UWCIMSS Tropical Cyclone Research : Current Progress and Developments

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UW-CIMSS Tropical Cyclone Research Current
Progress and Developments

• Tim Olander, Chris Velden,
• Jim Kossin, and Derrick Herndon

2004 Meteorological Satellite Coordinators
Conference Pearl Harbor, HI
Research and development funding provided by
Naval Research Laboratory - Monterey,
CA Funding contract N00173-01-C-2024
through Space and Naval Warfare Systems Command
(PMW-155) under PE 0603207N
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UW-CIMSS Tropical Cyclone Research
Briefing Overview

• Advanced Objective Dvorak Technique (AODT)
• Tropical cyclone Intensity Estimate (TIE) Model
• AMSU
• Environmental Shear Estimates

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AODT Latest Improvements

• Added new scene type classifications
• Separate classifications for eye and surrounding
  cloud regions
• Eye Clear, Pinhole, Large, Ragged, Obscured,
  None
• Cloud Uniform, Embedded Center, Irregular CDO,
  Curved Band, Shear
• Scenes more closely mimic Dvorak Technique
  classifications
• Convective symmetry parameter used to classify
  cloud region
• Expanded for tropical storm and weaker systems
• Utilize Curved Band analysis as defined in
  Dvorak EIR Technique
• to estimate cloud curvature signatures
• Irregular CDO scene covers transition scenes
• Removed Rapid Flag / modified time averaging
  scheme
• Final T changed from 12-hour to 6-hour weighted
  average
• New technique still allows for rapid
  intensification

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AODT Latest Improvements

• Modified land interaction rule
• Analysis suspended while storm is over land
  (keyword override)
• Rule 9 application stopped after storm is over
  land for gt12 hours
• Added Dvorak EIR Technique Rule 8
• Constrains growth/decay rate of storm intensity
  over time
• Position of maximum Curved Band analysis
  determined
• Location provided in addition to manual position
  values

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New Eye Scene Classifications
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New Cloud Scene Classifications
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AODT Latest Improvements (continued)

• Latitude bias adjustment applied to CI value
• A bias was found in Dvorak EIR Technique related
  to latitude
• The bias is caused by the latitudinal variation
  of eyewall
• cloud-top temperature, resulting from the
  latitudinal
• variation of tropopause temperature
• Bias adjustment increases/decreases intensity
  for storm
• positions north/south of approximately 22ºN
  latitude
• Adjustment based on linear regression and
  applied to CI value
• Adjusted and unadjusted CI values stored in
  AODT history file
• Reduces MSLP estimate error by about 10 in test
  cases
• This bias should also be applied to conventional
  Dvorak method

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Latitude-dependent bias in Dvorak Enhanced IR
(EIR)technique applied to Atlantic TCs.
The bias explains 15 to 20 of the variance of
error.
The overall bias is always between 1.5mb and
appears benign, but the latitude-dependent bias
is as large as 10mb at 10N and 14mb at 40N.
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Removal of the bias decreases RMSE 9 to 11
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Physical explanation
47 of the variance of cloud-top temperature is
explained
IR-measured eyewall cloud-top temperature is a
fundamental input parameter of the Dvorak EIR
technique (via a nomogram) and depends on
latitude much more strongly than intensity does.
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Application to other oceanic basins
Mean tropopause temperature (NCEP reanalysis).
Averaged over AUG SEP OCT (FEB MAR APR) in
Northern (Southern) hemisphere.
WMO-RMSC regions
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AODT-v6.3 Results Including Latitude Bias
Adjustment
Homogeneous Comparisons with Aircraft
Reconnaissance (26 storms between 1995 and 2002)
All Tropical Cyclone Intensities (MSLP) (errors
in millibars) Bias RMSE
Sample AODT adj 0.09 9.50 1630 AODT unadj
0.42 10.72 1630 Op Centers 0.22 10.65 1630
Note Positive error indicates underestimate
(e.g. AODT minus Recon)
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AODT Latest Improvements (continued)

• Improved automated center location determination
  scheme
• Operational Center forecast used as first guess
• JTWC or NHC forecasts used in conjunction with
• 6 and 12 hour old positions from AODT history
  file
• Current position interpolated using polynomial
  interpolation scheme
• Laplacian Analysis of cloud top region performed
  
• Analysis region within 75 km radius of
  interpolated forecast position
• Identifies pixels with strong gradients in cloud
  top temperature field
• Confidence factors derived for each methodology
• Forecast Time difference from
  Initial/Warning position
• Laplacian Density and scatter of large
  temperature gradients
• Laplacian typically only used where strong but
  concentrated
• temperature gradient fields are identified (e.g.
  Eye Scenes)

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AODT Auto-Positioning Comparison Hurricane Floyd
AODT Auto-Position Clear Eye ? 6.2 T
NHC Interpolated Forecast Position Uniform Cloud
Region ? 4.5 T
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AODT-v6.3 Results Automated AODT
Homogeneous Comparisons with Aircraft
Reconnaissance
Dependent Data Sample (26 storms between
1995-2002) (errors in millibars) Bias
RMSE Sample AODT Manual 0.09
9.50 1630 AODT Automated 1.70 10.04 1630 Op
Centers 0.22 10.65 1630
Independent Data Sample (5 storms in
2003) (errors in millibars) Bias RMSE
Sample AODT Automated 2.40 9.93 522 Op
Centers 2.67 11.81 522
Note Positive error indicates underestimate
(e.g. AODT minus Recon)
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AODT-v6.3 Results
Homogeneous Comparisons with Aircraft
Reconnaissance
Statistical Breakdown by AODT scene type (errors
in T units) Bias RMSE AAE Sample All
Points 0.11 0.67 0.51 3735 All
Eye Scenes -0.08 0.50 0.40 1063
Clear Eye -0.02 0.42 0.32 445
All other Eye -0.12 0.55 0.46
618 All Non-Eye Scenes 0.19 0.73 0.56
2672 Uniform CDO 0.17 0.64 0.51
1093 Embedded Center 0.19 0.66 0.46
193 Irregular CDO 0.11 0.70 0.55
278 Curved Band 0.17 0.73 0.60
324 Shear 0.25 0.85 0.64 784
Note Positive error indicates overestimate (e.g.
AODT minus Recon)
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AODT Future Directions

• Integrate regression-based TIE Model with AODT
• Use 2003 TC results to guide development of
  combined method
• Examine additional geostationary channels
• IRW-WV channel difference (Velden and Olander,
  1998)
• Additional Dvorak methods using visible and/or
  shortwave infrared
• Investigate additional satellite information
• AMSU analysis using technique developed at
  UW-CIMSS
• SSM/I and TRMM analysis based on work by
  NRL-MRY/R. Edson
• GOAL
• Develop an advanced multi-sensor objective
  technique
• Fuse results/output from different instruments
  and analysis techniques into an expert system

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AODT Availability

• The latest version of the AODT is currently
  available to any/all interested users for McIDAS
  platforms from the AODT webpage (along with the
  updated Users Guide)
• The latest version of the AODT is being
  integrated into the TeraScan system for use at
  JTWC during the 2004 TC season. The code has
  also been adapted for N-AWIPS at NOAA sites and
  Mark-IVB at AFWA
• An X-Window/Motif version of the AODT has also
  been developed and will is available for UNIX and
  Linux based systems (also available at the AODT
  webpage)

AODT webpage http//cimss.ssec.wisc.edu/tropic/aod
t
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• The Tropical cyclone Intensity Estimation (TIE)
  model
• Developed to formally explore the relationships
  between TC intensity and features in
  geostationary satellite infrared (IR) imagery,
  without the constraints of the Dvorak-based
  methods and their attendant rules.
• Applicable at all stages of TC lifetime.
• Completely objective no scene-typing.

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TIE-model
multivariate linear regression
TC central pressure (MSLP) measured from aircraft
reconnaissance is regressed onto 5 predictors
63 of the MSLP variance is explained by the
regression.
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TIE-model performance
Errors relative to recon. TIE-model errors based
on independent jackknife analysis.
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TIE-model performance
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• TIE-model RD
• Serves as a natural platform for testing
  additional parameters for correlation with TC
  intensity.
• Indices derived from synoptic fields (analogous
  to SHIPS) are being presently tested.
• Further development will include indices derived
  from microwave imagery these can include indices
  related to eyewall replacement cycles.
• TIE model estimates will be available in
  conjunction with AODT estimates on TeraScan
  system this coming season.

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AMSU Background

• What does AMSU really observe?

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Suitability of the AMSU
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CIMSS AMSU-based TC Intensity Estimation Algorithm

• Extrapolate storm position for AMSU pass time
• based on latest warning
• Find max AMSU Tb in ch7 and 8 within 100km
• of storm position (this is the TC upper-level
  warm core)
• Average environmental Tbs from 4 surrounding
  points and
• subtract from core value to determine thermal
  anomaly
• Estimate MSLP for ch7 and ch8 anomalies based on
• statistical regression. Use lower of the two
  values.
• Apply FOV bias correction based on RMW or...
• Call channel 7 retrieval if
• Raw Ch7 Tb anomaly gt Raw Ch8
• Raw Ch7 anomaly gt 1.0K
• Storm is located near the edge of the satellite
  swath

X
X
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2002 Results (MSLP)
Statistics for Atlantic (N 60) in
hPa CIMSS Dvorak Mean Error 3.29
4.52 Std Dev 2.30 3.40 Bias -0.08
-0.90 RMSE 4.00 5.62 Avg MSLP of sample
1002.4
Statistics for Pacific (N 10) in
hPa CIMSS JTWC Mean Error 8.00 11.16 Std
Dev 7.25 8.79 Bias 4.79 6.10 RMSE
10.57 13.93 Avg MSLP of sample 964.4
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MSLP
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2002 Atlantic Basin Results
NOAA/NESDIS Satellite Analysis Branch (SAB)
vs. AMSU (N34, /- 2hrs of aircraft
reconnaissance)
SAB 78 within /- 0.5T
AMSU 81 within /- 0.5T
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Sources of Error

• Radius of Maximum Winds
• Used as a proxy for eye size in algorithm
• Especially important for small intense storms
• How is it determined?

• Position
• First Guess comes from Warning Message
• Search algorithm locates max anomaly
• Sheared systems

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Future Work
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Summary and Conclusions
AMSU provides unique tropical cyclone perspective

• Can see beneath upper level cloud cover
• 55 Ghz region radiances can quantify inner core
  thermal
• structure / changes.
• Temperature anomaly strength directly associated
  with tropical
• cyclone intensity
• A unique addition to the forecaster tool kit

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UW-CIMSS/NESDIS wind shear products in Hurricane
Lili (2002)
Deep-layer
Tendency
Mid-level
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CIMSS experimental vertical shear product
(Gallina and Velden 2002)
TROPICAL STORM KYLE 1800UTC
07October2002 UW-CIMSS Experimental Vertical
Shear TC Intensity Trend Estimates Current
Conditions Latitude

321927 N Longitude

705045 W Intensity
(MSLP) 1005.0
hPa Max Pot Int (MPI)
971.7 hPa
MPI differential (MSLP-MPI)
33.3 hPa CIMSS
Vertical Shear Magnitude 2.8 m/s

Direction 215.0 deg Outlook
for TC Intensification Based on Current Env.
Shear Values Forecast Interval 6hr
12hr 18hr 24hr
F F
N N Legend VF -
Very Favorable F - Favorable N - Neutral
U Unfavorable
VU - Very Unfavorable -- Mean Intensity
Trend (negative indicates TC deepening) --
6hr 12hr
18hr 24hr VF lt
-3.0mb/ 6hr lt -6.0mb/12hr lt -9.0mb/18hr lt
-12.0mb/24hr F -3.0 - -1.5 -6.0
- -3.0 -9.0 - -4.5 -12.0 -
-6.0 N -1.5 - 1.5 -3.0 - 3.0
-4.5 - 4.5 -6.0 - 6.0 U
1.5 - 3.0 3.0 - 6.0 4.5 -
9.0 6.0 - 12.0 VU gt3.0
gt6.0 gt9.0
gt12
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SUMMARY During the 2002 TC season, a fully
automated algorithm based partially on
quantitative satellite data estimates of
environmental wind shear was used to derive
short-term TC intensity outlooks in real time.
The experimental output products were distributed
by CIMSS via email to selected Tropical Analysis
Centers. Preliminary analysis and forecaster
feedback suggests these products can be quite
useful as indicators of favorable/unfavorable
tropospheric environments precluding TC
deepening/filling trends. Quantitative
relationships between shear and short-term TC
intensity trends were developed based on an
exhaustive statistical analysis of shear fields
produced at CIMSS using satellite-derived wind
information. The resulting prognostic algorithm
shows preliminary evidence of skill over SHIPS
and other methods in some cases.
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RESULTS 1) Qualitatively, the new shear products
are showing promise to aid the TC intensity
forecast process. Initial assessment and feedback
from Tropical Analysis Centers has been quite
favorable. 2) In general, intensity trends are
related to changes in the environmental shear as
depicted by the CIMSS fields and outlook
products. The short term (24-hr) intensity trend
outlook (deepening or filling) was correct 93 of
the time. 3) Case studies illustrate situations
when shear thresholds and trends in shear can be
indicators of TC intensity changes. 4) The more
quantitative, experimental outlook product based
on a statistical analysis of the CIMSS shear
estimates vs. observed TC intensity was skillful
relative to SHIPS and other guidance in Lili, but
not in Kyle. Further analysis is underway.