THE PHYSICAL processes associated with tropical cyclone

Transcription

1 826 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 4, OCTOBER 2010 Detecting Tropical Cyclone Genesis From Remotely Sensed Infrared Image Data Miguel F. Piñeros, Elizabeth A. Ritchie, and J. Scott Tyo Abstract An objective technique to discriminate developing from nondeveloping cloud clusters during tropical cyclogenesis is described. Since vortices are characterized by high levels of organization or axisymmetry, their detection at early stages of the lifecycle of tropical cyclones makes it possible to determine when they form. To quantify the axisymmetry of a cloud cluster around a predefined radius, a statistical analysis of the orientation of the brightness-temperature gradient is performed. Results show that early detections of axisymmetric structures reliably indicate the cyclone genesis on an average of 0.6 h before an atmospheric disturbance becomes a tropical depression. In addition, the technique shows potential to discriminate nondeveloping from developing cloud clusters. A statistical analysis shows that a true-positive rate of detection of approximately 93% could be achieved with a false-alarm rate of 22%. Index Terms Genesis, meteorology, remote sensing, tropical cyclone (TC). I. INTRODUCTION THE PHYSICAL processes associated with tropical cyclone (TC) development have been a subject of considerable interest (see [1] for a general review). However, the mechanisms involved in the evolution of the immature storm are not well understood, and the problem of detecting which of the many tropical cloud clusters will eventually become a TC is still unresolved. TCs form and spend much of their life over the vast tropical oceans of the world where conventional measurements are sparse. Thus, satellite observations are an important tool to estimate the track and intensity of TCs, providing remotely sensed measurements of wide regions of the ocean where direct measurements are rarely available. Techniques using satellite measurements are perhaps the only way to reliably detect, and possibly even predict, the evolution of a cloud cluster into a TC. The most widely used technique based on satellite imagery is the Dvorak technique [2], [3], which was developed in the 1970s by V. Dvorak during the early years of remote-sensing satellites. The technique uses infrared (IR) and visible imagery to assess similarity of a cloud structure in a given TC to a standard set of visual patterns. These patterns detect cloud Manuscript received July 29, 2009; revised March 28, Date of publication June 1, 2010; date of current version October 13, This work was supported in part by the Office of Naval Research under Grant N and in part by the State of Arizona under a Technology and Research Initiative Fund Student Fellowship. M. F. Piñeros and J. S. Tyo are with the College of Optical Sciences, University of Arizona, Tucson, AZ USA ( mpineros@ece.arizona.edu; tyo@optics.arizona.edu). E. A. Ritchie is with the Department of Atmospheric Sciences, University of Arizona, Tucson, AZ USA ( ritchie@atmo.arizona.edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /LGRS rotation for circulation, the coldest brightness temperatures in the eyewall to detect deep thunderstorms, warm brightness temperatures to detect the eye at the center of circulation, and the differences between the two to determine intensity, when possible. An expert follows a complex set of rules to determine an intensity rating for the TC. The original Dvorak technique is subjective, time intensive, and relies on the expertise of the analyst, but it is still used as the primary intensity-estimation tool in many TC forecasting centers around the world (e.g., [4] and [5]). Velden et al. [6] integrated several modifications to the original Dvorak technique known as the objective Dvorak technique (ODT). The technique measures the difference of temperature from IR imagery between the eye of a TC and the surroundings. The intensity is estimated from an empirically determined lookup table. Although the ODT automatically calculates the intensity of a TC, the eye is located manually using similar methods to those employed in the original Dvorak technique or from other sources such as aircraft reconnaissance. Olander and Velden [7] introduced later developments to the ODT known as the advanced Dvorak technique. These included new rules, statistically based adjustments, and regression-based equations in order to estimate the intensity of TCs. Other procedures to estimate the intensity and predict the formation of TCs are based on numerical weather products, which are initialized and periodically corrected with information obtained from direct or remotely sensed measurements (e.g., [8] and [9]). Schumacher et al. [10] proposed a technique to predict TCs 24 h prior to their formation. It applies a set of thresholds and linear discriminant analysis to several convective and environmental parameters such as vertical shear, low-level circulation, and low-level divergence. The technique calculates the probability of TC formation for a given 5 5 area of the ocean but does not track and analyze each cloud cluster to identify developing systems. In addition, some procedures based on the Advanced Microwave Sounding Unit have been developed to estimate the intensity of TCs (e.g., [11]); however, these techniques do not discriminate developing from nondeveloping cloud clusters and are applied once there is an evident circulation pattern. Objective discrimination between cloud clusters that will develop into TCs and those that will not has proven to be a challenging task. Early-stage TCs are characterized by high levels of disorganization and appear structurally similar to nondeveloping cloud clusters. The level of organization of a cloud cluster is obtained by quantifying its axisymmetry. Since the organization and axisymmetry increases as the storm develops, the quantification of that symmetry provides an alternative and indirect method for estimating the intensity. An objective technique based on this conclusion has been recently presented X/$ IEEE

2 PIÑEROS et al.: DETECTING TROPICAL CYCLONE GENESIS FROM INFRARED IMAGE DATA 827 Fig. 1. IR image (10.7 μm) of the Gulf of Mexico and Western Atlantic Basin at 1915 UTC August 28, The scene includes Hurricane Katrina near 90 W and some other less organized cloud systems. in [12]. In that study, the best track intensity estimates from the National Hurricane Center (NHC) were compared with a parameter that quantifies the axisymmetry of the TC. The time series obtained from that parameter was well correlated to the time series of TC best track intensity and thus, described the development of the TC in terms of its cloud organization. Interestingly, the signals obtained in that study showed some frequency components that suggest the presence of organized structures even when the storm is in the very early stages of development. In this letter, we extend and modify the method of [12] in order to detect symmetric organization within cloud clusters that may distinguish nascent TCs from nondeveloping cloud clusters. In the next section, the process of quantifying the axisymmetry of a TC is described, and we show how this leads to objectively detect the formation of TCs. The results of applying this technique to TCs at the early stages of their lifecycle are presented, and its potential for a forecasting product is described in Section III. Finally, conclusions and future work are discussed in Section IV. II. METHODOLOGY The data used for this study are long wave (10.7 μm) IR images with 8 bits/pixel and 5 km/pixel of resolution from the Geostationary Operational Environmental Satellite (GOES- 12). All available imagery (a total of 9200 half-hourly images) were taken between the months of June and December of 2004 and 2005 over the Atlantic basin between 4 and 34 N latitude, and 105 and 28 W longitude. During this period, there were 40 TCs, including one tropical depression, one subtropical depression, one subtropical storm, 17 tropical storms, and 20 hurricanes. In addition, there were 136 nondeveloping cloud clusters. Hurricanes Danielle, Ivan, and Karl (2004), and Hurricane Vince (2005) were removed from the analysis because important sections of their trajectory, in particular, the genesis sections, were out of scope in the images. An example of the input data utilized in this study is shown in Fig. 1, which illustrates a frame of Hurricane Katrina sequence in 2005 plus several other cloud clusters of various levels of organization. Piñeros et al. [12] describe an automated method to locate the center of a cloud cluster about which the analysis can be done. However, the center may not be well defined, particularly at early stages of the TC lifecycle, and this may produce inaccurate results. To overcome this problem, each image is processed as described in [12] to calculate the gradient of the brightness-temperature field. Then, using every pixel in the image sequentially as a central reference point, the deviation angle from a perfect radial for each gradient vector extending from that central point for a radius of 70 pixels (350 km) is calculated, and a histogram of deviation angles is constructed similar to [12]. The variance of the histogram of deviation angles is the signal that determines the axisymmetry of any particular cloud cluster in the image. Fig. 2(b) and (c) shows this process for the pixel shown in Fig. 2(a). The main difference from [12] is that instead of isolating every cloud cluster and individually locating a center point, then calculating the deviation angles and variance around that point, the deviation angles and variance are now calculated for every pixel in the entire scene. The output is a frame or map of deviation-angle variances [Fig. 2(d)] in which low values of variance indicate that a cloud cluster exhibits high levels of axisymmetry in the circular neighborhood surrounding that pixel, as generally observed in vortices. Conversely, high levels of deviation angle variance indicate low levels of axisymmetry or organization, typically found in nondeveloping cloud clusters. Fig. 3 shows the calculation of the map of deviation-angle variances for a real case; an early stage of Hurricane Wilma (2005), which occurred approximately 4 h before the NHC classified it as a tropical depression in the best track records. The minimum value in the map of variances in Fig. 3(b) is 1648 deg 2, and the corresponding location in the image is indicated by the black dot in Fig. 3(a). For pixels away from this point, the deviation-angle calculation results in higher levels of variance. Following the expected increment in axisymmetry as the storm develops, the minimum value in the map of variances for a mature stage of Hurricane Wilma (67 m/s, 924 hpa) is 1189 deg 2 (Fig. 4), and the location of that variance value corresponds to the eye. The map of deviation-angle variances is calculated for all 9200 images. A series of threshold variance values is set to determine whether a cloud cluster has reached a level of axisymmetry that characterizes a TC. All pixels in the map are checked for this threshold value, and the detection time is defined from the moment that some pixels reach this threshold value to the time when the TC is classified as tropical depression by the NHC. Negative detection times indicate that pixels in the map reach the threshold value before the corresponding cloud cluster in the image is classified as tropical depression. High threshold values detect organized structures that are typically found in very intense TCs; therefore, high values tend to generate positive detection times. In contrast, low threshold values will detect unorganized structures that can characterize both developing and nondeveloping cloud clusters, and consequently, these may generate negative detection times. A wide interval of threshold values from the map of variances is selected in order to characterize the performance of the technique. This process is repeated for every image, and all detected cases are recorded and checked against the NHC best track database.

3 828 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 4, OCTOBER 2010 Fig. 2. Map of deviation-angle variances. (a) IR image. The area analyzed around a reference point is indicated by the black circle (350 km). (b) Deviation-angle calculation for (black arrow) a gradient vector relative to a radial line extending from the reference point. (c) Deviation-angle histogram. (d) Map of deviation-angle variances [deg 2 ]. Fig. 3. Images of Hurricane Wilma at 1345 UTC October 15, (a) IR image. This storm was not classified as tropical depression at this stage by the NHC. (b) The corresponding map of deviation-angle variances [deg 2 ].The minimum value in the map of variances (1648) corresponds to the location N, W and is indicated by the black dot in (a). Fig. 5. ROC curve for storm detections in IR images during 2004 and 2005 for various deviation-angle variance threshold values. Fig. 4. Images of Hurricane Wilma at 0015 UTC October 21, (a) IR image. Current best track intensity is 67 m/s and 924 hpa. (b) The corresponding map of deviation-angle variances [deg 2 ]. The minimum value in the map of variances (1189) corresponds to the location N, W. The center of the storm reported by the NHC was located at 19.1 of latitude and 85.8 of longitude. III. RESULTS The performance of this technique was measured by applying threshold variance values of axisymmetry from 1350 to 2000 deg 2 (in steps of 50) in the detection process. Note that for this range of variances, Wilma in Fig. 3 would already have been detected as a developing TC by the thresholds 1650 to 2000 deg 2. These results were compared with the NHC best track files to obtain the number of true detections (true positives), the detection time, and the number of detections of nondeveloping cloud clusters (false alarms). Note that TCs detected after reaching hurricane intensity are not included as true detections. Necessary climatological conditions for formation of a TC are described in [13] and include a requirement for a nonzero amount of Earth vorticity or Coriolis to exist. For this reason, all cloud clusters equatorward of 5 latitude were removed from the analysis. In addition, all cloud clusters over land and those with low average brightness temperatures within a radius of Fig. 6. Mean and median time of detection of storms by the technique for various deviation-angle variance threshold values before being classified as tropical depressions by NHC during 2004 and pixels (threshold was calculated as one-third of the maximum pixel value to the entire scene average) were also removed from the analysis. This is to avoid possible warm (black) regions on the image that may produce low deviation-angle variances on the maps. These low values may be generated due to the fact that only the orientation of the gradient vectors, but not their magnitude and pixel intensities, is considered when calculating the maps. The final results are shown in a receiver operating characteristic (ROC) curve in Fig. 5 and a plot of the mean detection time versus threshold value in Fig. 6. Fig. 5 shows that a high value of the detection threshold is a poor criterion to discriminate developing from nondeveloping cloud clusters. When the highest threshold value of 2000 deg 2 was applied, all cloud clusters met the genesis criteria. This is because a threshold level of 2000 deg 2 indicates a very disorganized structure, and this threshold often fails to discriminate those cloud clusters that truly go on to develop into TCs. Conversely, when the lowest threshold (1350 deg 2 )

4 PIÑEROS et al.: DETECTING TROPICAL CYCLONE GENESIS FROM INFRARED IMAGE DATA 829 Fig. 7. Normalized histogram of detection time of storms for the variance threshold value of 1700 deg 2 before being classified as tropical depressions by NHC during 2004 and is applied, eighteen storms (45%) were detected on an average of 40 h after they are classified as tropical depressions (Fig. 6). In particular, threshold value of 1700 deg 2 generated a total of 37 of the storms (93%) successfully detected on an average of 0.6 h before they are classified as tropical depressions (Fig. 6). For this threshold value, Fig. 7 shows the histogram of the detection time. The best cases corresponded to Hurricane Philippe (2005) and Tropical Storm Gamma (2005), which were detected approximately 70 h before developing into tropical depressions. These cases evolved from cloud clusters that lasted around three days. In contrast, the worst case was Hurricane Lisa (2004), characterized by a small area of associated deep convection at early stages. This system was identified as a vortex almost 100 h after being classified as tropical depression. Four tropical storms, Hemine (2004), Bret (2005), and subtropical depression twenty-two, were not detected at all due to their high degree of disorganization and small area of associated deep convection during their lifecycle. On the other hand, 30 false alarms out of 136 nondeveloping cloud clusters (22%) occurred when using the threshold value of 1700 deg 2. organized clouds are generally present in only a portion of the cloud cluster (near the center), the variance is simply calculated using every pixel in the entire scene as the reference point in turn and producing a map of variances. Low areas of variance indicate regions of high axisymmetry. This removes the need to know apriorihow many cloud clusters are in the scene because the technique calculates the deviation-angle variance for every pixel in the entire scene. Next, the lowest variance value corresponding to the highest axisymmetric portion of the cloud cluster is selected and compared with a threshold value that identifies a developing vortex. This technique, which only utilizes a single channel of remotely sensed IR imagery, produces different performances in detecting potential TCs according to the threshold value applied to the map of deviation-angle variances. The balance between the true and false detection rates and the detection time will determine the threshold value selected to objectively detect the genesis of a TC. For example, the threshold value of 1700 generates a true-positive rate of 93% and 22% of false-alarm rate (false-positive rate), on average of 0.6 h before TCs are classified as tropical depressions by the NHC. Currently, the technique is designed to flag a cloud cluster when it reaches a certain level of organization based on the deviation-angle variance threshold, and the results described earlier show its performance. While the technique can be applied by forecasters right now, there are some obvious next steps: 1) decrease the false positive rate by combining more features in the classification; and 2) modify the way the output is produced such that a probability of development of a particular cloud cluster within a certain period (e.g., 48 h) is calculated. Other future work includes adjusting the technique for other TC basins, and turning the technique into a real-time forecast tool. ACKNOWLEDGMENT Best track data were obtained from the National Oceanic Atmospheric Administration, National Hurricane Center, FL website at IV. DISCUSSION AND CONCLUSION This letter describes an objective technique to predict the formation of TCs by quantifying the axisymmetric organization of the embedded clouds in the early stages of their lifecycle. The premise is that as the TC circulation increases, the symmetric organization of the associated clouds also increases because the clouds are organized by the underlying TC vortex structure. The pre-tc cloud clusters are initially characterized by low levels of symmetric organization or axisymmetry. However, as the cloud cluster develops into a TC, the symmetric organization of the clouds within the cloud cluster increases. To quantify this level of organization, the technique relies on a statistical calculation of the gradient vector orientation of brightnesstemperature IR images relative to a reference point and within a predefined radius. However, the calculation of the deviationangle variance value is highly sensitive in choosing a good reference point, which is a challenging task at the early stages of TC development [14]. Because the most axisymmetrically REFERENCES [1] J. McBride, Tropical cyclone formation. Global view of tropical cyclones, WMO, Geneva, Switzerland, Tech. Rep. TCP-38, [2] V. F. Dvorak, Tropical cyclone intensity analysis and forecasting from satellite imagery, Mon. Weather Rev., vol. 103, no. 5, pp , May [3] V. F. Dvorak, Tropical cyclone intensity analysis using satellite data, NOAA, Washington, DC, Tech. Rep. NESDIS 11, [4] C. S. Velden, B. Harper, F. Wells, J. L. Beven, II, R. Zehr, T. Olander, M. Mayfield, C. Guard, M. Lander, R. Edson, L. Avila, A. Burton, M. Turk, A. Kikuchi, A. Christian, P. Caroff, and P. McCrone, The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years, Bull. Amer. Meteorol. Soc., vol. 87, no. 9, pp , Sep [5] C. S. Velden, B. Harper, F. Wells, J. L. Beven, II, R. Zehr, T. Olander, M. Mayfield, C. Guard, M. Lander, R. Edson, L. Avila, A. Burton, M. Turk, A. Kikuchi, A. Christian, P. Caroff, and P. McCrone, Supplement to: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years, Bull. Amer. Meteorol. Soc., vol. 87, no. 9, pp. S6 S9, Sep [6] C. Velden, T. Olander, and R. Zehr, Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery, Weather Forecast., vol.13,no.1,pp , 1998.

5 830 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 4, OCTOBER 2010 [7] T. L. Olander and C. S. Velden, The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery, Weather Forecast., vol. 22, no. 2, pp , Apr [8] M. DeMaria, M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, Further improvements to the statistical hurricane intensity prediction scheme (SHIPS), Weather Forecast., vol. 20, no. 4, pp , Aug [9] M. A. Bender, R. J. Ross, R. E. Tuleya, and Y. Kurihara, Improvements in tropical cyclone track and intensity forecasts using the GFDL initialization system, Mon. Weather Rev., vol.121,no.7,pp , Jul [10] A. B. Schumacher, M. Demaria, and J. Knaff, Objective estimation of the 24-h probability of tropical cyclone formation, Weather Forecast., vol. 24, no. 2, pp , Apr [11] J. L. Demuth, M. DeMaria, J. A. Knaff, and T. H. V. Harr, Evaluation of advanced microwave sounding unit tropical-cyclone intensity and size estimation algorithms, J. Appl. Meteorol., vol. 43, no. 2, pp , Feb [12] M. F. Piñeros, E. A Ritchie, and J. S. Tyo, Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data, IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pt. 1, pp , Nov [13] W. M. Gray, Hurricanes: Their formation, structure and likely role in the tropical circulation, in Meteorology Over the Tropical Oceans, D. B. Shaw, Ed. Berkshire, U.K.: Royal Meteorol. Soc., 1979, pp [14] M. F. Pineros, Objective measures of tropical cyclone intensity and formation from satellite infrared imagery, Ph.D. dissertation, Dept. Elect. Comput. Eng., Univ. Arizona, Tucson, Az, 2009.