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