microwave-tc intensity estimation

Microwave-TC intensity estimation

Ryo OyamaMeteorological Research Institute

Japan Meteorological Agency

Contents1. Introduction

2. Estimation of TC Maximum Sustained Wind (MSW) using TRMM Microwave Imager (TMI) data

3. Estimation of TC Minimum Sea Level Pressure (MSLP) based on warm core intensity observed by Advanced Microwave Sounding Unit-A (AMSU-A)

4. Future plan for Microwave-TC intensity estimation

2

IntroductionSatellite observations are essential for analysis of tropical cyclone (TC) intensity, such as Minimum Sea Level Pressure (MSLP) and Maximum Sustained Wind (MSW), particularly where in situ observations are sparse.

Use applications:• Early analysis• Best track analysis• Creation of TC bogus

vortex for NWP initial analysis

TC intensity analysis(MSLP, MSW)

In situ observations : Low spatial resolution

Satellite observations: Wide coverage, high temporal resolution (for MTSAT)

3

Dvorak technique(based on Infrared image from geostationary satellite)

:A primary method based on satellite observation

Brightness temperature (TB) of MTSAT infrared channel for TC Songda (1102)：Cloud top temperature

Dvorak technique requires analysis skills based on enough experience !

Curved band pattern Eye pattern Shear pattern

00UTC 23 May 2011 20UTC 26 May 2011 23UTC 28 May 2011

CDG

CMG

W

B

LG

MG

DG

OW

WMG

Cold

Warm

4

However, Dvorak technique has some weak points.

Jun.22 11UTC(CB cluster)

Jun.23 20UTC(CB cluster)

Jun.24 10UTC(Curved band)

Jun.26 06UTC(Shear/LCV)

970975980985990995

100010051010

00 12 00 12 00 12 00 12 00 12 00 12 00 12 00

MSL

P (h

Pa)

Month/Day and Time (UTC)

Dvorak MSLP Best track MSLP

6/21 6/22 6/23 6/24 6/25 6/26 6/27

TC Meari (1105)

in situ observation at Miyako-jima:

982 hPa

5

Satellite microwave sensor can observe TC internal structure !

Warm core

MW radiation from cloud/rain

:ice/liquid water

55 GHz MW radiation from the atmosphere

: temperatureIR radiation from cloud top

: cloud top temperature

gray : clouds, white: ice cloud/rain, light blue : liquid rain

Eye

wal

l

Spira

l rai

n ba

nd

10-19 GHz MW radiation from sea surface

: sea foams induced by surface winds

Sea surface with sea foams induced by winds

Scattering by ice particles

MTSAT NOAA/AMSU TRMM/TMI

6

Estimation of TC maximum sustained wind (MSW) using TRMM Microwave

Imager (TMI) data

7

Basic TC structure seen in rain and wind distributions

Pre

ssur

e le

vel (

hPa)

Inner core (radius < 100～200 km)

Convergence near the surface increases due to increase of inflow with tangential wind intensified,

Cloud and rain water increases as eye walls and rain bands are formed.

Radial cross section through an idealized, axially symmetric hurricanein TC inner core (Wallace and Hobbs 2006)

8

TRMM/TMI observation

TMI microwave imager (November 1997～)• Onboard TRMM for observing rain and sea surface over the tropical region.• Channel frequencies (GHz) : 10.7(V/H), 19.35(V/H), 21.3(V), 37(V/H) and

85.5(V/H).• Spatial resolution of data is 38.3 km (10.7GHz)～4.4 km (85.5GHz).• 3 observations per day at maximum available (depending on TC location)

TRMM

9

85GHz

TMI can obtain information on ice/liquid rain and sea surface.

10GHz 19GHz 37GHz

TMI TB images for TC Francisco (1327) at 1759UTC on 20 Oct 2013

R=200km

Emission from liquid cloud/rainand water vapor

Scattering by iceEmission from sea surface

with foams induced by winds

V-Pol V-Pol V-Pol V-Pol

H-Pol H-Pol H-Pol

21GHz

V-Pol

H-Pol

10

TC maximum sustained wind estimation (MSW) method using TMI observation

(TMI technique) • TMI technique had been developed by MRI/JMA in 2004-2011 and has

been validated in 2012-2013.

• TMI technique estimates MSW using information on ice/liquid rain distribution and sea foams induced by surface winds in TC inner core (radius < 2 degrees) obtained from TMI observation.

• MSW is estimated by using a multiple-regression equation where TB parameters computed using TMI TBs are used as the input variables. The TB parameters are also used for recognition of TB image pattern.

• The multiple-regression equations for MSW estimation were derived for respective TB image patterns from TMI observations in reference to JMA best-track data for TCs during 1998-2008.

11

Algorithm of TMI technique for MSW estimation

Domains for computing TB parameters

abou

t 200

km

2.0°

1.5°

1.0°

0.5°0.25°

TC Moving

(Step 1) TB parameters (max, average, min etc. in the defined domains) for TMI channels are computed.

(Step 3) MSW is estimated by using a multiple-regression equation for each TB image pattern.

(Step 2) TB image pattern of TC inner core (radius < 2 degrees) was determined (out of 10 patterns) using TB parameters.

Regression equation for MSW estimation:

MSW = A0 + Σ {A(n) x TBparam(n)} TBparam(n): TB parameters highly

correlated to MSWn = 1 to 7

12

85GHz TB

Validation of MSW estimates by TMI technique with reference to the best-track data

Black : Observations for TCs in 1998-2008(used for deriving the estimation equation)

Number = 749

Red : Observations for TCs in 2009-2012(independent on the estimation equation)

Number = 341RMSE = 6.26 m/sBIAS = 0.99 m/s

Relatively large estimation errors come from(i) Inadequate use of TB parameters for

estimation during TC formation stage(ii) Determination error of TC center

position

Best track MSW (m/s)

MSW

est

imat

e by

TM

I tec

hniq

ue (m

/s)

13

TC Soulik (1307)

13

18(m/s)33.451.5

46.341.230.9

/July

MSW

in situ observation at Yonaguni-jima

14

1015202530354045505560

00 12 00 12 00 12 00 12 00 12 00 12 00 12 00

MSW

（m

/s）

（UTC）

TMI MSWDvorak MSWJMA best track

7/7 7/8 7/9 7/10 7/11 7/12 7/13

TC Soulik (1307)

85GHz (PCT)

10GHz (H)

① ②③ ④

①

② ③ ④

in situ observation at Yonaguni-jima:

44 m/s (10-min ave.)

ice cloud/rain

liquid rain and sea foams

15

Summary and conclusion on MSW estimation by TMI technique

• TMI technique estimates MSW based on TB parameters computed using TMI TBs in TC inner core.

• Validation of the MSW estimates to best track data for TCs in 2009-2012 showed that RMSE is 6.26 m/s (comparable to Dvorak technique).

• It is essential to find in which situation MSW estimate by TMI technique could support operational TC intensity analysis, in addition to improvements of the algorithm.

16

Estimation of TC Minimum Sea Level Pressure (MSLP) based on warm core

intensity observed by Advanced Microwave sounding Unit-A (AMSU-A)

17

What is warm core ?• Warm core is formed near TC center, with a positive temperature anomaly

to the environment.• Warm core is a characteristic feature to identify TC intensity and TC size.

Warm air Low surface pressure

Large (small) warm core Large (small) TC size

TC Bolaven (1215)MSLP=940 hPa, MSW=41 m/s,

R30 = 555km

Temperature anomaly by AMSU-A for TC Danas (1324)MSLP=975 hPa, MSW=31 m/s,

Shortest radius of 30knot winds (R30)= 222km

Anomaly～5K at 200 hPa

Anomaly～11Kat 200 hPa

TB attenuation of microwave

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Advanced Microwave Sounding Unit-A (AMSU-A)AMSU-A

- is onboard NOAA and METOP series polar orbital satellites.- has been operated since 1998 (NOAA-15 is the first satellite for AMSU).- observes twice per day at maximum (5 satellites available)- consists of twelve channels (Ch3 - Ch14) for atmospheric temperature

sounding.

atmosphereAMSU-A 55-GHz band channels observe radiation from oxygen in the atmosphere.

width of scan line: about 2000 km

NOAA

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Weighting functions of AMSU-A channels

(Kidder et al. 2000)

trop

osph

ere

Field of View (FOV) of AMSU-A channels (open ellipses)

(kidder et al. 2000)

AMSU-A FOV size: 48km - 150 km

20

• AMSU-A channels for observing the troposphere are Ch4 (900 hPa level), Ch5 (600 hPa), Ch6 (400 hPa), Ch7 (250 hPa) and Ch8 (180 hPa).

• TBs for Ch4 and Ch5 for observing the lower troposphere tend to be attenuated significantly by rain near TC center.

MSLP estimation method based on TC warm core intensity observed by AMSU-A

(AMSU technique)

• AMSU technique was developed by MRI/JMA in collaboration with RSMC Tokyo – Typhoon Center in 2011-2012.

• This technique estimates TC Minimum Sea Level Pressure (MSLP) using AMSU-A brightness temperature (TB) anomaly corresponding to TC warm core intensity.

• A regression equation for MSLP estimation was derived using AMSU-A observations in reference to JMA best-track data for 22 TCs for 2008.

21

Basis of MSLP estimation from temperature anomaly corresponding to TC warm core

MSLP Environmental surface pressure

Surface pressure decrease equivalent to temperature anomaly at TC center

≅－

Tenv

Teye

Height

Top of the atmosphere

(ZTop)

Surface (Z=Z0) P0envP0

eye

Hydrostatic equilibrium theory ：

550 km～600 km(Default value)

Warm core

MSLP

22

Warm core intensity used for MSLP estimation

Max TB anomalyMax TB anomalyMax TB anomaly

TB anomaly forCh6 (～400 hPa level)



Maximum(defined as warm core intensity)

23

y = 0.0235x - 0.0965Number = 102

-4

-3

-2

-1

0

1

2

3

4

-40 -30 -20 -10 0 10 20 30 40 50 60 70 80

AMAX

_DIF

F(K

)

SIWAMAX

(c) Ch8

Correction of warm core intensity retrieval errorsand MSLP estimation

Error due to low spatial resolution (48～150 km) of AMSU-A observation

Scattering Index over Water (SIW)unde

rest

imat

ion

of w

arm

co

re in

tens

ity (K

)

MSLP estimation equation derived using AMSU-A observations with reference to JMA best track data for TCs in 2008 :

MSLP = SLOPE×(warm core intensity) + OFFSET

TB attenuation error due to ice particles

These warm core intensity retrieval errors are corrected by developed schemes.

Ch8

24

Validation of AMSU MSLPs to JMA best-track data for TCs during 2009-2011

y = 0.878 x + 120.99 R = 0.89

RMSE =10.1 hPaBIAS = 0.3 hPa

880

900

920

940

960

980

1000

1020

880 900 920 940 960 980 1000 1020

Estim

atio

n (h

Pa)

Best track MSLP (hPa)

2009 - 2011

Number of observations: 1029RMSE : 10.1 hPaBIAS: 0.3 hPa

Statistical validation revealed several characteristics of AMSU MSLPs:

1. Better quality of AMSU MSLPs for large TCs than compact TCs, suggesting a difficulty of observing small warm core.

2. Quality degrading of AMSU MSLPs due to too large microwave scattering near TC center.

3. Superiority of AMSU MSLPs to Dvorak MSLPs when TC is not compact and TC cloud pattern is “Curved band” or “Shear/LCV”.

25

Characteristics of MSLP estimates by AMSU technique

• Characteristics of MSLP estimates by AMSU technique (AMSU MSLP) are shown in comparison with JMA best track data and MSLP estimates by Dvorak technique (Dvorak MSLP) for three typical cases of TCs during 2009-2011.

• For TCs during 2009-2011, JMA best track data depends on Dvorak MSLP, while it does not depend on AMSU MSLP.

• Shortest radius of 30 knot winds (R30) from best track data is used as TC size related to warm core size. Average R30 value between 2000-2011 for each MSLP is also used as the criterion.

26

TC Meari (1105)

970975980985990995

100010051010

00 12 00 12 00 12 00 12 00 12 00 12 00 12 00

MSL

P (h

Pa)


Dvorak MSLP AMSU MSLP Best track MSLP

6/21 6/22 6/23 6/24 6/25 6/26 6/27

average R30 (261 km) < R30 of Meari (370 km)for MSLP of 975 hPa

AMSU-A TB anomaly (Ch6): ～400 hPa MW scattering (SIW) IR TB; Shear pattern

27

TC Noru (1113)

980

985

990

995

1000

1005

1010

12 00 12 00 12 00 12 00 12 00 12 00

MSL

P (h

Pa)



9/2 9/3 9/4 9/5 9/6

average R30 (197 km) < R30 of Noru (370 km)for MSLP of 990 hPa

AMSU-A TB anomaly (Ch6): ～400 hPa MW scattering (SIW) IR TB; Shear/LCV

28

TC Mirinae (0921)

950

960

970

980

990

1000

1010

00 12 00 12 00 12 00 12 00 12 00 12 00 12 00 12 00 12 00

MSL

P (h

Pa)



10/25 10/26 10/27 10/28 10/29 10/30 10/31 11/1 11/2

average R30 (354 km) > R30 of Mirinae (148 km)for MSLP of 960 hPa

AMSU-A TB anomaly (Ch6): ～400 hPa MW scattering (SIW) IR TB

29

Summary and conclusion on MSLP estimationby AMSU technique

• AMSU technique estimates MSLP using TC warm core intensity as observed by AMSU-A.

• MSLP estimates by AMSU technique tended to be better than those by Dvorak technique for incompact TCs with specific TC cloud patterns (Curved band or Shear/LCV).

• AMSU MSLP is expected to support operational MSLP analysis when in situ data is not available and the estimation accuracy of Dvorak technique is low.

30

Future plan for Microwave-TC intensity estimation

RSMC Tokyo – Typhoon center began to use TC intensity estimates by AMSU and TMI techniques as references for the operational TC intensity analysis in 2013.

Future works for further contribution of the estimation to operational TC intensity analysis are:

• Improvements to the current algorithms• Use of satellite observations other than AMSU-A and TMI for

TC intensity estimationSSMIS microwave imager/sounder (DMSP)AMSR2 microwave imager (GCOM-W1) ATMS microwave sounder (NPP)

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Thank you

32

microwave-tc intensity estimation

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