CIMSS/NESDIS-USAF/NRL Experimental AMSU TC Intensity Estimation: Storm position corresponds to AMSU-A FOV 8 [1<--->30] Raw Ch8 (~150 hPa) Tb Anomaly: 5.36 C Raw Ch7 (~250 hPa) Tb Anomaly: 5.34 C AMSU-A MSLP (Ch8): 909.9 hPa RMW value: 24.0 Km Storm is sub-sampled based on RMW and FOV. Bias correction applied is: -15.1 hPa SUPER TYPHOON 19W Thursday 26aug04 Time: 0447 UTC Latitude: 23.79 Longitude: 135.960 Satellite: NOAA-16

TOWARD AN OBJECTIVE SATELLITE-BASED ALGORITHM TO PROVIDE REAL-TIME ESTIMATES OF TC INTENSITY USING INTEGRATED MULTISPECTRAL (IR AND MW) OBSERVATIONS

Christopher Velden, James Kossin, Tim Olander, Derrick Herndon, Tony Wimmers, Howard Berger University of Wisconsin – Cooperative Institute for Meteorological Satellite Studies

Robert Wacker,

United States Air forceJeff Hawkins NRL

at Monterey, CA

Uses pattern recognition techniques to extract TC characteristics in SSM/I

imagery (85 GHz). Bankert and Tag, 2002 (JAM)

“Computer Vision” Approach

Automated intensity estimates from passive microwave imagery

Example: SSMI 85 GHz and Rain Rate features

Using passive microwave. Example: TRMM Microwave Imager (TMI), 85 GHz overpass of Hurricane Isidore between the Yucatan peninsula and Cuba. “PCT” is a weighted difference between vertical and horizontal polarizations that indicates scattering by ice crystals and is a proxy for precipitation.Best track center, white cross; spiral-fitting score field, white contours; optimum spiral center, white square.

Using IR data. Example: GOES IR image of Hurricane Juan; initial guess of TC center based on a forecast, black triangle; spiral-fitting score field, white contours; area used in calculating the score field, gray circle; optimum eye ring, black circle.

Integrated Satellite-Based

TC IntensityEstimation System

AMSU

Microwave ImageryAODT

89 GHz defines eye based on ice scattering in the eyewall

1/nwi (est)iEnsemble Intensity Estimate =

The weights (wi) represent the confidences of the various (n) algorithm estimates (esti). The confidence is based on performance characteristics

of the algorithm as well as any additional factors such as data latency associated with polar orbiting satellite data.

AODT AMSU Consensus

7.0

5.3

0.56

6.16.910.5RMSE

4.85.58.6ABS Error

0.17-0.22-1.0Bias

N=214 Hybrid

AODT Statistics for Version 6.3AODT Statistics for Version 6.3Homogeneous (independent) data sample of 522 cases from 2003Homogeneous (independent) data sample of 522 cases from 2003

9.3311.812.67Op Center

8.08 9.932.40AODT (auto)

Abs. Err.RMSEBiasUnits in (hPa)

Stratified by Post-Eye and Scene TypeStratified by Post-Eye and Scene Type

680.610.80-0.07Curved Band

720.470.55 0.01Irregular CDO

1400.380.52 0.10Embedded Center

2620.580.81-0.12Shear

10630.400.50-0.08All Eye Scenes

10970.460.63-0.04All No Eye Scenes

0.41

0.43

AbsErr

555

2160

Sample

0.57-0.06All Scenes

RMSEBiasUnits in T-Number

0.52-0.04CDO

AODT Statistics for Version 6.3AODT Statistics for Version 6.3

For a complete description of the latest version of theFor a complete description of the latest version of theAdvanced Objective Dvorak Technique (AODT), see theAdvanced Objective Dvorak Technique (AODT), see theabst by Olander, Velden and Kossin, 26abst by Olander, Velden and Kossin, 26thth AMS Hurr Conf AMS Hurr Conf

CIMSS TC Intensity Methods for Hurricane Gabrielle

970

975

980

985

990

995

1000

1005

1010

1015

18:57 18:46 7:17 13:32 13:08 18:26 6:54 12:45 18:16 12:21

12-Sep 13-Sep 14-Sep 14-Sep 15-Sep 15-Sep 16-Sep 16-Sep 16-Sep 17-Sep

recon amsu aodt Hybrid

CIMSSAMSU

Super Typhoon 19W

Introduction and Motivation

Current Satellite-Based TC Intensity Estimation Methods Developed at CIMSS

Several existing or promising satellite-based methods to estimate tropical cyclone (TC) intensity are available to forecasters today. Some of these, such as the Dvorak Technique, have been utilized operationally for over 30 years. Others, such as those based on microwave data, are just emerging as new, more capable, meteorological satellite instruments become operational. Each of the methods by themselves represents or promises significant contributions to TC intensity analysis. However, each technique (or instrument that it is based on) also has its limitations. An effort is underway at CIMSS to build an integrated algorithm that is fully automated and objective, and utilizes a multispectral approach. This system would build on, and take advantage of, the latest science advances in existing (and emerging) methods.

Corresponding author: Chrisv@ssec.wisc.edu

AODT AMSU

For a complete description of the latest version of the CIMSSAdvanced Microwave Sounding Unit (AMSU) algorithm,

see the abstract by Herndon and Velden, 26th AMS Hurr Conf

Overall Performance

Multi-SensorInformation Sharing

Improving Center-Fix Methods

Satellite Estimates of RMW

TC Intensity Estimation: Integrated Approach

Basic Consensus (2 CIMSS Methods)

Preliminary Results

Weighted Ensemble (Multiple Methods)

Situational Performance

Other Methods as Potential Candidates for the Ensemble

SSMI/TMI/AMSRE

Empirical ApproachCorrelates patterns in SSMI imagery

with Dvorak-like patterns.Edson and Lander, 2002 (Proc. Of

25th AMS Hurricane Conf.)

High Confidence

Storm core well defined Nadir FOV FOV matches storm center

Multiple storm ‘cores’

Poor Confidence

Near limb FOV FOV offset from storm center

AMSU Confidence Scenarios

Accurate sub/over-samplingcorrections

Wrong choice of RMWcan lead to large estimate error

FOV captures all of warming

FOV captures fraction of warming

Colors represent confidence (green high, red low). The colored barsindicate ‘probabilities’ based on climate/persistence. The final estimate

is a weighted blend with error bars (black).

All units in hPa. The ‘hybrid’ uses an additional predictor,which is the estimate spread of the 2 members in the consensus

Two methods based on the study summarized in Wimmers and Velden,

26th AMS Hurr Conf

Empirical method employed at JTWCUsing SSMI and TRMM/TMI

CIMSS AMSU algorithm performance for storms from 2001-2004 using latest algorithm logic

6.35.0Mean Error

8.97.8RMSE

1.41.0Bias

DvorakCIMSS AMSUMSLP (hPa)

333333N

Summary

As part of an R&D effort at CIMSS to develop improved TC intensity estimation from satellites, existing methods to estimate intensity from different satellite platforms/sensors are being employed to create a more robust and reliable integrated approach. Taking advantage of the single method characteristics and situational tendencies, the final TC intensity estimate at a given analysis time will be obtained by employing a weighted consensus, decision tree, or “expert system” technique to blend/resolve the independent estimates. The algorithm will output both TC intensity parameters and confidence indicators.

This work is being sponsored by the Office of Naval Research, Program Element (PE-0602435N), the Oceanographer of the Navy through the program office at the PEO C4I&Space/PMW-180 (PE-

0603207N), and the Naval Research Laboratory-Monterey. T-Number relates to TC Vmax via the Dvorak relationship.

T-Number increments give a more realistic representation of actual intensity change due in part to the nonlinear relationship between MSLP and Vmax

Channel 8 Tb Anomaly

Average IR-calculated Eye Size (km)

Air

craft

Measu

red

RM

W (

km)

MSLP (hPa)

Best Guess

IR Estimate ATCF

Bias 1.6 -0.5 5.1

Absolute Error

5.4 6.8 8.3

RMSE 7.5 8.7 10.6

N 50 50 50

AMSU Intensity estimates using IR RMW method perform better than using ATCF RMW on independent

cases verified against Atlantic recon.

RMSE = 6.16 kmR2 = 0.60

Relationship between eye size, as measured by IR, and aircraft-measured RMW, for clear-eye Atlantic TC cases (AODT now provides these RMW estimates for clear-eye scenes).

Existing Method – Microwave-Based - Subjective

New Method – IR-Based - Objective