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TOWARD AN OBJECTIVE SATELLITE-BASED ALGORITHM TO PROVIDE REAL-TIME ESTIMATES OF TC INTENSITY USING INTEGRATED MULTISPECTRAL (IR AND MW) OBSERVATIONS Christopher Velden, James Kossin, Tim Olander, Derrick Herndon, Tony Wimmers, Howard Berger University of Wisconsin – Cooperative Institute for Meteorological Satellite Studies Introduction and Motivation Several existing or promising satellite-based methods to estimate tropical cyclone (TC) intensity are available to forecasters today. Some of these, such as the Dvorak Technique, have been utilized operationally for over 30 years. Others, such as those based on microwave data, are just emerging as new, more capable, meteorological satellite instruments become operational. Each of the methods by themselves represents or promises significant contributions to TC intensity analysis. However, each technique (or instrument that it is based on) also has its limitations. An effort is underway at CIMSS to build an integrated algorithm that is fully automated and objective, and utilizes a multispectral approach. This system would build on, and take advantage of, the latest science advances in existing (and emerging) methods. Multi-Sensor Information Sharing Jeff Hawkins NRL at Monterey, CA United States Air force TC Intensity Estimation: Integrated Approach Basic Consensus (2 CIMSS Methods) Improving Center-Fix Methods Preliminary Results Two methods based on the study summarized in Wimmers and Velden, 26 th AMS Hurr Conf Corresponding author: [email protected] wisc. edu Using passive microwave. Example: TRMM Microwave Imager (TMI), 85 GHz overpass of Hurricane Isidore between the Yucatan peninsula and Cuba. “PCT” is a weighted difference between vertical and horizontal polarizations that indicates scattering by ice crystals and is a proxy for precipitation. Best track center, white cross; spiral-fitting score field, white contours; optimum spiral center, white square. Current Satellite-Based TC Intensity Estimation Methods Developed at CIMSS AODT Robert Wacker, AMSU N=214 AODT AMSU Consensus Hybrid Bias -1. 0 0. 56 -0. 22 0. 17 ABS Error 8. 6 5. 3 5. 5 4. 8 RMSE 10. 5 7. 0 6. 9 6. 1 All units in h. Pa. The ‘hybrid’ uses an additional predictor, which is the estimate spread of the 2 members in the consensus Weighted Ensemble (Multiple Methods) Super Typhoon 19 W Ensemble Intensity Estimate = 1/n wi (est)i CIMSS The weights (wi) represent the confidences of the various (n) algorithm estimates (esti). The confidence is based on performance characteristics of the algorithm as well as any additional factors such as data latency associated with polar orbiting satellite data. AMSU CIMSS/NESDIS-USAF/NRL Experimental AMSU TC Intensity Estimation: Storm position corresponds to AMSU-A FOV 8 [1<--->30] Raw Ch 8 (~150 h. Pa) Tb Anomaly: 5. 36 C Raw Ch 7 (~250 h. Pa) Tb Anomaly: 5. 34 C AMSU-A MSLP (Ch 8): 909. 9 h. Pa RMW value: 24. 0 Km Storm is sub-sampled based on RMW and FOV. Bias correction applied is: -15. 1 h. Pa SUPER TYPHOON 19 W Thursday 26 aug 04 Time: 0447 UTC Latitude: 23. 79 Longitude: 135. 960 Channel 8 Tb Anomaly Satellite: NOAA-16 For a complete description of the latest version of the CIMSS Advanced Microwave Sounding Unit (AMSU) algorithm, see the abstract by Herndon and Velden, 26 th AMS Hurr Conf For a complete description of the latest version of the Advanced Objective Dvorak Technique (AODT), see the abst by Olander, Velden and Kossin, 26 th AMS Hurr Conf Overall Performance AODT Statistics for Version 6. 3 CIMSS AMSU algorithm performance for storms from 2001 -2004 using latest algorithm logic Homogeneous (independent) data sample of 522 cases from 2003 Units in (h. Pa) Bias AODT (auto) RMSE 2. 40 Op Center Abs. Err. 9. 93 2. 67 MSLP (h. Pa) 1. 0 5. 0 Satellite Estimates of RMW 7. 8 333 SSMI/TMI/AMSRE Automated intensity estimates from passive microwave imagery 8. 9 N AMSU Other Methods as Potential Candidates for the Ensemble Existing Method – Microwave-Based - Subjective 6. 3 RMSE 9. 33 Using IR data. Example: GOES IR image of Hurricane Juan; initial guess of TC center based on a forecast, black triangle; spiral-fitting score field, white contours; area used in calculating the score field, gray circle; optimum eye ring, black circle. 1. 4 Mean Error AODT Integrated Satellite-Based TC Intensity Estimation System Dvorak Bias 8. 08 11. 81 CIMSS AMSU Microwave Imagery 333 Situational Performance AMSU Confidence Scenarios AODT Statistics for Version 6. 3 Stratified by Post-Eye and Scene Type Bias RMSE Abs. Err Sample All Scenes -0. 06 0. 57 0. 43 2160 All Eye Scenes -0. 08 0. 50 0. 40 1063 CDO -0. 04 0. 63 0. 46 RMSE = 6. 16 km R 2 = 0. 60 Storm core well defined 0. 52 0. 41 555 Embedded Center 0. 10 0. 52 0. 38 140 Irregular CDO 0. 01 0. 55 0. 47 Nadir FOV matches storm center -0. 07 0. 80 0. 61 -0. 12 0. 81 0. 58 T-Number relates to TC Vmax via the Dvorak relationship. T-Number increments give a more realistic representation of actual intensity change due in part to the nonlinear relationship between MSLP and Vmax ATCF Multiple storm ‘cores’ Wrong choice of RMW can lead to large estimate error Near limb FOV offset from storm center FOV captures fraction of warming Relationship between eye size, as measured by IR, and aircraft-measured RMW, for cleareye Atlantic TC cases (AODT now provides these RMW estimates for clear-eye scenes). 1. 6 -0. 5 5. 1 Absolute Error FOV captures all of warming Poor Confidence 262 IR Estimate 5. 4 6. 8 8. 3 7. 5 8. 7 50 50 50 “Computer Vision” Approach Uses pattern recognition techniques to extract TC characteristics in SSM/I imagery (85 GHz). Bankert and Tag, 2002 (JAM) Summary As part of an R&D effort at CIMSS to develop improved TC intensity estimation from satellites, existing methods to estimate intensity from different satellite platforms/sensors are being employed to create a more robust and reliable integrated approach. Taking advantage of the single method characteristics and situational tendencies, the final TC intensity estimate at a given analysis time will be obtained by employing a weighted consensus, decision tree, or “expert system” technique to blend/resolve the independent estimates. The algorithm will output both TC intensity parameters and confidence indicators. 10. 6 N Accurate sub/over-sampling corrections 68 Shear Best Guess Bias 72 Curved Band MSLP (h. Pa) Example: SSMI 85 GHz and Rain Rate features RMSE 1097 -0. 04 Empirical method employed at JTWC Using SSMI and TRMM/TMI New Method – IR-Based - Objective High Confidence Units in T-Number All No Eye Scenes 89 GHz defines eye based on ice scattering in the eyewall Colors represent confidence (green high, red low). The colored bars indicate ‘probabilities’ based on climate/persistence. The final estimate is a weighted blend with error bars (black). AMSU Intensity estimates using IR RMW method perform better than using ATCF RMW on independent cases verified against Atlantic recon. Empirical Approach Correlates patterns in SSMI imagery with Dvorak-like patterns. Edson and Lander, 2002 (Proc. Of 25 th AMS Hurricane Conf. ) This work is being sponsored by the Office of Naval Research, Program Element (PE-0602435 N), the Oceanographer of the Navy through the program office at the PEO C 4 I&Space/PMW-180 (PE 0603207 N), and the Naval Research Laboratory-Monterey.