TC Intensity Estimation: SATellite CONsensus (SATCON). University of Wisconsin - Madison Cooperative Institute for Meteorological Satellite Studies. Derrick Herndon and Chris Velden. Interdepartmental Hurricane Conference Savannah, GA 01-04 March 2010. Research supported by - PowerPoint PPT Presentation
TC Intensity Estimation: TC Intensity Estimation: SATellite CONsensus (SATCON) SATellite CONsensus (SATCON) Derrick Herndon and Chris Velden Interdepartmental Hurricane Conference Interdepartmental Hurricane Conference Savannah, GA Savannah, GA 01-04 March 2010 01-04 March 2010 Research supported by the ONR Marine Meteorology and Atmospheric Effects Program University of Wisconsin - Madison Cooperative Institute for Meteorological Satellite Studies
Interdepartmental Hurricane ConferenceInterdepartmental Hurricane ConferenceSavannah, GASavannah, GA
01-04 March 201001-04 March 2010
Research supported by the ONR Marine Meteorology and Atmospheric Effects Program
University of Wisconsin - Madison
Cooperative Institute for Meteorological Satellite Studies
Motivation• Importance of getting current TC intensity right
- Intensification trends > 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
Uses IR imagery to objectively assess storm cloud patterns and structure to infer intensity
Latest version uses information from MW to make adjustments
Clear Eye Pinhole Eye Large Eye
ShearCurved Band Uniform
SATCON Members: CIMSS AMSU
0
20
40
60
80
100
120
-1 0 1 2 3 4 5 6 7 8
Channel 6
Channel 7
Channel 8
350 mb
250 mb
150 mb
AMSU Tb Anomaly vertical cross section for Katrina 2005
70 Knots
125 knots
55 Knots
AMSU Channel 8 Tb Anomaly Magnitude
TC
Pre
ssur
e A
nom
aly
Mag
nitu
de
SATCON Members: CIRA AMSU
IR image from NRL TC Page
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.
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
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
Adjust AMSU pressure if
needed
SATCON cross-method information sharing
ADT Estimate of Eye Size
Compare to AMSU-A FOV resolution
Example: ADT to AMSU
In eye scenes, IR can be used to estimate eye size
CIMSS AMSU uses eye size information to correctresolution sub-sampling
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
Information Sharing
ARCHER eye = 33 km Information can be input to AMSUmethod
SATCON Weighting Scheme
Example: ADT Scene type vs. performance
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)
RMSE 14 knots RMSE 12 knots RMSE 18 knots
CDO EYE SHEAR
Examples
ADT determines scene is an EYE
CIMSS AMSU: Good, near nadir pass. Eye is well resolved by AMSU resolution
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.
1999-2009 SATCON compared to a simple straight consensus (Atlantic)
N = 460N = 460SATCONSATCON
MSLPMSLP
SIMPLESIMPLE
MSLPMSLPSATCON SATCON
VmaxVmaxSIMPLESIMPLE
VmaxVmax
BIASBIAS 0.30.3 -2.5-2.5 -1.0-1.0 - 4.0- 4.0
AVG AVG ERRORERROR 5.25.2 5.75.7 7.27.2 8.18.1
RMSERMSE 6.46.4 7.77.7 8.38.3 9.39.3
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
1999-2009 SATCON compared to operational Dvorak (Atlantic)
N = 460SATCON
MSLP
Dvorak
MSLPSATCON
VmaxDvorak
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
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.
Summary
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. 687–697
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. 282–296
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. 287–298
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. 1195–1210
Wimmers, A., and C. Velden, 2010: Objectively determining the rotational center of tropical cyclones in passive microwave satellite imagery. Submitted to JAMC.
