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 CyclonesSan Jose, Costa RicaNovember 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
International Workshop on Tropical CyclonesSan Jose, Costa RicaNovember 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 algorithms4. 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) products11. IR relationships with wind radii and TC structure 12. Objective IR identification of annular hurricanes 13. IR based short range structure change
analysis/forecast14. High resolution IR images15. Tropical cyclone IR archives
International Workshop on Tropical CyclonesSan Jose, Costa RicaNovember 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. Windsat4. 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 CyclonesSan Jose, Costa RicaNovember 22, 2006
IR Satellite Applications -- Tropical cyclone structure and
structure change
Ray Zehr
IWTC-VI
22 Nov 2006
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.
IR images - Basics
• Spatial resolution
• Time latency
• Time interval
• IR temperature pixel resolution
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
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
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)
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
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
Intensity algorithms
Sampling (frequency of images) ANDTime averagingAre IMPORTANT For obtaining results having:
reasonable rates of intensity change… times of peaking
and overall accuracy
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
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
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
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
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# Bias RMSE Ave. Error
ODT 16.83 26.07 19.93
ODT-A 10.78 20.07 16.00
ADT 2.78 15.47 12.11
Final CI# Bias RMSE Abs. Error
ODT 12.67 20.45 15.00
ODT-A 4.26 14.21 10.20
ADT 0.52 13.16 10.25
Other simple IR data applications
• Cold IR cloud area time series
• Azimuthal mean time series plots
• IR asymmetry computations
• Center relative IR average images
Cold IR cloud area time series
Azimuthal mean time series plots
IR asymmetry computations
Center relative IR average images
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.
Inclusion of IR-derived winds in numerical forecast models
Difference between ~11 and ~12 micrometer wavelength IR images
Saharan Air Layer (SAL)product (Dunion andVelden 2001)
SAL interacting withHurricane Erin (2001).The SAL consists of dustand dry lower-troposphereair that may impede TCintensification by increasing the local vertical shear, enhancing the low-level inversion, and intruding dry air into the TC inflow layer.
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
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)
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
Further statistical relationships between IR imageryand TC intensity:
Correlation of IR Tb withbest track wind in Hurricane Bret (1999)
First PC of the IR imagerycorrelated with the sequenceof H*Wind fields inHurricane Gordon (2000)
Maximum Correlation Analysis (MCA) will be performed using IR sequencesand H*Wind fields (and QuikSCAT) to deduce formal relationships between2D IR and wind fields.
Collaboration between CIMSS, CIRA, and HRD.
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
• 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
High resolution IR images
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
In spite of shortcomings such as "cirrus obscuration", infrared imagerycontinues 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 propertiesand 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
Wilma Rapid Intensification period
Wilma RSO Center-relative
Wilma 4-h Center-relative Average Images at 2-h interval