October 17, 2002National Hurricane Center Current and Future Tropical Cyclone Projects at...

October 17, 2002 National Hurricane Center

Current and Future Tropical Cyclone Projects at

CIRA/NESDIS: An Update and Outlook

Presented by

John Knaff

CIRA


Acknowledgments

• Nick Shea, Michelle Manelli, Jim Kossin, John Kaplan, Julie Demuth, Ray Zehr, Mark DeMaria, and many others….

• A special thanks to Kristina Katsaros for paying my way!


Outline

• JHT– AMSU– SHIPS

• Insurance Friends– Ensemble SHIPS– Wind Probablities

• Continuing and Future Projects


Tropical Cyclone Intensity and Size Estimation From AMSU Data

• CIRA algorithm– Physical retrieval of T, P and wind from all

AMSU-A channels– Statistical estimation of max wind, min

pressure, radii of 34, 50 and 64 kt winds using input from retrievals


AMSU Instrument Properties

• AMSU-A1– 13 frequencies 50-89 GHz– 48 km maximum resolution– Vertical temperature profiles

0-45 km• AMSU-A2

– 2 frequencies 23.8, 31.4 GHz– 48 km maximum resolution– Precipitable water, cloud

water, rain rate

NOAA – 15, Launched May 13, 1998


Horizontal Resolution: AMSU-A vs. MSU

Useful Region


Vertical Weighting: AMSU-A


Typical AMSU Data Coverage (NOAA-15)

Total Precipitable Water


Hurricane Temperature Profile


1. Apply a statistical retrieval to obtain vertical temperature (T) profiles and integrated cloud liquid water (CLW) from all available AMSU limb-corrected brightness temperatures at the satellite footprint locations. The storm must be within 600 km of the swath center for the algorithm to run.

2. Apply statistical correction to T in regions of high CLW to account for attenuation.

3. Interpolate T and CLW to an evenly spaced lat/lon grid using a two-pass Barnes analysis

4. Apply “Laplacian” filter to remove localized cold anomalies resulting from ice scattering

5. Using a smoothness upper boundary condition, integrate the hydrostatic equation downwards from 50 hPa to the surface to obtain the pressure field

6. Assume gradient balance to obtain the tangential wind from the pressure field as a function of radius and height

7. Statistically relate parameters from the retrieved CLW, temperature, pressure and wind fields to the observed MSLP


WithoutCorrection

WithCorrection

Ta

V

Ta

V


AMSU Surface Pressure for Floyd 14 Sept 1999

Uncorrected Corrected


Statistically relate parameters from the

retrieved CLW, temperature, pressure and wind fields to the

observed MSLP and Wind


Potential IntensityEstimators

Description

MINP Minimum surface pressure (hPa) at storm centerDP0 Pressure drop (hPa) at the surface from r = 600 to 0 kmDP3 Pressure drop (hPa) at z = 3 km from r = 600 to 0 km

TMAXMaximum temperature perturbation (°C) calculated as the temperatureat r = 600 km minus the temperature at each radius

ZMAX Height (km) of maximum temperature perturbation (TMAX)SS Resolution (km) of AMSU footprint at storm center

VMX0 Maximum wind speed (kts) at the surfaceRMX0 Radius (km) of maximum winds at the surfaceVMX3 Maximum wind speed (kts) at z = 3 kmRMX3 Radius (km) of maximum winds at z = 3 kmVBI0 Tangential winds at surface averaged from r = 0 to 250 kmVBI3 Tangential winds at z = 3 km averaged from r = 0 to 250 kmVBI5 Tangential winds at z = 5 km averaged from r = 0 to 250 kmVBO0 Tangential winds at surface averaged from r = 250 to 500 kmVBO3 Tangential winds at z = 3 km averaged from r = 250 to 500 kmVBO5 Tangential winds at z = 5 km averaged from r = 250 to 500 km

CLWAVE Cloud liquid water (CLW) content (mm) averaged from r = 0 to 100 kmCLWPER Percentage of area with CLW values > 0.5 mm from r = 0 to 300 km

LAT* Latitude from NHC at storm center, interpolated to AMSU swath time


Maximum Sustained Winds (kts)

R2 = 72.3% Minimum Pressure (hPa)

R2 = 76.5% Dependent Variable

Coefficient Normalized Coefficient

P-Value Coefficient Normalized Coefficient

P-Value

MINP n/a n/a n/a 0.623 0.259 0.000 DP0 -1.648 -0.459 0.000 1.521 0.602 0.000

TMAX 2.996 0.204 0.008 -2.777 -0.269 0.000 SS 0.191 0.070 0.005 -0.145 -0.076 0.001

RMX3 -0.020 -0.069 0.006 0.014 0.072 0.002 VBI5 4.109 0.883 0.000 -2.492 -0.760 0.000

CLWAVE 19.556 0.327 0.000 -12.499 -0.297 0.000 CLWPER -0.222 -0.168 0.000 0.116 0.124 0.001

Statistical Coefficients/ Wind, MSLP


Development Data Set (n=473)

Jackknife Data Set (n=473)

Jackknife Data Against Reconnaissance Data

(n=100)

VMX MINP VMX MINP VMX MINP

Coefficient of Determination (R2)

72.3% 76.5% 70.2% 73.9% 74.3% 83.1%

MAE 10.6 6.5 11.0 6.7 10.7 6.1

RMSE 13.5 8.8 14.1 9.3 14.9 9.2

Standard Deviation of Residuals

13.5 8.8 14.1 9.3 13.7 9.0

Performance Tests


2002 Verification through 11 Oct

• What was produced in real-time at CIRA

• Most got to NHC

• 243 Cases


Bias = -1.9 mbMAE = 7.6 mb (6.7)RMSE = 10.28 mb (9.3)


SkeletonStorms


Bias = 3.0 ktMAE = 10.9 kt (11.0)RMSE = 14.1 kt (14.1)


AMSUGood

Bad

DvorakGoodSkeleton

Storms


AMSU Wind Radii Estimates

• Predict azimuthally averaged winds with multiple linear regression

• Apply asymmetries based upon motion.

Estimation of Wind Radii

• 20 potential predictors are used to predict the mean radii of 35, 50, and 65 knot winds if they exist.

• Asymmetries are accounted for by a simple relationship

• Actual mean radii are estimated using

But are solved using a variational method since all radii rarely exist.

)cos())((),( ar

raVrV xm

m

2

0

/1])cos(

[2

daV

aVrr xmm

Cost Function and Variational Analysis

• Cost Function (thing to minimize iteratively)

2

2

2

2

264

26464

250

25050

234

23434

)()(

)()()(),(

rm

mcmrm

x

cx

m

rrxx

RrRrRrxrC

Last two terms are used to constrain the results close to climatology (penalty terms) – climatological r and x are functions of intensity.


Azimuthally Averaged 34 kt Wind Radii (nm)

R2 = 71.9%

Dependent Variable Coefficient Normalized Coefficient

P-Value

MINP -3.788 -0.718 0.000 VBI5 -3.135 -0.349 0.005 LAT 2.100 0.327 0.000

BTVMX* 1.113 0.501 0.000


R2 = 65.9%


P-Value

TMAX 9.461 0.526 0.006 VMX0 1.273 0.525 0.004 VMX3 -2.059 -0.772 0.001 VBO5 2.298 0.300 0.004

CLWPER 0.620 0.321 0.002


R2 = 80.8%


P-Value

TMAX 6.286 0.623 0.000 SS 0.427 0.168 0.005

CLWPER 0.270 0.248 0.010 BTVMX* 0.217 0.188 0.005

Coefficients/ Wind Radii


34 kt Azimuthally Averaged Radii

(n=129)


(n=92)


(n=68)

Dev Jack Dev Jack Dev Jack

Coefficient of Determination (R2)

71.9% 65.4% 65.9% 39.3% 80.8% 73.7%

MAE (% of ave) 21.2

(19.0%) 24.3

(21.7%) 17.9

(26.9%) 25.7

(38.7%) 8.0 (21.2%)

9.4 (25.0%)

RMSE 28.3 31.6 23.3 32.6 10.1 11.8

Bias N/A 1.0 N/A -9.9 N/A -0.1

Performance Tests for Symmetric Winds


0

30

60

900

1530

45

60

75

90

105

120

135

150165

180195

210

225

240

255

270

285

300

315

330345

34 NHC

34 AMSU

50 NHC

50 AMSU

Tropical Storm IrisVMX = 55 ktsStorm Direction = 289°Storm Speed = 15 kts

0

50

100

150

200

2500

1530

45

60

75

90

105

120

135

150165

180195

210

225

240

255

270

285

300

315

330345

34 NHC

34 AMSU

50 NHC

50 AMSU

64 NHC

64 AMSU

Hurricane GertVMX = 71 ktsStorm Dir = 23Storm Sp = 21 kts


34 kt Asymmetric Wind Radii

NE SE SW NW

R2 59.5% 66.9% 60.9% 45.7% MAE 28.4 28.9 30.5 32.8

RMSE 38.4 39.0 41.8 43.9 Bias -13.1 -1.8 11.5 -9.3


NE SE SW NW

R2 33.9% 67.3% 52.8% 26.6% MAE 29.2 21.8 19.3 25.4

RMSE 41.2 29.6 27.0 37.1 Bias -15.8 -2.8 4.4 -6.8


NE SE SW NW

R2 69.0% 59.5% 44.3% 55.5% MAE 15.3 12.0 14.7 14.4

RMSE 18.9 17.0 19.1 19.6 Bias -5.0 6.3 3.9 -4.6

Performance Tests for Asymmetric Winds


Verification

• Will be done following the season with g-deck information.


Future Plans for AMSU Analyses

• Finish program to access data from BUFR files– Dostalek/Krautkramer collaboration

• Perform validation of 2002 season• Investigate method to remove high bias for

“skeleton” storms• Generalize wind model to improve

asymmetric wind radii estimation– Current method only includes wavenumber one

asymmetry due to motion– Obtain higher-order asymmetries from AMSU

nonlinear balance winds


Balance Equation Variational Solution - Hurricane Floyd

AMSU 850 hPa height First guess wind:(u=0, v=0)

Nonlinear balance wind


SHIPS Improvements Using Satellite Data(NESDIS/CIRA JHT Project)

• Parallel version of SHIPS with predictors from GOES and satellite altimetry data – % Pixel counts < -20 C– BT standard deviation– Ocean Heat Content along storm track > 50 kJ/m2

• Provides correction to operational SHIPS forecast (No land and decay version of SHIPS)

• Implemented on NCEP/IBM Aug 20, 2002 beginning with Dolly


Sample Image and Radial/Time Profiles for Hurricane Floyd 1999

Mean Tb Tb std dev

Pixels < -30 C Pixels < -40 C

Pixels < -50 C Pixels < -60 C


SAMPLE OCEANIC HEAT CONTENTSAMPLE OCEANIC HEAT CONTENT(26 Sept 2002)(26 Sept 2002)


Preliminary 2002 Intensity Validation

• Arthur-Lili as of 11 Oct 2002

• NHC validation rules with working best track– 186 cases at 12 hr, 77 cases at 120 hr

• Conclusion: It was a difficult year for intensity forecasting

0

10

20

30

40

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Inte

nsi

ty E

rro

r (k

t)

5-Day SHIFOR

SHIPS

Decay-SHIPS

GFDL


Impact of GOES/OHC Predictors on Decay-SHIPS Forecasts

• Dolly-Lili as of 11 Oct 2002• NHC validation rules with working best track

– 169 cases at 12 hr, 78 cases at 120 hr• Positive Impact 0-60 hr, Negative impact 72-120 hr• Negative impact influenced by Isidore track forecast errors

-15

-10

-5

0

5

10

12 24 36 48 60 72 84 96 108 120


Per

cen

t Im

pro

vem

ent


Separation of GOES and OHC Effects

• GOES data provide modest corrections (~0-8 kt) for most storms

• OHC has little effect for most storms, but large increases (up to 20 kt) over limited regions

• Impacts of GOES and OHC will be evaluated separately after the season

• Preliminary analysis:– OHC effects: Isidore, Lili– GOES effects: Dolly, Edouard, Fay, Gustav, Hanna,

Josephine, Kyle


Impact of GOES Predictors on Decay-SHIPS Forecasts

• Sample for cases without OHC impacts– Dolly-Kyle without Isidore

• Positive Impact 0-108 hr, Negative impact 120 hr– GOES predictors are independent of track forecast– GOES data reduced intensity of sheared cases

• (2002 sample ideal for this effect)

-10

-5

0

5

10

15

12 24 36 48 60 72 84 96 108 120


Per

cen

t Im

pro

vem

ent


Future Improvements to SHIPS

• Short Term – Evaluate SST and OHC with smaller time step– Update SST analysis daily (already done by C. Sisko)– Test GOES predictors in east Pacific SHIPS

• Longer Term (Possible new JHT project)– Neural Network Prediction

• Time step approach – (0-12, 12-24, … instead of 0-12, 0-24, etc)

– Extend developmental sample back to 1981 using re-analysis data

– Investigate “False Start” tropical cyclones – Develop specialized prediction for hurricanes

• Account for inner core structure (eye wall cycles, etc)• Incorporate aircraft data, possibly microwave imagery


“False Start” Tropical Cyclones

• Significant intensification predicted by SHIPS, but storm decays in low-shear environment with warm SSTs– Danny 1991, Cindy, Bret 1993, TD05 1997, Alex

1998, Ernesto, Joyce 2000, Chantal, Erin 2001, Dolly 2002

• Large source of SHIPS (and NHC forecast errors• Develop objective measure for “False Starts”• Examine storm environment to aid identification

– Emphasis on thermodynamics– Possible connection with Saharan Air Layer


D-SHIPS forecasts for Chantal 2001 and Dolly 2002 (“False Start” Storms)


SHIPS Ensemble Forecasts(“Insurance Friends” Project)

• Provide a range of plausible forecast tracks using error characteristics of NHC official forecasts– Errors fitted to analytic distributions– Errors sampled at 0.5 and 1.0

• Run ensemble SHIPS and Decay-SHIPS • Calculate ensemble mean and ensemble spread• Preliminary results for Dolly through Lili

– Includes depression stage– Validated using working best track– 239 cases at 12 hr, 121 at 120 hr


Sample 16-member Track Ensemble(East Pacific case)


Control and Ensemble Mean SHIPS Errors, and Average Ensemble Spread (No land correction)

0

10

20

30

40

12 24 36 48 60 72 84 96 108 120


Inte

nsi

ty E

rro

r o

r E

nse

mb

le S

pre

ad (

kt)

Control

Ensemble Mean

Ensemble Spread


Control and Ensemble Mean D-SHIPS Errors, and Average Ensemble Spread (With land correction)

0

10

20

30

40

12 24 36 48 60 72 84 96 108 120


Inte

ns

ity

Err

or

or

En

se

mb

le S

pre

ad

(k

t)Control

Ensemble Mean

Ensemble Spread


Sample N-AWIPS Ensemble Product(Developed by M. Mainelli)


Preliminary Conclusions from SHIPS Ensembles

• SHIPS ensembles of limited utility over the ocean due to small forecast spread

• More useful for storms near land masses– 3-5 day forecast improvements of ~10%

• Analysis will be performed using final best track as well as the parallel versions of SHIPS at the end of the 2002 season

• Results will be presented at the IHC


Monte Carlo Wind Speed Probabilities(New “Insurance Friends” Project)

• Determine ensemble of forecast tracks using probability distributions from NHC official track forecast errors – Generalization of method used for tracks for SHIPS ensembles

• For each track, determine intensity forecast by sampling from NHC intensity error distribution– Adjustment for movement over land using empirical decay

equation from SHIPS• For ensemble members with high enough wind speeds,

estimate wind radii (34, 50 and 64 kt) from observed sample

• Calculate field of probabilities for various wind thresholds from ensemble track/radii forecasts

• Plans for Atlantic prototype by June 2003


Future Directions

• Expand the tropical cyclone genesis parameter to a multibasin, basin-wide application.

• Track probability density functions derived from Jonathan Vigh’s Kilo-ensemble track forecasts

• Satellite based tropical cyclone surface wind analysis

October 17, 2002National Hurricane Center Current and Future Tropical Cyclone Projects at...

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