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Chair: Christopher Velden Cooperative Institute for Meteorological Satellite Studies Madison, Wisconsin USA

International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006. Topic 1: Tropical cyclone structure and structure change Special Focus Topic 1a: Tutorial on the use of satellite data to define TC structure. Chair: Christopher Velden

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Chair: Christopher Velden Cooperative Institute for Meteorological Satellite Studies Madison, Wisconsin USA

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  1. International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006 Topic 1: Tropical cyclone structure and structure changeSpecial Focus Topic 1a: Tutorial on the use of satellite data to define TC structure Chair: Christopher Velden Cooperative Institute for Meteorological Satellite Studies Madison, Wisconsin USA

  2. International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006 Special Focus Topic 1a: Tutorial on the use of satellite data to define TC structure Outline Introduction: Christopher Velden IR-Based Data and Methods: Ray Zehr MW-Based Data and Methods: Jeff Hawkins Questions: All

  3. International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006 Special Focus Topic 1a: Tutorial on the use of satellite data to define TC structure IR-Based TC Structure Applications(Ray Zehr) 1. Background 2. Basic IR image interpretation 3. TC Intensity algorithms 4. Cold IR cloud area time series 5. Azimuthal mean time series plots 6. IR asymmetry computations 7. Center relative IR average images 8. Inclusion of IR data into statistical forecast models 9. Inclusion of IR-derived winds in numerical forecast models 10. Saharan Air Layer (SAL) products 11. IR relationships with wind radii and TC structure 12. Objective IR identification of annular hurricanes 13. IR based short range structure change analysis/forecast 14. High resolution IR images 15. Tropical cyclone IR archives

  4. International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006 Special Focus Topic 1a: Tutorial on the use of satellite data to define TC structure MW-Based TC Structure Applications(Jeff Hawkins) 1. Background 2. Basic MW image interpretation 3. Windsat 4. Concentric eyewall structures 5. MW image morphing applications 6. COMET training module 7. AMSU applications 8. Consensus TC intensity algorithm development 9. Scatterometer TC applications 10. Summary

  5. IR Satellite Applications -- Tropical cyclone structure and structure change Ray Zehr IWTC-VI 22 Nov 2006

  6. Early applications • tracking (center fixing) • intensity following the Dvorak technique. • Those applications remain today as primary and important applications. • IR data quality, timeliness, frequency, displays, enhancements, etc. have improved.

  7. IR images - Basics • Spatial resolution • Time latency • Time interval • IR temperature pixel resolution

  8. IR images - Interpretation • Cold overshoots • Cirrus canopies obscuring TC centers and structure • IR temperature change – cooling vs warming • Combine with visible images • Combine with microwave images

  9. Intensity algorithms • 1. Dvorak – early 80s • 2. RAMM / CIRA – (Zehr) late 80s / 90s • 3. ODT -- (Velden/Olander) 1995-2001 • 4. AODT –(Olander/Velden) 2001-2004 • 5. ADT–(Olander/Velden) 2004-present

  10. Dvorak (1984) “digital IR” • Two IR measurements: • Eye Temperature – warmest eye pixel • Surrounding Temperature -- warmest pixel lying on a circle of R=55 km (1 deg lat diameter) Table gives T-No. to nearest 0.1 Vmax(kt) = 25T – 35 (for 65-140 kt)

  11. Typical “Eye” and “Surrounding” Temperatures associated with hurricane intensity T-surr (deg C)T-eye • T5.0 (90 kt) -60 -45 • T6.0 (115 kt) -64 -5 • T6.5 (127.5 kt) -68 +5 • T7.0 (140 kt) -71 +11 • T7.5 (155 kt) -75 +14 • T7.6 (158 kt) -76 +14 • T7.6 (158 kt) -79 -5

  12. CIRA/RAMM refinements to Dvorak digital IR intensity algorithm • 1. Expanded look-up table to handle observed IR measurements • 2. Multi-radius Surrounding Temperature measurements to use the coldest • 3. Intensity given by 6-hour average value, limited by weakening rate of 1.5 T / day

  13. Intensity algorithms Sampling (frequency of images) AND Time averaging Are IMPORTANT For obtaining results having: reasonable rates of intensity change… times of peaking and overall accuracy

  14. ODT : Objective Dvorak Technique, CIMSS, Olander / Velden Velden, C.S., T.L. Olander, and R.M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. and Forecasting, 13, 172-186 -- documented and validated objective algorithm and showed it to be competitive with the operational Dvorak technique -- some additional analysis added to handle weaker TCs

  15. AODT: Advanced Objective Dvorak Technique, CIMSS, Olander / Velden • 1) technique developed for tropical depression and storm stages • 2) implemented several additional rules and methodologies • 3) incorporated an automated storm center determination methodology

  16. ADT: Advanced Dvorak Technique, CIMSS, Olander / Velden Velden, C.S., and T.L. Olander, 2006: The Advanced Dvorak Technique (ADT) – continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Submitted, Wea. and Forecasting -- Implemented operationally at: TPC / NHC JTWC

  17. Table 4. Raw T# (top) and Final CI# (bottom) TC intensity estimate (MSLP) comparisons between ADT and ODT vs. aircraft reconnaissance measurements for a homogeneous sample of 1116 Atlantic cases from 1996-2005. ODT-A indicates ODT using storm center positions determined from ADT autocenter determination techniques. Positive bias indicates underestimate of intensity by the ODT/ADT techniques. Units are in hPa.

  18. Other simple IR data applications • Cold IR cloud area time series • Azimuthal mean time series plots • IR asymmetry computations • Center relative IR average images

  19. Cold IR cloud area time series

  20. Azimuthal mean time series plots

  21. IR asymmetry computations

  22. Center relative IR average images

  23. Inclusion of IR data into statistical forecast models • The GOES IR data significantly improved the east Pacific forecasts by up to 7% at 12–72 h. (DeMaria et al, 2005) • The GOES predictors are: • 1) the percent of the area (pixel count) from 50 to 200 km from the storm center where TB is colder than −20°C and • 2) the standard deviation of TB (relative to the azimuthal average) averaged from 100 to 300 km.

  24. Inclusion of IR-derived winds in numerical forecast models

  25. Difference between ~11 and ~12 micrometer wavelength IR images

  26. Saharan Air Layer (SAL) product (Dunion and Velden 2001) SAL interacting with Hurricane Erin (2001). The SAL consists of dust and dry lower-troposphere air that may impede TC intensification by increasing the local vertical shear, enhancing the low-level inversion, and intruding dry air into the TC inflow layer.

  27. IR relationships with wind radii and TC structure -- Mueller et al Mueller, K. J., M. DeMaria, J. A. Knaff, J. P. Kossin, and T. H. VonderHaar, 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, -- use aircraft observations along with statistical relationships with IR data to estimate radius of maximum wind and TC structure

  28. Objective IR identification of annular hurricanes Cram, T. A., J. A. Knaff, M. DeMaria, and J. P. Kossin, 2006: Objective identification of annular hurricanes using GOES and reanalysis data. 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, 24-28 April 2006. -- developed algorithm that uses IR data to objectively identify annular hurricanes. The algorithm is based on linear discriminant analysis, and is being combined with a similar algorithm being developed at CIMSS What is an “annular hurricane” ? “hurricane that is distinctly more axisymmetric with a large circular eye surrounded by a nearly uniform ring of deep convection and a curious lack of deep convective features outside this ring” (Knaff, et al 2003)

  29. IR relationships with wind radii and TC structure -- Kossin et al 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, 2006a: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Submitted, Wea. Forecasting. • applied IR data to new objective methods of estimating radius of maximum wind (RMW), and standard operational wind radii (R-34, R-50, R-64). • routine developed to generate the entire 2-dimensional wind field within 200 km radius. • w/ IR images with eye: • RMW ~ -45C IR isotherm

  30. Further statistical relationships between IR imagery and TC intensity: Correlationof IR Tb with best track wind in Hurricane Bret (1999)

  31. First PC of the IR imagery correlated with the sequence of H*Wind fields in Hurricane Gordon (2000) Maximum Correlation Analysis (MCA) will be performed using IR sequences and H*Wind fields (and QuikSCAT) to deduce formal relationships between 2D IR and wind fields. Collaboration between CIMSS, CIRA, and HRD.

  32. IR relationships with wind radii and TC structure -- Kossin et al Kossin, J., H. Berger, J. Hawkins, and T. Cram, 2006: Development of a Secondary Eyewall Formation Index for Improvement of Tropical Cyclone Intensity Forecasting. Proceedings of the 60th Interdepartmental Hurricane Conference, Mobile, AL -- found that IR imagery does contain information about the onset of eyewall replacement cycles by using Principal Component Analysis to enhance the signal to noise ratio -- information was combined with other information from microwave imagery and environmental fields to form an objective index to calculate the probability of secondary eyewall formation

  33. TOPICS • on IR based structure change analysis / short range forecast • IR based information on inner core (intensity and RMW) along with “size” • onset of rapid intensification • onset of eyewall replacement cycles • pressure-wind relationship

  34. High resolution IR images

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