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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.

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Presentation on theme: "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."— Presentation transcript:

1 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 Cooperative Institute for Meteorological Satellite Studies Madison, Wisconsin USA International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006

2 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 International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006

3 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 International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006

4 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 International Workshop on Tropical Cyclones San Jose, Costa Rica November 22, 2006

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

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15 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

16 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

17 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)

18 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

19 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

20 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

21 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

22 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

23 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

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25 Primary ADT upgrades since original ODT description -Expanded analysis range to operate on TD/TS stages of TC lifecycle -Added new scene type categories for cloud and eye regions (Table 1) -Modified intensity determination scheme for EYE and CDO scenes (regression-based determination with new predictors) -Added a modified DT Step 9 (weakening rule) -Added a modified DT Step 8 (constraint rule) -Implemented new constraints dependent on situation and scene types -Modified surrounding cloud region temperature determination scheme (coldest ring average instead of warmest pixel temperature on ring) - Modified scene type determination scheme -Implemented improved automated storm center determination techniques -Added latitude bias adjustment to MSLP -Added radius of maximum wind (RMW) determination scheme -Modified time averaging technique period from 12 hours to 6 hours (3 hours in EYE scenes) -Added user scene override capability -Added new graphical and ATCF format output options

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28 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. Raw T#BiasRMSEAve. Error ODT16.8326.0719.93 ODT-A10.7820.0716.00 ADT 2.7815.4712.11 Final CI#BiasRMSEAbs. Error ODT12.6720.4515.00 ODT-A 4.2614.2110.20 ADT 0.5213.1610.25

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

30 Cold IR cloud area time series

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33 Azimuthal mean time series plots

34 IR asymmetry computations

35 Center relative IR average images

36 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.

37 Inclusion of IR-derived winds in numerical forecast models

38 Difference between ~11 and ~12 micrometer wavelength IR images

39 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.

40 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

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42 Objective IR identification of annular hurricanes -- 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 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. 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)

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44 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

45 Further statistical relationships between IR imagery and TC intensity: Correlation of IR T b with best track wind in Hurricane Bret (1999)

46 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.

47 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

48 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

49 High resolution IR images

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54 Tropical cyclone IR archives --- RAMM/CIRA (Zehr/Knaff) –4 km, 30 min interval, MCIDAS format –1995-2004, predominantly ATL, EPAC –Global Oct 2004 -- present ISCCP B1 (Knapp/Kossin) –8 km, 3 hr interval, NetCDF format, OnLine –Global 1983-2005

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56 In spite of shortcomings such as "cirrus obscuration", infrared imagery continues to be an extremely useful source of information for TC analysis and forecasting. The sheer historical volume of IR images readily allows for exploration of robust statistical relationships between cloud properties and TC structure, intensity, and intensity change. The operational availability, quick time latency, and frequent interval imaging, is invaluable for real-time use and forecasting. Combining and merging IR data with synoptic/environmental data (numerical analyses, ocean heat content, SST, etc) and additional remotely sensed fields (microwave imagers, sounders, scatterometer winds, etc) will optimize its utility. Summary

57 Wilma Rapid Intensification period

58 Wilma RSO Center-relative

59 Wilma 4-h Center-relative Average Images at 2-h interval


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