1. Detecting tropical cyclone formation from satellite imagery Elizabeth A. RitchieMiguel F. PiñerosJ. Scott TyoS. Galvin University of Arizona Acknowledgements: Office of Naval Research Marine Meteorology Program TRIF – image processing fellowship HFIP – Hurricane Forecast Improvement Program 90 W75 W60 W45 W 30 N 20 N 10 N

2.   The Deviation-Angle Variance Technique (DAV-T) Hurricane Beta (2005) 350 km -- Produce the map of variance values -- produce a time series of the minimum variance value for a cloud system -- the lower the variance, the more organised the system

3. Correlation: Total time series -0.93 From 25 kts on-0.92 Hurricane Wilma 2005 33 kt NHC first warning Low deviation-angle variances at early stages

4. False Positive Rate 1700 1750 1800 1850 1900 1950 2000 Variance Thresholds 1550 1500 1600 1400/1450 1350 1650 True Positive Rate ROC curve for IR imagery

5. Variance Threshold [deg 2 ] Time [h] 2004 & 2005 Time to Detection – “TD” TPR = 93% FPR = 22% TPR = 96% FPR = 40%

6. What Next? 1.Improve the True-Positive Rate while reducing the False-Alarms - Add more information – water vapor channel - Add more information – water vapor channel - Test sensitivity to some of the parameters in the technique - Test sensitivity to some of the parameters in the technique - Brightness threshold 2.Increase the time-to-detection - Add more information - Test sensitivity to some of the parameters in the technique 3.Increase the database to ensure statistical stability (ongoing) 4.Develop a probabilistic Forecasting Protocol (Summer/10) 5.Making the process of cloud tracking more efficient!! - the Cloud-Tracking Interface

7. Water Vapor Imagery … and the “Cloud-Tracking Interface” Study:- Aug-Sep 2005 * Test sensitivity to Brightness Threshold * Cloud-Tracking Interface * Is a gui designed by a (desperate) undergraduate student to make the process more efficient (less boring) * displays satellite imagery – can be stepped through in time * stores the first time the minimum variance value meets a threshold in a database * links to the NHC best-track database to display tracks and assign actual TCs to cloud clusters.

8. Test with 2 months of water vapor imagery:

9. Sensitivity to Brightness Temperature Threshold:

11. True Positive Rate ROC curve Aug/Sep 2005 Inter-comparison 17001800 1700 1800 1700 1800 False Positive Rate

12. Threshold Minimum Variance Values Time (h) Mean Time to Detection PoD: 85% FAR: 13% PoD: 92% FAR: 26% PoD: 100% FAR: 43%

13. Conclusions A completely objective technique to determine whether a cloud system will develop into a TC. A completely objective technique to determine whether a cloud system will develop into a TC. Water Vapor imagery gives similar PoD vs FAR results to IR Water Vapor imagery gives similar PoD vs FAR results to IR Mean detection times can be improved by use of WV imagery and by tuning some of the parameters Mean detection times can be improved by use of WV imagery and by tuning some of the parameters Best potential is using a combination of the two types of imagery – IR provides better PoD to FAR, WV provides earlier detection Best potential is using a combination of the two types of imagery – IR provides better PoD to FAR, WV provides earlier detection Future Work:- Future Work:- Continue to test the sensitivity to parameters Continue to test the sensitivity to parameters Develop a Forecaster protocol as per NHC needs Develop a Forecaster protocol as per NHC needs

14. Thank you 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. Geosciences and remote sensing. 46, 3574-3580. Piñeros, M. F., E. A. Ritchie, and J. S. Tyo 2010: Detecting tropical cyclone genesis from remotely-sensed infrared image data. (IEEE Trans. Geosciences and remote sensing. In Review)