TropicalTropical CycloneCycloneStuStudiesdies

bbyyMicrowavMicrowavee SensorsSensors

Chandra MohChandra Mohan Kishtawalan KishtawalASDASD/MOGMOG

Space ApplicSpace Applications Centreations Centre

ISRO/MOP/SM-2.1

Channel Number 1/2 3/4 5 6/ 7 8/ 9

Center Freq [GHz] 10.65 19.35 21.3 37.0 85.5

Beam EFOV [kmxkm] 63x37 30x18 23x18 16x9 7x5

Objectives : TC Geolocation, Intensity Estimation and prediction Using Microwave observations

Data : TMI observations for TC Over global oceans during past 5 years ( more than 400 TMI scenes analyzed).

TC Track and Intensity data was collected from NHC/TPC archives for algorithm development and validation

Cyclone GeolocationGeolocation Using Microwave Observationsave Observations

Warned region is 3 times larger than the region where actual damage takes place. This proves Very Expensive. Also this shows the importance of Even A marginal improvement in track prediction accuracy.

Warned region

Damaged Region

0 10 20 30 40 50 60 70 80 90In itia l Position E rror ( Km )

0

50

100

150

200

250

300

350

24 -

H F

CS

T P

ositi

on E

rror

( K

m )

Impact of Initial Position Error on Track Forecast

A Comparison of

Microwave

and

Infrared

Observations of Tropical Cyclones

10 GHz

19-37 GHz

85 GHz

TRMM - 33151 Rain RateB

TB

TB

T

100 K

310 K

0 50 mm/h

Sensitivity of different TMI frequencies to TC-Rain

2 km

4 km

8 km

85 GHz“Cold” PrecipitationAgainst Warmer Ocean Background

37 GHz“Warm” PrecipitationAgainst ColderOcean Background

Two Main Microwave Sensing Channels for TC’s

ColdWarm

Warm

Cold

PARALAX PROBLEM IN CONICAL SCAN

Paralax Error

ParalaxErrors

85 GHz15-20 km

37 Ghz ~ 5 km

08-Aug-2000, 1057 UTC TC-JALAWAT

37 GHz 85 GHz

Example of Paralax

Differences between TMI derived TC centers from Best-track Positions (IMD) ( After Paralax Compensation)

CycloneCyclone IntensityIntensity EstiEstimationmation

Operational Centers worldwide still depend on Dvorak’s technique for TC intensity estimates that uses manual pattern-analysis of VIS/IR images. In operational set-up it proves slow.

We developed an automatic technique for TC intensity assessment, that is quick, and reliable.

15582

33108

CONVCTIVE ORGANIZATION WITHIN STORMS

100 K

310 K

10 GHz

19-37 GHz

85 GHz

TRMM - 33151 Rain RateB

TB

TB

T

100 K

310 K

0 50 mm/h

Sensitivity of different TMI frequencies to TC-Rain

2 km

4 km

8 km

1.0O

2.5O

100 K

310 K

ISOIN = 0.621ISOOUT= 0.234

ISOIN = 0.832ISOOUT= 0.523

ISO = i Øi /((n-1)* Ā), n=12 (5) Øi = (Loge(Ni+1) – Ā) if Loge(Ni+1) Ā, otherwise Øi =0

NI = No of TMI pixels with PCT < 240 K

Quantifying Isotropy of Convection

ALGORITHM DEVELOPMENT BY GENETIC ALGORITHM DEVELOPMENT BY GENETIC ALGORITHMSALGORITHMS

Randomized search and optimization technique guided by the principle of natural genetic systems.

PARENT-1 PARENT-2

CHROMOSOMES

GENETIC EVOLUTION OF PATTERNS

PARENT-1 PARENT-2

CHILDREN

12

1 2

Random Initialization of Equation Population

Select the best individuals as per “cost”

Best ones get chance to reproduce

Offspring again reproduces as per merit

Mutation of a fraction of low-order population

Fittest individual emerges after N generations

A Simplified Concept of Genetic AlgorithmA Simplified Concept of Genetic Algorithm

PARAMETER LIST FOR INTENSITY ESTIMATION

MEAN BT10(H)

10-MAX(BT10H)

10-MIN(BT10H)

10-GHZ BT WITHIN 2 DEG RADIUS

Distance from Center

Maximum Sensitivity Region

CONVECTIVE ISOTROPY (SYMETRY OF THE REGION DEFINED BY PCT < 240 K)

100 K

310 K

ISO = i Øi /((n-1)* Ā), n=12 (5) Øi = (Loge(Ni+1) – Ā) if Loge(Ni+1) Ā, otherwise Øi =0

NI = No of TMI pixels with PCT < 240 K

MSW(kt) = a-d/(i-7.09)+(e+f-d)/ ((-52.15+c/b-f/(h-75.75))*

(-21.96))+b-168.17

Term Expression

a Mean of 10-H for r < 1o

b Convective Isotropy for r < 1o

c Convective Isotropy for 1o < r < 2.5o

d Mean of cold 10-V pixels ( r < 1o)

e Sum of 11 warmest 10-H ( r < 1o)

f

g

Sum of 11 coldest 10-H ( r < 1o)

Mean (37-V – 37-H) ( r < 1o)

No Parameter Low Intensity Storms (MSW < 64 Kt)

High Intensity storms

(MSW > 64kt)

1

2

Mean BT10-h

in R < 1 deg

Mean of coldest 10 pix

1.33

0.54

1.33

0.69

3 Isotropy (inner)

0.02 0.73

4 Isotropy (outer)

0.04 0.24

SENSITIVITY OF DIFFERENT TERMS

Automatic Intensity Estimation : Skill for Global TCs

Paper to appear in GRL:April-2005.

TC-CASES

NIONATLNEP

(Mean ~ 11 kt)

Depression Severe Cyclone

JTWC : 25 KtEstimated : 27 Kt

JTWC : 60 KtEstimated : 52 Kt

Automatic Intensity Estimation : Case Studies

18-Oct-2000 22-May-2001

Very Severe Cyclone-1 Very Severe Cyclone-2

JTWC : 94 KtEstimated : 88 Kt

JTWC : 110 KtEstimated : 120 Kt

Automatic Intensity Estimation : Case Studies

18-May-1999 16-Oct-1999

Automatic Intensity Estimation : Skill Levels

TMI estimated v/s JTWC Intensity Correlations and RMS Error

Training Set ( 60 TMI Scenes) : 91%

Verification Set ( 20 TMI Scenes) : 90%

Mean RMS error : 12.53 Kt

0 20 40 60 80 100 120 140 160JTW C Intensity ( K ts)

0

20

40

60

80

100

120

140

160

TM

I-E

stim

ated

Inte

nsity

(K

ts)

Verification Set

Tra in ing set

Compar

e

with

Bankert & Tag-2002RMSE : 19.7 KtNEP+ ATL + IO

CycloneCyclone IntensityIntensity PrePredictiondiction

Area of cyclonic influence

(Ro=u/(f*r) ~ 1, core boundary)

Environmental forcing beginsTo take over.

Eye wall

Principal Band

•The outward edge of bands respond earliest to environmental flow •Convective bands transport large cloud mass upward, much larger than eye-wall

OBSERVATION-1 : Intensification Process Of Weak Cyclones ( Msw < 64 Kt) is very much different from that of strong cyclones (MSW > 64 kt)

BT ( 37-H)

Predictors Intensity Change

For Normal Intensity Cyclones

Mean of 5 low frequency channels over the un-masked regionConvective Mass in high CLW region ( BT-37H > 240 K) Convective Mass = CMCM=(240-PCT)1.1

if PCT < 240 K , Else CM=0

Minimum PCT in high CLW region

High CLW region

3 0 3 5 4 0 4 5 5 0 5 5 6 0

M axim um Intensity in Sam ple ( K t )

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cor

rela

tion

Coe

ff

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0C

orre

latio

n C

oeff.

M ean BT ( 10-V )

M ean BT (19-V )

M in PC T

W ITH O U T C LO U D M ASK

W ITH C LO U D M ASK

With the use of Cloud Mask, the correlations of low frequency channels with 24-hour intensity change improve, implying that much of the signals arrive from ‘outside’ the storm ( due to wind ? SST ? ) However these are unusable if storm intensity increases beyond ~ 60 kt.

PCTmin is computed from masked area in both the graphs. It is shown only for comparison

Convective Mass in Inner Core ( r < 1.3o) Convective Mass = CMCM=(230-PCT)1.1

if PCT < 230 K , Else CM=0

Convective Isotropy in Inner Core ( r < 1.3o)

Convective Isotropy in outer Core ( 1.3o < r < 2.5o)

Low Isotropy Case

High Isotropy Case

PCT (K)

PCT (K)

Predictors Intensity Change

For High Intensity Cyclones

PredictorsHigh Intensity

PredictorsHigh Intensity

Minimum PCT in inner coreAverage PCT in inner coreAverage 10V BT inner coreAverage 10V BT in outer core

Convective SHEAR ( angular shift b/w high density region of high BT(37H) and that of low PCT in 85 GHz image.

BT (37-H)

PCT

Picking the SST Signatures

Mean of 10 GHz (V) BT in45o angular section surroundingthe direction of cyclone motionduring past 12 hours. A Pixel is Considered only if BT(37-H) < 185 K. This parameter may pickSST signatures ahead of a TC Direction of TC Motion in last

12 hours

Predictors Intensity Change

For High Intensity Cyclones

100 120 140 160 180 200 220 240 260 280 300 320 340Brightness Tem p (K )

0

20

40

60

80

100M

ean

Pop

ulat

ion

INTENSIFYING STO RM S

C H AN N EL

10-V

19-V

37-V

85-V

100 120 140 160 180 200 220 240 260 280 300 320 340Brightness Tem p (K )

0

20

40

60

80

100

Mea

n P

opul

atio

n

D EC AYIN G STO R M S

C H AN N EL

10-V

19-V

37-V

85-V

Mean Histograms Of Decaying And Intensifying Storms

100 K

310 K

BT (100-325)

85 GHZ

10 GHZ

+1 -1

BAR-CODING FOR SIGNAL ENHANCEMENT

-40 -30 -20 -10 0 10 20 30 40 50 60Predicted 24-H In tensity C hange ( K t)

-40

-30

-20

-10

0

10

20

30

40

50

60

Obs

erve

d 24

-H In

tens

ity C

hang

e (

Kt)

T ra in ing ( N = 180 )

Validation ( N = 49 )

Performance of Prediction Algorithm

(Accuracy ~ 8 kt)