TC Intensity Estimation: SATellite CONsensus (SATCON)

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TC Intensity Estimation SATellite CONsensus
(SATCON)

• Derrick Herndon and Chris Velden

University of Wisconsin - Madison Cooperative
Institute for Meteorological Satellite Studies
Interdepartmental Hurricane Conference Savannah,
GA 01-04 March 2010
Research supported by the ONR Marine Meteorology
and Atmospheric Effects Program
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Motivation

• Importance of getting current TC intensity right
• Intensification trends gt forecasts
• Predictor for statistical forecast models
• Climatology (Basin Best Tracks)
• Initial conditions for numerical models
• Contemporary methods to estimate TC intensity
  can vary by more than 40 knots
• Several objective TC intensity methods exist,
  but the goal of SATCON is to assist forecasters
  in assessing current intensity by combining the
  confident aspects of the individual objective
  estimates into a single best estimate

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Motivation
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Motivation
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SATCON Members

• ADT (Advanced Dvorak Technique)
• Uses IR imagery to objectively assess storm
  cloud patterns and
• structure to infer intensity
• Latest version uses information from MW to make
  adjustments

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SATCON Members CIMSS AMSU
Channel 8
150 mb
Channel 7
250 mb
55 Knots
Channel 6
350 mb
AMSU Tb Anomaly vertical cross section for
Katrina 2005
70 Knots
TC Pressure Anomaly Magnitude
125 knots
AMSU Channel 8 Tb Anomaly Magnitude
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SATCON Members CIRA AMSU
AMSU-A Tb are used to produce a statistical
temperature retrieval at 23 pressure levels.
Estimates of Vmax are then determined from the
thermal warm core structure.
IR image from NRL TC Page
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SATCON
The strengths and weaknesses of each method are
assessed based on statistical analysis, and that
knowledge is used to assign weights to each
method in the consensus algorithm based on
situational performance to arrive at a single
intensity estimate
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Another component of SATCON is cross-method
information sharing

• What relationships might exist between the
  parameters of
• the member algorithms?
• Can some of the unique information from these
• parameters be shared between the algorithms to
  improve
• the individual members?
• Corrections can be made to improve the
  performance of
• each algorithm, then the weights re-derived to
  produce an
• improved weighted consensus

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SATCON cross-method information sharing
ADT Estimate of Eye Size
Example ADT to AMSU In eye scenes, IR can be
used to estimate eye size CIMSS AMSU uses eye
size information to correct resolution
sub-sampling
Compare to AMSU-A FOV resolution
Adjust AMSU pressure if needed
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Information Sharing
Example Objective estimates of eye size from
CIMSS ARCHER method (using MW imagery)
Currently, AMSU uses IR-based eye size or values
from op center if no eye in IR. MW imagery (MI)
often depicts eyes when IR/ADT cannot ARCHER
method (Wimmers and Velden, 2010) uses objective
analysis of MI and accounts for eyewall slope
ARCHER eye 33 km Information can be
input to AMSU method
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SATCON Weighting Scheme

• Weights are based on situational analysis for
  each member
• Separate weights for MSW and MSLP estimates
• Example criteria scene type (ADT)
• scan geometry/sub-sampling (AMSU)

Example ADT Scene type vs. performance
SHEAR
CDO
EYE
RMSE 14 knots
RMSE 12 knots
RMSE 18 knots
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Examples
ADT determines scene is an EYE CIMSS AMSU
Good, near nadir pass. Eye is well resolved by
AMSU resolution CIRA is sub-sampled by FOV
offset with TC center
SATCON Weighting ADT 28 CIMSS AMSU 47
CIRA AMSU 25
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Examples
ADT determines scene is a SHEAR scene CIMSS
AMSU indicates no sub-sampling present CIRA
AMSU little sub-sampling due to position offset
from FOV center
Center of TS Chris
SATCON Weighting ADT 18 CIMSS AMSU 41
CIRA AMSU 41
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1999-2009 performance stats (Vmax) - Atlantic
N 460 CIMSS AMSU CIMSS ADT CIRA AMSU SATCON
BIAS 4.0 - 5.0 -8.6 -1.0
AVG ERROR 9.1 11.5 12.3 7.2
RMSE 10.2 13.5 14.6 8.3
Dependent sample. Values in knots. Validation
is best track Vmax coincident with aircraft
recon /- 3 hours from estimate time. Negative
bias method was too weak.
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1999-2009 SATCON compared to a simple straight
consensus (Atlantic)
N 460 SATCON MSLP SIMPLE MSLP SATCON Vmax SIMPLE Vmax
BIAS 0.3 -2.5 -1.0 - 4.0
AVG ERROR 5.2 5.7 7.2 8.1
RMSE 6.4 7.7 8.3 9.3
Dependent sample. Vmax validation in knots vs.
BT. MSLP validation in hPa vs. recon. Negative
bias method was too weak. SIMPLE is simple
average of the 3 members
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1999-2009 SATCON compared to operational Dvorak
(Atlantic)
N 460 SATCON MSLP Dvorak MSLP SATCON Vmax Dvorak Vmax
BIAS 0.3 -2.7 -1.0 -3.0
AVG ERROR 5.2 7.6 7.2 8.1
RMSE 6.4 9.1 8.3 9.0
Dependent sample. Vmax validation in knots vs.
BT. MSLP validation in hPa vs. recon. Neg. bias
method was too weak. Dvorak is average of TAFB
and SAB estimates
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SATCON Web Site
http//cimss.ssec.wisc.edu/tropic2/real-time/satco
n
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Summary
A weighted consensus of three objective
satellite-based methods to estimate TC intensity
(SATCON) shows skill compared to conventional
Dvorak-based methods. Independent trials during
2008 and 2009 in the Atlantic support the
dependent sample results. SATCON also showed
skill vs. other methods in the WestPac during
TPARC/TCS-08 in 2008 (small sample of validated
cases). SATCON is run experimentally on all
global TCs in real-time, with the information
available on the CIMSS TC web site.
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References
Brueske K. and C. Velden 2003 Satellite-Based
Tropical Cyclone Intensity Estimation Using the
NOAA-KLM Series Advanced Microwave Sounding
Unit (AMSU). Monthly Weather Review Volume
131, Issue 4 (April 2003) pp. 687697 Demuth J.
and M. DeMaria, 2004 Evaluation of Advanced
Microwave Sounding Unit Tropical-Cyclone
Intensity and Size Estimation Algorithms.
Journal of Applied Meteorology Volume 43, Issue 2
(February 2004) pp. 282296 Herndon D. and C.
Velden, 2004 Upgrades to the UW-CIMSS AMSU-based
TC intensity algorithm. Preprints, 26th
Conference on Hurricanes and Tropical
Meteorology, Miami, FL, Amer. Meteor. Soc.,
118-119 Olander T. and C. Velden 2007 The
Advanced Dvorak Technique Continued Development
of an Objective Scheme to Estimate Tropical
Cyclone Intensity Using Geostationary Infrared
Satellite Imagery. Wea. and Forecasting Volume
22, Issue 2 (April 2007) pp. 287298 Velden C.
et al., 2006 The Dvorak Tropical Cyclone
Intensity Estimation Technique A Satellite-Based
Method that Has Endured for over 30 Years.
Bulletin of the American Meteorological Society
Volume 87, Issue 9 (September 2006) pp. 11951210
Wimmers, A., and C. Velden, 2010 Objectively
determining the rotational center of tropical
cyclones in passive microwave satellite imagery.
Submitted to JAMC.
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Analysis of Sat-Based TC Intensity Estimation in
the WNP During TCS-08
Comparison of All Satellite-based Estimates
Vmax (Kts)
N14 Blind Dvorak Consensus Oper Dvorak Consensus (w/Koba) ADT w/MW CIMSSAMSU SATCON
Bias 3.6 2.0 -3.6 2.9 -0.1
Abs Error 9.3 12.0 13.6 8.6 9.0
RMSE 11.9 14.9 17.4 10.1 10.6
Positive Bias indicates method estimates are too
strong
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Analysis of Sat-Based TC Intensity Estimation in
the WNP During TCS-08
Comparison of All Satellite-based Estimates
MSLP (mb)
N14 Blind Dvorak Consensus Oper Dvorak Consensus (w/Koba) ADT w/MW CIMSSAMSU SATCON
Bias 0.7 0.1 -1.0 -1.9 -1.3
Abs Error 5.2 7.5 10.7 4.9 6.0
RMSE 6.6 8.9 12.8 6.3 7.2
Positive Bias indicates method estimates are too
strong. 2mem SATCON RMSE 4.7 Blind and Oper
Dvorak conversion is Knaff/Zehr