tropicalcyclone tropical cyclone studies by microwave sensors chandra mohan kishtawal asdmog asd/mog...
TRANSCRIPT
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TropicalTropical CycloneCycloneStuStudiesdies
bbyyMicrowavMicrowavee SensorsSensors
Chandra MohChandra Mohan Kishtawalan KishtawalASDASD/MOGMOG
Space ApplicSpace Applications Centreations Centre
ISRO/MOP/SM-2.1
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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
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Cyclone GeolocationGeolocation Using Microwave Observationsave Observations
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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
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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
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A Comparison of
Microwave
and
Infrared
Observations of Tropical Cyclones
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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
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85 GHz“Cold” PrecipitationAgainst Warmer Ocean Background
37 GHz“Warm” PrecipitationAgainst ColderOcean Background
Two Main Microwave Sensing Channels for TC’s
ColdWarm
Warm
Cold
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PARALAX PROBLEM IN CONICAL SCAN
Paralax Error
ParalaxErrors
85 GHz15-20 km
37 Ghz ~ 5 km
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08-Aug-2000, 1057 UTC TC-JALAWAT
37 GHz 85 GHz
Example of Paralax
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Differences between TMI derived TC centers from Best-track Positions (IMD) ( After Paralax Compensation)
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CycloneCyclone IntensityIntensity EstiEstimationmation
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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.
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15582
33108
CONVCTIVE ORGANIZATION WITHIN STORMS
100 K
310 K
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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
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1.0O
2.5O
100 K
310 K
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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
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ALGORITHM DEVELOPMENT BY GENETIC ALGORITHM DEVELOPMENT BY GENETIC ALGORITHMSALGORITHMS
Randomized search and optimization technique guided by the principle of natural genetic systems.
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PARENT-1 PARENT-2
CHROMOSOMES
GENETIC EVOLUTION OF PATTERNS
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PARENT-1 PARENT-2
CHILDREN
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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
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PARAMETER LIST FOR INTENSITY ESTIMATION
MEAN BT10(H)
10-MAX(BT10H)
10-MIN(BT10H)
10-GHZ BT WITHIN 2 DEG RADIUS
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Distance from Center
Maximum Sensitivity Region
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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
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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)
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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
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Automatic Intensity Estimation : Skill for Global TCs
Paper to appear in GRL:April-2005.
TC-CASES
NIONATLNEP
(Mean ~ 11 kt)
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Depression Severe Cyclone
JTWC : 25 KtEstimated : 27 Kt
JTWC : 60 KtEstimated : 52 Kt
Automatic Intensity Estimation : Case Studies
18-Oct-2000 22-May-2001
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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
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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
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CycloneCyclone IntensityIntensity PrePredictiondiction
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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)
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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
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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
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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
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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
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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
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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
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BT (100-325)
85 GHZ
10 GHZ
+1 -1
BAR-CODING FOR SIGNAL ENHANCEMENT
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-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)
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