Topic 1: Tropical cyclone structure and structure change Special Focus Topic 1a: Tutorial on the use of satellite data to define TC structure

1
Topic 1 Tropical cyclone structure and structure
changeSpecial Focus Topic 1a Tutorial on the
use of satellite data to define TC structure
International Workshop on Tropical Cyclones San
Jose, Costa Rica November 22, 2006

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

2
Special Focus Topic 1a Tutorial on the use of
satellite data to define TC structure
International Workshop on Tropical Cyclones San
Jose, Costa Rica November 22, 2006

• Outline
• Introduction Christopher Velden
• IR-Based Data and Methods Ray Zehr
• MW-Based Data and Methods Jeff Hawkins
• Questions All

3
Special Focus Topic 1a Tutorial on the use of
satellite data to define TC structure
International Workshop on Tropical Cyclones San
Jose, Costa Rica November 22, 2006

• 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
Special Focus Topic 1a Tutorial on the use of
satellite data to define TC structure
International Workshop on Tropical Cyclones San
Jose, Costa Rica November 22, 2006

• 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
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
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 R55 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
24
(No Transcript)
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
26
(No Transcript)
27
(No Transcript)
28
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
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.
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
31
(No Transcript)
32
(No Transcript)
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 1272 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 -20C 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
41
(No Transcript)
42
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)
43
(No Transcript)
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 Tb with best track wind in
Hurricane Bret (1999)
46
First PC of the IR imagery correlated with the
sequence of HWind fields in Hurricane Gordon
(2000)
Maximum Correlation Analysis (MCA) will be
performed using IR sequences and HWind 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
50
(No Transcript)
51
(No Transcript)
52
(No Transcript)
53
(No Transcript)
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

55
(No Transcript)
56
Summary
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.
57
Wilma Rapid Intensification period
58
Wilma RSO Center-relative
59
Wilma 4-h Center-relative Average Images at 2-h
interval