1. CIMSS/NESDIS-USAF/NRL Experimental AMSU TC Intensity Estimation: Storm position corresponds to AMSU-A FOV 8 [1 30] Raw Ch8 (~150 hPa) Tb Anomaly: 5.36 C Raw Ch7 (~250 hPa) Tb Anomaly: 5.34 C AMSU-A MSLP (Ch8): 909.9 hPa RMW value: 24.0 Km Storm is sub-sampled based on RMW and FOV. Bias correction applied is: -15.1 hPa SUPER TYPHOON 19W Thursday 26aug04 Time: 0447 UTC Latitude: 23.79 Longitude: 135.960 Satellite: NOAA-16 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 Robert Wacker, United States Air force Jeff Hawkins NRL at Monterey, CA Uses pattern recognition techniques to extract TC characteristics in SSM/I imagery (85 GHz). Bankert and Tag, 2002 (JAM) “Computer Vision” Approach Automated intensity estimates from passive microwave imagery Example: SSMI 85 GHz and Rain Rate features 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. 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. Integrated Satellite-Based TC Intensity Estimation System AMSU Microwave Imagery AODT 89 GHz defines eye based on ice scattering in the eyewall 1/n  w i (est) i Ensemble Intensity Estimate = The weights (w i ) represent the confidences of the various (n) algorithm estimates (est i ). 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. AODTAMSUConsensus 7.0 5.3 0.56 6.16.910.5RMSE 4.85.58.6ABS Error 0.17-0.22Bias N=214 Hybrid AODT Statistics for Version 6.3 Homogeneous (independent) data sample of 522 cases from 2003 9.3311.812.67Op Center 8.08 9.932.40AODT (auto) Abs. Err.RMSEBiasUnits in (hPa) Stratified by Post-Eye and Scene Type 680.610.80-0.07Curved Band 720.470.55 0.01Irregular CDO 1400.380.52 0.10Embedded Center 2620.580.81-0.12Shear 10630.400.50-0.08All Eye Scenes 10970.460.63-0.04 All No Eye Scenes 0.41 0.43 AbsErr 555 2160 Sample 0.57-0.06All Scenes RMSEBiasUnits in T-Number 0.52-0.04CDO AODT Statistics for Version 6.3 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 CIMSS AMSU Super Typhoon 19W Introduction and Motivation Current Satellite-Based TC Intensity Estimation Methods Developed at CIMSS 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. Corresponding author: Chrisv@ssec.wisc.edu AODT AMSU 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 Overall Performance Multi-Sensor Information Sharing Improving Center-Fix Methods Satellite Estimates of RMW TC Intensity Estimation: Integrated Approach Basic Consensus (2 CIMSS Methods) Preliminary Results Weighted Ensemble (Multiple Methods) Situational Performance Other Methods as Potential Candidates for the Ensemble SSMI/TMI/AMSRE Empirical Approach Correlates patterns in SSMI imagery with Dvorak-like patterns. Edson and Lander, 2002 (Proc. Of 25 th AMS Hurricane Conf.) High Confidence Storm core well defined Nadir FOV FOV matches storm center Multiple storm ‘cores’ Poor Confidence Near limb FOV FOV offset from storm center AMSU Confidence Scenarios Accurate sub/over-sampling corrections Wrong choice of RMW can lead to large estimate error FOV captures all of warming FOV captures fraction of warming 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). All units in hPa. The ‘hybrid’ uses an additional predictor, which is the estimate spread of the 2 members in the consensus Two methods based on the study summarized in Wimmers and Velden, 26 th AMS Hurr Conf Empirical method employed at JTWC Using SSMI and TRMM/TMI CIMSS AMSU algorithm performance for storms from 2001-2004 using latest algorithm logic 6.35.0Mean Error 8.97.8RMSE 1.41.0Bias DvorakCIMSS AMSUMSLP (hPa) 333 N 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. This work is being sponsored by the Office of Naval Research, Program Element (PE-0602435N), the Oceanographer of the Navy through the program office at the PEO C4I&Space/PMW-180 (PE- 0603207N), and the Naval Research Laboratory-Monterey. 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 Channel 8 Tb Anomaly MSLP (hPa) Best Guess IR EstimateATCF Bias1.6-0.55.1 Absolute Error 5.46.88.3 RMSE7.58.710.6 N50 AMSU Intensity estimates using IR RMW method perform better than using ATCF RMW on independent cases verified against Atlantic recon. RMSE = 6.16 km R 2 = 0.60 Relationship between eye size, as measured by IR, and aircraft-measured RMW, for clear- eye Atlantic TC cases (AODT now provides these RMW estimates for clear-eye scenes). Existing Method – Microwave-Based - Subjective New Method – IR-Based - Objective

2. Integrated Satellite-Based TC Intensity Estimation System AMSU Microwave Imagery AODT The weights (w i ) represent the confidences of the various (n) algorithm estimates (est i ). The confidences will be based on performance characteristics of the algorithm as well as any additional factors such as data latency associated with polar orbiting satellite data. Ensemble Intensity Estimate = 1/n  w i (est) i Colors represent confidences (green high, red low). The colored bars indicate ‘probabilities’ based on climate/persistence. The final estimate is a weighted blend with error bars (black). Approach