Numerical Weather Prediction Division The usage of the ATOVS data in the Korea Meteorological...

Numerical Weather Prediction Division

The usage of the ATOVS data in the Korea The usage of the ATOVS data in the Korea Meteorological Administration (KMA)Meteorological Administration (KMA)

Sang-Won Joo

Korea Meteorological Administration

• Feb. 1999 : TOVS data assimilation in the Global model (1DVAR)

• Nov. 2001 : AOTVS(HIRS+AMSU-A) assimilation in the Global Model (1DVAR)

History of the satellite sounding assimilation in KMA


Introduction 1. 1DVAR in KMA

- Background error implies geographical variation

- Observation error is calculated from the innovation and background error

• Evaluation of effect on the model performance- Evaluation of the time averaged fields

- Typhoon track forecast error


Distribution of Background Error

TTB-BTB [K]

-8 -6 -4 -2 0 2 4 6 8

Fre

qu

en

cy

0

20

40

60

80

NHTRSH

Inhomogeneous background error


Methodology

SSeee

NNeee

trshb

trnhb

2515,)25(15

2515,)25(15

221121 ,, vevvRvevvC jjj

21

211

11

211 ,, vevvRvevvC jjj

Error variance changes but correlation is fixed

Damping area is assigned

Eq. 20N20S90S 90N

e

be betre nheshe

Error covariance becomes

Inverse matrix of error covariance becomes


Observation error Statistical Method for observation error

Assumption

1. Tangent linear approximation

2. No correlation between background error and RTM error

3. Biases are well removed

Derivation

xxHxHxxHxH ttttb HH 1st assumption

yxxHyyxxHxHyxH tttttob HH)(

TTTTobob yyxxFyxHyxH HH 2nd assumption

22y

Tx

TTxxEF HHHH

TTobob yxHyxH HBHR 3rd assumption

RTM error


Meaning of the resulting equation

TTobob yxHyxH HBHR

• Square of innovation

• First estimates of Derber and Wu (1999)

• Background error in radiance space

• Observation error

• RTM error and instrument error

• Innovation is the sum of observation error and background error if there is no correlation

• The resulting equation says the above statement in radiance space.


Feedback of observation error

RR

OBSERVATIONOBSERVATION

BACKGROUNDBACKGROUND

BB

1DVAR1DVAR

MODELMODELINNOVATIONINNOVATION

ANALYSISANALYSIS

• Relationship exists between observation error and NWP analysis through B

• Improvement of background error can readily affect the observation error

• The error ratio (eigenvlaue) is changed automatically

Benefits of our method


MODEL Basic Equation Primitive Equation

Resolution Triangular truncation of 213 in horizontal and 30 levels sigma-p hybrid coordinate from surface to 10hPa

Numerical Scheme Semi-implicit time integration, spherical harmonics for horizontal representation and finite difference in the vertical

Radiation Lacis and Hansen (1974) for short-wave and water vapor, carbon dioxide and ozone for long-wave

Convective Parameterization

Kuo type(1974)

Large Scale Condensation Kanamitsu et. al. (1983)

Shallow Convection Tiedke(1985)

Gravity Wave Drag Iwasaki et. al. (1989)

PBL scheme 2 Layer method from Yamada and Meller (1982)

Land Surface Processes SiB

ANALYSIS Method 3 Dimensional Multivariate Optimum Interpolation

Resolution 0.5625 degrees

Update Cycle 6 hourly

Description of the Global Model


Observation (ATOVS TBB Data=OTB)

CHANNEL CHANNEL CHANNEL CHANNEL CHANNEL

HIRS1 HIRS6 HIRS12 AMSU5 HIRS10



HIRS4 HIRS10 HIRS15 HIRS8 AMSU13

HIRS5 HIRS11 AMSU4 HIRS9 AMSU14

Background (Profile=BPR)

Variable Level Source Element

Temperature [K] Surface – 10 hPa 6 hour forecast from GDAPS 1-16

Temperature [K] 10 hPa – 0.4 hPa NESDIS retrieval 17-20

Specific Humidity [g/ g] Surface – 300 hPa 6 hour forecast from GDAPS 21-27

Skin Temperature Surface NOAA weekly SST analysis 28

Pressure [hPa] Sea Level 6 hour forecast from GDAPS 29

U [m/ s] Surface 6 hour forecast from GDAPS 30

V [m/ s] Surface 6 hour forecast from GDAPS 31

CTP [hPa] NESDIS retrieval 32

Cloudness NESDIS retrieval 33


Others

• Quality control(Eyre, 1992)

• Forward operator: RTM(RTTOV version 6) + Vertical interpolation

• Minimization algorithm: BFGS method (quasi-Newtonian algorithm)

• Dimension reduction to the TOVS BUFR format

• Optimum interpolation interface (Lorence 1986, Eyre 1993)

• Bias correction: Scan angle and air mass bias correction (Joo and Okamoto, 2000)


Flowchart

Background(B), Analysis(A)

Observation(O),

Profile(PR)Brightness Temperature(TB)

PREFIX: SURFIX:

Departure(D)

no

ADJOINTADJOINT

MINIMIZATION

BIAS C.BIAS C.DTB

DTB_B

J & J

APR

BPR

APR

RTMRTM

BIAS C.BIAS C.

OTB ATB

OTB_B

- DPR

-

yes1st

Background Error

Observation Error

Radiance Space

Physical Space

BFGSBFGS


Flow chart of the 1DVAR with NWP analysis

Background Error Bias

1DVAR

3D O.I.

Global Model

10 day forecast

6 hour forecast

FEP

back

grou

nd

24 and 48 hour Forecasts for 1 Month

B

ATOVS data

Synoptic Obs.

O-B for 1 Month

Observation error

Diagnostics

R BiasB

Tv

1DVAR


Analysis verification (September 2001)


Observation verification(Sep. 2001)


Averaged typhoon track forecast error (TY0111-TY0123)

AVERAGED TYPHOON TRACK ERROR

0

100

200

300

400

500

600

12 24 36 48 60 72

FORECAST HOUR

TRAC

K ER

RO

R[k

m]

OPER1DVA


Summary

• The 1DVAR is developed in KMA to assimilate the ATOVS data

• The statistics shows positive effect mostly and also in ASIA

• Typhoon track is well predicted with the 1DVAR and it is mainly caused by the better specification of the Pacific High

• The 1DVAR is in operation from 1 November, 2001


Future Plans

• Improvement of the bias correction scheme

• Utilization of the ATOVS data over the land

• Improvement of cloud detection scheme

• Implementation of the 1DVAR in the regional model


Verification with RAOB

• Poor performance near surface and tropopause

• Large improvement in the S.H.

• We need more improvement in the N.H. and Tropics.


ATOVS InformationCNTL (NB)

-8

0

8

-8 0 8

(OTB-BTB) [K](T

TB-B

TB)

[K]

• The 2nd and 4th quadrants data mislead the analysis.

• There are many data in the 2nd quadrant.

• Observation should be in the same direction as RAOB from background

(T-B) X (O-B) > 0

B.G.(B) Obs(O)Anal(A)

True Value(T)

1 2

O-B

Right Information

T-BWrong

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