532.

Velden, C., T. Olander, and R. Zehr, 1998: Development of an

objective scheme to estimate tropical cyclone intensity from

digital geostationary satellite infrared imagery. Wea. Fore-

casting, 13, 172–186.

——, D. C. Herndon, J. Kossin, J. Hawkins, and M. DeMaria,

2006a: Consensus estimates of tropical cyclone intensity using

integrated multispectral (IR and MW) satellite observations.

Preprints, 27th Conf. on Hurricanes and Tropical Meteorol-

ogy, Monterey, CA, Amer. Meteor. Soc., P4.1. [Available

online at http://ams.confex.com/ams/pdfpapers/107408.pdf.]

——, and Coauthors, 2006b: The Dvorak tropical cyclone in-

tensity estimation technique: A satellite-based method that

has endured for over 30 years. Bull. Amer. Meteor. Soc., 87,1195–1210.


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