UW-CIMSSUW-CIMSS Tropical Cyclone Research :Tropical Cyclone Research :

Current Progress and DevelopmentsCurrent Progress and Developments

Tim Olander, Chris Velden, Tim Olander, Chris Velden,

Jim Kossin, and Derrick HerndonJim Kossin, and Derrick Herndon

2004 Meteorological Satellite Coordinators Conference2004 Meteorological Satellite Coordinators ConferencePearl Harbor, HIPearl Harbor, HI

Research and development funding provided by :Research and development funding provided by :

Naval Research Laboratory - Monterey, CANaval Research Laboratory - Monterey, CAFunding contract N00173-01-C-2024 throughFunding contract N00173-01-C-2024 through

Space and Naval Warfare Systems Command (PMW-155) Space and Naval Warfare Systems Command (PMW-155)

under PE 0603207Nunder PE 0603207N

• Advanced Objective Dvorak Technique (AODT)Advanced Objective Dvorak Technique (AODT)

• Tropical cyclone Intensity Estimate (TIE) ModelTropical cyclone Intensity Estimate (TIE) Model

• AMSUAMSU

• Environmental Shear EstimatesEnvironmental Shear Estimates

UW-CIMSS Tropical Cyclone ResearchUW-CIMSS Tropical Cyclone Research

BriefingBriefing Overview Overview

AODT – Latest ImprovementsAODT – Latest Improvements

• Added new scene type classificationsAdded new scene type classifications– Separate classifications for eye and surrounding cloud regionsSeparate classifications for eye and surrounding cloud regions

• Eye: Clear, Pinhole, Large, Ragged, Obscured, NoneEye: Clear, Pinhole, Large, Ragged, Obscured, None• Cloud: Uniform, Embedded Center, Irregular CDO, Curved Band, ShearCloud: Uniform, Embedded Center, Irregular CDO, Curved Band, Shear

– Scenes more closely mimic Dvorak Technique classificationsScenes more closely mimic Dvorak Technique classifications– Convective symmetry parameter used to classify cloud regionConvective symmetry parameter used to classify cloud region

• Expanded for tropical storm and weaker systemsExpanded for tropical storm and weaker systems– Utilize Curved Band analysis as defined in Dvorak EIR Technique Utilize Curved Band analysis as defined in Dvorak EIR Technique

to estimate cloud curvature signaturesto estimate cloud curvature signatures– Irregular CDO scene covers “transition” scenesIrregular CDO scene covers “transition” scenes

• Removed Rapid Flag / modified time averaging schemeRemoved Rapid Flag / modified time averaging scheme– Final T# changed from 12-hour to 6-hour weighted averageFinal T# changed from 12-hour to 6-hour weighted average– New technique still allows for rapid intensificationNew technique still allows for rapid intensification

• Modified land interaction ruleModified land interaction rule– Analysis suspended while storm is over land (keyword override)Analysis suspended while storm is over land (keyword override)– Rule 9 application stopped after storm is over land for >12 hoursRule 9 application stopped after storm is over land for >12 hours

• Added Dvorak EIR Technique Rule 8Added Dvorak EIR Technique Rule 8– Constrains growth/decay rate of storm intensity over timeConstrains growth/decay rate of storm intensity over time

• Position of maximum Curved Band analysis determinedPosition of maximum Curved Band analysis determined– Location provided in addition to manual position valuesLocation provided in addition to manual position values

AODT – Latest ImprovementsAODT – Latest Improvements

• Latitude bias adjustment applied to CI# value Latitude bias adjustment applied to CI# value – A bias was found in Dvorak EIR Technique related to latitudeA bias was found in Dvorak EIR Technique related to latitude – The bias is caused by the latitudinal variation of eyewall The bias is caused by the latitudinal variation of eyewall

cloud-top temperature, resulting from the latitudinal cloud-top temperature, resulting from the latitudinal

variation of tropopause temperaturevariation of tropopause temperature– Bias adjustment increases/decreases intensity for storm Bias adjustment increases/decreases intensity for storm

positions north/south of approximately 22ºN latitudepositions north/south of approximately 22ºN latitude – Adjustment based on linear regression and applied to CI# valueAdjustment based on linear regression and applied to CI# value– Adjusted and unadjusted CI# values stored in AODT history file Adjusted and unadjusted CI# values stored in AODT history file – Reduces MSLP estimate error by about 10% in test cases Reduces MSLP estimate error by about 10% in test cases

– This bias should also be applied to conventional Dvorak methodThis bias should also be applied to conventional Dvorak method

AODT – Latest ImprovementsAODT – Latest Improvements(continued)(continued)

The The overalloverall bias bias is always is always between ±1.5mb between ±1.5mb and appears and appears benign, but the benign, but the latitude-latitude-dependent bias is dependent bias is as large as –as large as –10mb at 10°N 10mb at 10°N and +14mb at and +14mb at 40°N.40°N.

The bias The bias explains 15% to explains 15% to 20% of the 20% of the variance of variance of error.error.

Latitude-Latitude-dependent bias dependent bias in Dvorak in Dvorak Enhanced IR Enhanced IR (EIR)technique (EIR)technique applied to applied to Atlantic TC’s.Atlantic TC’s.

Application Application to other to other oceanic oceanic basins:basins:

Mean tropopause temperature Mean tropopause temperature (NCEP reanalysis). Averaged (NCEP reanalysis). Averaged over AUG SEP OCT (FEB MAR over AUG SEP OCT (FEB MAR APR) in Northern (Southern) APR) in Northern (Southern) hemisphere.hemisphere.

WMO-RMSC regions

AODT-v6.3 – ResultsAODT-v6.3 – ResultsIncluding Latitude Bias AdjustmentIncluding Latitude Bias Adjustment

All Tropical Cyclone Intensities (MSLP)All Tropical Cyclone Intensities (MSLP)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT adjAODT adj 0.09 0.09 9.50 9.50 16301630

AODT unadjAODT unadj 0.42 0.42 10.7210.72 16301630Op CentersOp Centers 0.22 0.22 10.6510.65 16301630

All Tropical Cyclone Intensities (MSLP)All Tropical Cyclone Intensities (MSLP)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT adjAODT adj 0.09 0.09 9.50 9.50 16301630

AODT unadjAODT unadj 0.42 0.42 10.7210.72 16301630Op CentersOp Centers 0.22 0.22 10.6510.65 16301630

Homogeneous Comparisons Homogeneous Comparisons with Aircraft Reconnaissancewith Aircraft Reconnaissance(26 storms between 1995 and 2002)(26 storms between 1995 and 2002)

Note: Positive error indicates underestimate (e.g. AODT minus Recon)Note: Positive error indicates underestimate (e.g. AODT minus Recon)

• Improved automated center location determination schemeImproved automated center location determination scheme– Operational Center forecast used as first guessOperational Center forecast used as first guess

• JTWC or NHC forecasts used in conjunction with JTWC or NHC forecasts used in conjunction with

6 and 12 hour old positions from AODT history file6 and 12 hour old positions from AODT history file• Current position interpolated using polynomial interpolation schemeCurrent position interpolated using polynomial interpolation scheme

– Laplacian Analysis of cloud top region performed Laplacian Analysis of cloud top region performed • Analysis region within 75 km radius of interpolated forecast positionAnalysis region within 75 km radius of interpolated forecast position• Identifies pixels with strong gradients in cloud top temperature field Identifies pixels with strong gradients in cloud top temperature field

– Confidence factors derived for each methodology Confidence factors derived for each methodology • Forecast : Time difference from “Initial/Warning” positionForecast : Time difference from “Initial/Warning” position• Laplacian : Density and scatter of large temperature gradientsLaplacian : Density and scatter of large temperature gradients

– Laplacian typically only used where strong but concentrated Laplacian typically only used where strong but concentrated

temperature gradient fields are identified (e.g. Eye Scenes)temperature gradient fields are identified (e.g. Eye Scenes)

AODT – Latest ImprovementsAODT – Latest Improvements(continued)(continued)

AODT Auto-Positioning ComparisonAODT Auto-Positioning ComparisonHurricane FloydHurricane Floyd

AODT Auto-PositionAODT Auto-PositionClear Eye Clear Eye 6.2 T# 6.2 T#

NHC Interpolated Forecast PositionNHC Interpolated Forecast PositionUniform Cloud Region Uniform Cloud Region 4.5 T# 4.5 T#

Dependent Data SampleDependent Data Sample (26 storms between 1995-2002) (26 storms between 1995-2002)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT ManualAODT Manual 0.090.09 9.50 9.50 16301630

AODT AutomatedAODT Automated 1.701.70 10.0410.04 16301630Op CentersOp Centers 0.220.22 10.6510.65 16301630

Dependent Data SampleDependent Data Sample (26 storms between 1995-2002) (26 storms between 1995-2002)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT ManualAODT Manual 0.090.09 9.50 9.50 16301630

AODT AutomatedAODT Automated 1.701.70 10.0410.04 16301630Op CentersOp Centers 0.220.22 10.6510.65 16301630

AODT-v6.3 – ResultsAODT-v6.3 – ResultsAutomated AODTAutomated AODT

Homogeneous Comparisons Homogeneous Comparisons with Aircraft Reconnaissancewith Aircraft Reconnaissance

Independent Data SampleIndependent Data Sample (5 storms in 2003) (5 storms in 2003)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT AutomatedAODT Automated 2.402.40 9.93 9.93 522 522Op CentersOp Centers 2.672.67 11.8111.81 522 522

Independent Data SampleIndependent Data Sample (5 storms in 2003) (5 storms in 2003)

(errors in millibars)(errors in millibars) Bias RMSE Sample Bias RMSE SampleAODT AutomatedAODT Automated 2.402.40 9.93 9.93 522 522Op CentersOp Centers 2.672.67 11.8111.81 522 522

Note: Positive error indicates underestimate (e.g. AODT minus Recon)Note: Positive error indicates underestimate (e.g. AODT minus Recon)

Statistical Breakdown by AODT scene typeStatistical Breakdown by AODT scene type(errors in T# units)(errors in T# units) Bias RMSE AAE Sample Bias RMSE AAE SampleAll PointsAll Points 0.11 0.11 0.67 0.67 0.51 3735 0.51 3735

All Eye Scenes All Eye Scenes -0.08 -0.08 0.50 0.50 0.40 1063 0.40 1063 Clear EyeClear Eye -0.02 -0.02 0.42 0.42 0.32 445 0.32 445 All other EyeAll other Eye -0.12 -0.12 0.55 0.55 0.46 618 0.46 618

All Non-Eye Scenes All Non-Eye Scenes 0.190.19 0.73 0.73 0.56 2672 0.56 2672 Uniform CDOUniform CDO 0.17 0.17 0.64 0.64 0.51 1093 0.51 1093 Embedded Center 0.19Embedded Center 0.19 0.66 0.66 0.46 193 0.46 193 Irregular CDOIrregular CDO 0.11 0.11 0.70 0.70 0.55 278 0.55 278 Curved BandCurved Band 0.17 0.17 0.73 0.73 0.60 324 0.60 324 ShearShear 0.25 0.25 0.85 0.85 0.64 784 0.64 784

Statistical Breakdown by AODT scene typeStatistical Breakdown by AODT scene type(errors in T# units)(errors in T# units) Bias RMSE AAE Sample Bias RMSE AAE SampleAll PointsAll Points 0.11 0.11 0.67 0.67 0.51 3735 0.51 3735

All Eye Scenes All Eye Scenes -0.08 -0.08 0.50 0.50 0.40 1063 0.40 1063 Clear EyeClear Eye -0.02 -0.02 0.42 0.42 0.32 445 0.32 445 All other EyeAll other Eye -0.12 -0.12 0.55 0.55 0.46 618 0.46 618

All Non-Eye Scenes All Non-Eye Scenes 0.190.19 0.73 0.73 0.56 2672 0.56 2672 Uniform CDOUniform CDO 0.17 0.17 0.64 0.64 0.51 1093 0.51 1093 Embedded Center 0.19Embedded Center 0.19 0.66 0.66 0.46 193 0.46 193 Irregular CDOIrregular CDO 0.11 0.11 0.70 0.70 0.55 278 0.55 278 Curved BandCurved Band 0.17 0.17 0.73 0.73 0.60 324 0.60 324 ShearShear 0.25 0.25 0.85 0.85 0.64 784 0.64 784

AODT-v6.3 – ResultsAODT-v6.3 – ResultsHomogeneous Comparisons with Aircraft ReconnaissanceHomogeneous Comparisons with Aircraft Reconnaissance

Note: Positive error indicates overestimate (e.g. AODT minus Recon)Note: Positive error indicates overestimate (e.g. AODT minus Recon)

AODT – Future DirectionsAODT – Future Directions

• Integrate regression-based TIE Model with AODT

– Use 2003 TC results to guide development of combined methodUse 2003 TC results to guide development of combined method

• Examine additional geostationary channelsExamine additional geostationary channels

– IRW-WV channel difference (Velden and Olander, 1998)IRW-WV channel difference (Velden and Olander, 1998)

– Additional Dvorak methods using visible and/or shortwave infraredAdditional Dvorak methods using visible and/or shortwave infrared

• Investigate additional satellite informationInvestigate additional satellite information

– AMSU analysis using technique developed at UW-CIMSSAMSU analysis using technique developed at UW-CIMSS

– SSM/I and TRMM analysis based on work by NRL-MRY/R. EdsonSSM/I and TRMM analysis based on work by NRL-MRY/R. Edson

GOALGOAL• Develop an advanced multi-sensor objective techniqueDevelop an advanced multi-sensor objective technique

– Fuse results/output from different instruments and Fuse results/output from different instruments and analysis analysis techniques into an expert systemtechniques into an expert system

Integrated Satellite-Based

TC IntensityEstimation System

IRW-WV channel difference

AMSU

Microwave imagery

Current AODT

AODT – AvailabilityAODT – Availability

• The latest version of the AODT is currently available to any/all The latest version of the AODT is currently available to any/all interested users for McIDAS platforms from the AODT webpage interested users for McIDAS platforms from the AODT webpage (along with the updated Users’ Guide)(along with the updated Users’ Guide)

• The latest version of the AODT is being integrated into the The latest version of the AODT is being integrated into the TeraScan system for use at JTWC during the 2004 TC season. TeraScan system for use at JTWC during the 2004 TC season. The code has also been adapted for N-AWIPS at NOAA sites and The code has also been adapted for N-AWIPS at NOAA sites and Mark-IVB at AFWAMark-IVB at AFWA

• An X-Window/Motif version of the AODT has also been An X-Window/Motif version of the AODT has also been developed and will is available for UNIX and Linux based developed and will is available for UNIX and Linux based systems (also available at the AODT webpage)systems (also available at the AODT webpage)

AODT webpagehttp://cimss.ssec.wisc.edu/tropic/aodthttp://cimss.ssec.wisc.edu/tropic/aodt

The Tropical cyclone Intensity The Tropical cyclone Intensity Estimation (TIE) model:Estimation (TIE) model:

•Developed to formally explore the relationships Developed to formally explore the relationships between TC intensity and features in between TC intensity and features in geostationary satellite infrared (IR) imagery, geostationary satellite infrared (IR) imagery, without the constraints of the Dvorak-based without the constraints of the Dvorak-based methods and their attendant rules. methods and their attendant rules.

•Applicable at all stages of TC lifetime.Applicable at all stages of TC lifetime.

•Completely objective; no scene-typing.Completely objective; no scene-typing.

63% of the MSLP variance is explained by the regression.63% of the MSLP variance is explained by the regression.

TIE-model:TIE-model:

Predictor (sig > 99.9%) Normalized CoefficientPredictor (sig > 99.9%) Normalized Coefficient

Warmest eye temperature Warmest eye temperature –– 0.64 0.64

Mean eyewall temperature + 0.43Mean eyewall temperature + 0.43

Symmetric organization + 0.34Symmetric organization + 0.34

SST-based parameter + 0.26SST-based parameter + 0.26

Latitude Latitude –– 0.16 0.16

}IR-derivedIR-derived

multivariate linear regressionmultivariate linear regression

TC central pressure (MSLP) measured TC central pressure (MSLP) measured from aircraft reconnaissance is from aircraft reconnaissance is regressed onto 5 predictorsregressed onto 5 predictors

TIE-model performance:TIE-model performance:

Errors relative to recon. TIE-model errors based Errors relative to recon. TIE-model errors based on independent jackknife analysis.on independent jackknife analysis.

The The good:good:

The bad:The bad:

TIE-model TIE-model performancperformance:e:

TIE-model R&D:TIE-model R&D:

•Serves as a natural platform for testing Serves as a natural platform for testing additional parameters for correlation with additional parameters for correlation with TC intensity.TC intensity.

•Indices derived from synoptic fields Indices derived from synoptic fields (analogous to SHIPS) are being presently (analogous to SHIPS) are being presently tested.tested.

•Further development will include indices Further development will include indices derived from microwave imagery; these derived from microwave imagery; these can include indices related to eyewall can include indices related to eyewall replacement cycles.replacement cycles.

•TIE model estimates will be available in TIE model estimates will be available in conjunction with AODT estimates on conjunction with AODT estimates on TeraScan system this coming season.TeraScan system this coming season.

AMSU BackgroundAMSU Background• What does AMSU What does AMSU reallyreally observe? observe?

• AMSU-A passive microwave radiometerAMSU-A passive microwave radiometer

• Flown on NOAA-15/16/17 and Aqua polar orbiting satellitesFlown on NOAA-15/16/17 and Aqua polar orbiting satellites• Up to 6 warm core observations daily (NOAA birds)Up to 6 warm core observations daily (NOAA birds)

• Senses terrestrial radiation near 55GHz regionSenses terrestrial radiation near 55GHz region

• ChallengesChallenges• Scattering prohibits hydrostatic integrationScattering prohibits hydrostatic integration• Variability in peak warming altitudeVariability in peak warming altitude• Variable horizontal resolutionVariable horizontal resolution

FOV 1FOV 1 FOV 30FOV 30

Suitability of the AMSUSuitability of the AMSU

929hPa

Hurricane Inez 1966Hurricane Inez 196688

77

66

55

Hurricane Floyd 1999Hurricane Floyd 1999

CIMSS AMSU-based TC Intensity Estimation AlgorithmCIMSS AMSU-based TC Intensity Estimation Algorithm

Extrapolate storm position for AMSU pass timeExtrapolate storm position for AMSU pass timebased on latest warningbased on latest warning

Find max AMSU Tb in ch7 and 8 within 100kmFind max AMSU Tb in ch7 and 8 within 100kmof storm position (this is the TC upper-level warm core)of storm position (this is the TC upper-level warm core)

Average environmental Tbs from 4 surrounding points and Average environmental Tbs from 4 surrounding points and subtract from core value to determine thermal anomalysubtract from core value to determine thermal anomaly

Estimate MSLP for ch7 and ch8 anomalies based onEstimate MSLP for ch7 and ch8 anomalies based onstatistical regression. Use lower of the two values.statistical regression. Use lower of the two values.Apply FOV bias correction based on RMW or...Apply FOV bias correction based on RMW or...

Call channel 7 retrieval if:Call channel 7 retrieval if:• Raw Ch7 Tb anomaly > Raw Ch8Raw Ch7 Tb anomaly > Raw Ch8• Raw Ch7 anomaly > 1.0KRaw Ch7 anomaly > 1.0K• Storm is located near the edge of the satellite swathStorm is located near the edge of the satellite swath

X

X

10

93010130

AMSU MSLP

?

2002 Results (MSLP)2002 Results (MSLP)

Statistics for Atlantic (N= 60) in hPa

CIMSS DvorakMean Error 3.29 4.52Std Dev 2.30 3.40Bias -0.08 -0.90RMSE 4.00 5.62

Avg MSLP of sample 1002.4

Statistics for Atlantic (N= 60) in hPa

CIMSS DvorakMean Error 3.29 4.52Std Dev 2.30 3.40Bias -0.08 -0.90RMSE 4.00 5.62

Avg MSLP of sample 1002.4

Statistics for Pacific (N = 10) in hPa

CIMSS JTWCMean Error 8.00 11.16Std Dev 7.25 8.79Bias 4.79 6.10RMSE 10.57 13.93

Avg MSLP of sample 964.4

Statistics for Pacific (N = 10) in hPa

CIMSS JTWCMean Error 8.00 11.16Std Dev 7.25 8.79Bias 4.79 6.10RMSE 10.57 13.93

Avg MSLP of sample 964.4

MSLP

2002 Atlantic Basin Results2002 Atlantic Basin Results

13%13%

3%

3%

34%34%(0.0)(-0.5)

(+0.5)

(-1.0)

(-1.5)

(+1.0)

19%

3% 28%

28%22% (0.0)

(-0.5)

(-1.0)

(+0.5)(+1.0)

AMSUAMSU81% within +/- 0.5T81% within +/- 0.5T

NOAA/NESDIS Satellite Analysis Branch (SAB) NOAA/NESDIS Satellite Analysis Branch (SAB) vs.vs.

AMSU AMSU (N=34, +/- 2hrs of aircraft reconnaissance)(N=34, +/- 2hrs of aircraft reconnaissance)

SAB SAB 78% within +/- 0.5T78% within +/- 0.5T

Sources of ErrorSources of Error

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

PositionPosition• First Guess comes from Warning MessageFirst Guess comes from Warning Message• Search algorithm locates max anomalySearch algorithm locates max anomaly• Sheared systemsSheared systems

AMSU vs. JTWC Wrng for STY Pongsona (Correcting for RMW / Posit)

900905910915920925930935940945950955960965970975980985990995

1000100510101015

12/02 11

12/02 21

12/03 00

12/03 03

12/03 08

12/03 16

12/03 20

12/03 23

12/04 15

12/05 09

12/05 12

12/05 21

12/06 21

12/07 04

12/07 13

12/07 17

12/08 04

12/08 09

12/08 12

12/08 16

12/08 22

12/09 01

12/09 16

12/09 21

12/10 08

12/10 12

12/10 16

12/10 21

12/11 00

Date/Time (UTC)

MS

LP (m

b)

JTWC AMSU Corrected for RMW / Posit AMSU Original

AAFB 937mb

X

Future WorkFuture Work

Improve TechniqueImprove Technique

• Bias correction when storm is near satellite limbBias correction when storm is near satellite limb

• Separate treatment of pinhole eye storms using new coefficientsSeparate treatment of pinhole eye storms using new coefficients

• Time averaging / smoothingTime averaging / smoothing

• Add channel 8 retrievalAdd channel 8 retrieval

• Remove precipitation contamination (Bob Wacker)Remove precipitation contamination (Bob Wacker)

• Addition of AMSU on Aqua (2 more passes per day)Addition of AMSU on Aqua (2 more passes per day)

• Basin specific coefficients (need sufficient “observations”)Basin specific coefficients (need sufficient “observations”)

Confidence Indicator ?Confidence Indicator ?

• Give forecasters a measure of estimate confidenceGive forecasters a measure of estimate confidence

• Based on FOV, eye size (RMW), anomaly heightBased on FOV, eye size (RMW), anomaly height

Summary and ConclusionsSummary and Conclusions

AMSU provides unique tropical cyclone perspectiveAMSU provides unique tropical cyclone perspective

• Can see beneath upper level cloud coverCan see beneath upper level cloud cover

• 55 Ghz region radiances can quantify inner core thermal55 Ghz region radiances can quantify inner core thermal structure / changes.structure / changes.

• Temperature anomaly strength directly associated with tropicalTemperature anomaly strength directly associated with tropical cyclone intensitycyclone intensity

• A unique addition to the forecaster “tool kit”A unique addition to the forecaster “tool kit”

AMSU-generated MSLP estimate accuracy improved in 2002AMSU-generated MSLP estimate accuracy improved in 2002

• Addition of second AMSU channel and advanced logicAddition of second AMSU channel and advanced logic

• Skill comparable to, and in some cases better than, the Dvorak estimatesSkill comparable to, and in some cases better than, the Dvorak estimates

• Future changes will improve performanceFuture changes will improve performance

UW-CIMSS/NESDIS wind shearUW-CIMSS/NESDIS wind shearproducts in Hurricane Lili (2002)products in Hurricane Lili (2002)

Deep-layer

Tendency

Mid-level

TROPICAL STORM KYLE 18:00UTC 07October2002TROPICAL STORM KYLE 18:00UTC 07October2002UW-CIMSS Experimental Vertical Shear TC Intensity Trend EstimatesUW-CIMSS Experimental Vertical Shear TC Intensity Trend Estimates Current Conditions :Current Conditions : Latitude : 32:19:27 NLatitude : 32:19:27 N Longitude : 70:50:45 WLongitude : 70:50:45 W Intensity (MSLP) : 1005.0 hPaIntensity (MSLP) : 1005.0 hPa Max Pot Int (MPI) : 971.7 hPaMax Pot Int (MPI) : 971.7 hPa MPI differential (MSLP-MPI) : 33.3 hPaMPI differential (MSLP-MPI) : 33.3 hPa CIMSS Vertical Shear Magnitude : 2.8 m/sCIMSS Vertical Shear Magnitude : 2.8 m/s Direction : 215.0 degDirection : 215.0 deg

Outlook for TC Intensification Based on Current Env. Shear ValuesOutlook for TC Intensification Based on Current Env. Shear Values Forecast Interval : 6hr 12hr 18hr 24hrForecast Interval : 6hr 12hr 18hr 24hr F F N NF F N N

Legend: VF - Very Favorable F - Favorable N - NeutralLegend: VF - Very Favorable F - Favorable N - Neutral U – Unfavorable VU - Very UnfavorableU – Unfavorable VU - Very Unfavorable -- Mean Intensity Trend (negative indicates TC deepening) ---- Mean Intensity Trend (negative indicates TC deepening) -- 6hr 12hr 18hr 24hr6hr 12hr 18hr 24hr VF < -3.0mb/ 6hr < -6.0mb/12hr < -9.0mb/18hr < -12.0mb/24hrVF < -3.0mb/ 6hr < -6.0mb/12hr < -9.0mb/18hr < -12.0mb/24hr F -3.0 - -1.5 -6.0 - -3.0 -9.0 - -4.5 -12.0 - -6.0F -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 - +6.0N -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 - +12.0U +1.5 - +3.0 +3.0 - +6.0 +4.5 - +9.0 +6.0 - +12.0 VU >+3.0 >+6.0 >+9.0 >+12VU >+3.0 >+6.0 >+9.0 >+12

CIMSS experimental vertical shear product (Gallina and Velden 2002)CIMSS experimental vertical shear product (Gallina and Velden 2002)

TC Intensity Outlooks Based on CIMSS Vertical Shear Analyses vs. Best-Track MSLP for Hurricane Kyle

(12L)

970

975

980

985

990

995

1000

1005

1010

1015

1020

2002

Pre

ssu

re (

mb

)

Best-Track MSLP6Hr Forecast Pressure12 Hr Forecast Pressure18 Hr Forecast Pressure24 Hr Forecast Pressure

9/22 9/24 10/2 10/6 10/8 10/12

Average Forecast Errors

6 Hr ---> 1.2 mb12 Hr ---> 2.8 mb18 Hr ---> 3.8 mb24 Hr ---> 4.8 mb

9/26 9/28 9/30 10/4 10/10

SUMMARYSUMMARY

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.

RESULTSRESULTS

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.