Estimating Tropical Cyclone Intensity from Infrared Image Data

Transcription

1 690 W E A T H E R A N D F O R E C A S T I N G VOLUME 26 Estimating Tropical Cyclone Intensity from Infrared Image Data MIGUEL F. PIÑEROS 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 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 s 21 ) 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 observations 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 soundings, among others, none of these include direct measurements 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 Corresponding author address: Miguel F. Piñeros, PAS Bldg., Rm. 542, P.O. Box , The University of Arizona, Tucson, AZ mpineros@ece.arizona.edu 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 simulating the original Dvorak technique. One of the most DOI: /WAF-D Ó 2011 American Meteorological Society

2 OCTOBER 2011 P I Ñ EROS ET AL. 691 important improvements of the ADT consists of the introduction 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 twodimensional surface wind field are estimated from infrared (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 tropical cyclone have also been developed on the basis of measurements 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 Piñeros et al. (2008). In that study, a method to quantify the axisymmetry of a tropical cyclone from remote-sensing data was introduced. Using 30-min-resolution geostationary infrared imagery, the gradient of the brightness temperatures was calculated, and the departure of that gradient from a perfectly axisymmetric hurricane was determined. A single value that quantified that departure 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 organization 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 Piñeros 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 Method The study incorporates the 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 Geostationary 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 satellite 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 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 cyclones. 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 (Piñeros 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 variance of that distribution (the deviation-angle variance or DAV) is determined. The higher the variance of the angle distribution is, the more disorganized is the cloud. The lower the variance is, the closer to pure axisymmetry 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 location to create a map of DAVs (Piñeros et al. 2010) that corresponds to the original IR image. In Piñeros 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 radius 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.

3 692 W E A T H E R A N D F O R E C A S T I N G VOLUME 26 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 deg 2. 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: e 2kt ) 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 besttrack 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 problem, 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 (s ) exp[a(s (kt), (1) 1 b)] where a and b are two parameters to fit from the input data and s 2 is the filtered DAV value. Note that the estimatedwindspeedf(s 2 ) is bounded between 25 and

4 OCTOBER 2011 P I Ñ EROS ET AL. 693 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 deg 2 ; (b) 0015 UTC 16 Aug 60 kt, 991 hpa, and an MMV of 1548 deg 2 ; (c) at 0015 UTC 18 Oct 125 kt, 944 hpa, and an MMV of 1250 deg kt (where 1 kt 0.5 m s 21 ) 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 radius 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 between 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 different training sets, and the resulting two testing datasets, to measure its effectiveness as an intensity estimate. test set. This set included five tropical cyclones from 2004, seven from 2005, two from 2006, and six from Figure 3 shows the two-dimensional histogram of the filtered DAV samples and best-track intensity estimates for this random set. The best-fit sigmoid curve for this set is shown as a solid line and was obtained 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 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 FIG. 3. Two-dimensional histogram of the 300-km filtered DAV samples and best-track intensity estimates using 20 deg kt bins for 70% of the tropical cyclones randomly chosen from the period The curved line corresponds to the best-fit sigmoid curve for the median of the samples.

5 694 W E A T H E R A N D F O R E C A S T I N G VOLUME 26 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. 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 estimator with all tropical cyclones from the years 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 tropical 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 overestimated 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 accomplished in an automated way, it will be necessary to supervise 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. best-track intensity estimates from the NHC that are used as the reference are available only every 6 h as compared 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 fluctuations in the DAV signal in comparison with the lowertemporal-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 relationship, 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. 4. Discussion a. DAV time series Although the filtered DAV signals are negatively correlated with the best-track intensity estimates (Piñeros 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 FIG. 5. Two-dimensional histogram of the 350-km filtered DAV samples and best-track intensity estimates using 20 deg kt bins for tropical cyclones from the period The curved line corresponds to the best-fit sigmoid curve for the median of the samples.

6 OCTOBER 2011 P I Ñ EROS ET AL. 695 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). b. Intraseasonal and interannual variability Previous work by our group (Demirci et al. 2007) has demonstrated that interannual and intraseasonal variability 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 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. 7. Intensity estimates of two weak tropical cyclones in 2009 that were removed from the analysis: Tropical Storms (a) Ana and (b) Erika.

7 696 W E A T H E R A N D F O R E C A S T I N G VOLUME 26 FIG. 8. Intensity estimates and best-track intensities for 2009, using tropical cyclones from the period to obtain a and b. The RMSE is 12.9 kt. 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 similar 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 significant value added in developing different parametric curves for individual seasons or for different months. c. Physical reasons for the overall robustness of 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 related 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 concentrated 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 decrease 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 intensification 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 filter 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 TABLE 1. Estimator parameters calculated for two training sets. Training set 50 tropical cyclones (70%) randomly chosen from 2004 to tropical cyclones from 2004 to 2008 a (deg 22 ) b (deg 2 ) Radius (km) FIG. 9. Best-track intensity and 350-km DAV signal for Hurricane Jeanne (2004). The two open circles are 23.5 h apart.

8 OCTOBER 2011 P I Ñ EROS ET AL. 697 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 dispersion produced from the DAV oscillations. 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 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 obtained, a shell script can be executed every hour to compute 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 asingleimage.thedavsignalisobtainedbyaddingone 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. 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 tropical cyclones: one set randomly selected from the period to be used as the testing set and the other set comprising eight tropical cyclones from 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 kt error is probably due to the DAV signal oscillations that do not occur in the smoothed best-track intensity estimates, other factors that may help to reduce the overall 5. Conclusions This paper describes improvements to a completely objective technique developed in Piñeros et al. (2008, 2010) to characterize the intensity of a tropical cyclone. The technique quantifies the axisymmetry of a tropical FIG. 11. Estimated intensity results for Hurricane Wilma (2005). The RMSE is 31 kt.

9 698 W E A T H E R A N D F O R E C A S T I N G VOLUME 26 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 characterize its dynamics) has been demonstrated in this and previous papers. This characterization of the tropical cyclone 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 suggest that the intensity estimates produced by this technique 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 synthetic maximum surface wind data from the same simulations at the same temporal resolution. From these models the technique can be calibrated to estimate intensity without the need to filter the signal to improve the correlation with the lower-resolution best-track records. 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 output 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 Center Internet site ( This study has been supported by the Office of Naval Research NOPP program under Grant N REFERENCES Beven, J. L., and Coauthors, 2008: Atlantic hurricane season of Mon. Wea. Rev., 136, Brennan, M. J., R. D. Knabb, M. Mainelli, and T. B. Kimberlain, 2009: Atlantic hurricane season of Mon. Wea. Rev., 137, Brown, D. P., J. L. Beven, J. L. Franklin, and E. S. Blake, 2010: Atlantic hurricane season of Mon. Wea. Rev., 138, Demirci, O., J. S. Tyo, and E. A. Ritchie, 2007: Spatial and spatiotemporal projection pursuit techniques to predict the extratropical transition of tropical cyclones. IEEE Trans. Geosci. Remote Sens., 45, 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, Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, Franklin, J. L., and D. P. Brown, 2008: Atlantic hurricane season of Mon. Wea. Rev., 136, , R. J. Pasch, L. A. Avila, J. L. Beven, M. B. Lawrence, S. R. Stewart, and E. S. Blake, 2006: Atlantic hurricane season of Mon. Wea. Rev., 134, 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 Society, 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, 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: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, McBride, J., 1995: Tropical cyclone formation. A Global View of Tropical Cyclones, R. Elsberry, Ed., WMO (Tech. Rep. TCP-38), 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 infrared satellite imagery. Wea. Forecasting, 22, Piñeros, M. F., E. A. Ritchie, and J. S. Tyo, 2008: Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data. IEEE Trans. Geosci. Remote Sens., 46, ,, and, 2010: Detecting tropical cyclone genesis from remotely-sensed infrared image data. IEEE Geosci. Remote Sens. Lett., 7, 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 technology for hurricane research: A closer look at formation and intensification. Hurricane! Coping with Disaster, R. Simpson, Ed., Amer. Geophys. Union, Spencer, R., and W. D. Braswell, 2001: Atlantic tropical cyclone monitoring with AMSU-A: Estimation of maximum sustained wind speeds. Mon. Wea. Rev., 129, 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. Forecasting, 13, , 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 Meteorology, Monterey, CA, Amer. Meteor. Soc., P4.1. [Available online at and Coauthors, 2006b: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87,