Transcript IR Satellite Applications -- Tropical cyclone structure

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
Cooperative Institute for Meteorological Satellite Studies
Madison, Wisconsin USA
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
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
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
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
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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
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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)
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T5.0 (90 kt)
T6.0 (115 kt)
T6.5 (127.5 kt)
T7.0 (140 kt)
T7.5 (155 kt)
T7.6 (158 kt)
T7.6 (158 kt)
-60
-64
-68
-71
-75
-76
-79
T-eye
-45
-5
+5
+11
+14
+14
-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)
AND
Time averaging
Are 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
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.
Other simple IR data applications
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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 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.
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
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)
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 imagery
and TC intensity:
Correlation of IR Tb with
best track wind in
Hurricane Bret (1999)
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.
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
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.
Wilma Rapid Intensification period
Wilma RSO Center-relative
Wilma 4-h Center-relative
Average Images at 2-h interval