Global and regional OSEs at JMA

Ko KOIZUMI

Numerical Prediction Division

Japan Meteorological Agency

Contents• Experiments with Global Spectral Model

– Asia-Pacific RARS and EARS– MTSAT-1R Clear-Sky Radiance– BUFR AMV (incl. MTSAT-1R Hourly AMV) instead of

SATOB

• Experiments with Meso-Scale Model– BUFR AMV (incl. MTSAT-1R Hourly AMV) instead of

SATOB– Doppler radar radial wind– Ground-based GPS

Global Experiments Specification

• Model: Global Spectral Model TL319L40• Assimilation:

– 4D-Var method– Inner model resolution: T106L40– Assimilation window: six hours– Six-hourly cycle

• Experiment period: one month each for summer and winter

• Forecasts: 216 hour forecasts once a day at 12 UTC

ATOVS used in Global Analysis

Early Analysis

Cycle Analysis

Data cut-off time : 2h20min.

Data cut-off time :

11h35min.(00 and 12 UTC)

5h35min.(06 and 18 UTC)

Coverage of RARS data

EARS

AP-RARS

2008.5.12

Analysis difference of 20hPa height(Early analysis – Cycle analysis)

06 UTC 25 Sep. 2006

Data from Beijing and Crib Point were provided by AP-RARS

06 UTC 25 Sep. 2006

Data from Beijing and Crib Point were provided by AP-RARS

with AP-RARS

w/o AP-RARS

Comparison of RMSE scores(winning % among 30 forecasts in September 2006)

(forecast hours)

Almost neutral for scores of troposphere

EARS (EUMETSAT Advanced Retransmission Service)

EARS data (AMSU-A)at 12 UTC 17 June 2007

EARS data (AMSU-B)at 12 UTC 17 June 2007

Analysis difference of 500hPa height

w/o EARS with EARS

Comparison of RMSE scores(winning % among 30 forecasts in June 2007)

(forecast hours)

•Positive impacts mainly on early hours of forecasts•Difference of impacts of AP-RARS and EARS might be due to the difference of data amount

MTSAT-1R Clear-Sky Radiance

• Infrared 3 channel (6.5-7.0 μm)• Averaging radiances of cloud-fre

e pixels in a 16 x 16 pixel region (60km x 60km at nadir)

• Thinned to 2 x 2 degree longitude/latitude and to every two hours

• Variational bias correction applied

Weighting Function

0

100

200

300

400

500

600

700

800

900

10000 0.001 0.002 0.003 0.004

dτ / dp

Pre

ssur

e(hP

a)

Comparison of RMSE scores(winning % among 31 forecasts in Aug. 2006 and Jan. 2007)

August 2006

January 2007

Typhoon track forecasts(Typhoon center position errors in August 2006)

RED: w/o MTSAT-1R CSRBLUE: with MTSAT-1R CSR

AMV in BUFR format (instead of SATOB)

• Larger amount of data, including hourly reports of MTSAT-1R AMV, are available

• Data selection using Quality Indicator (contained in the reports) is possible

More strict data selection from larger amount of candidates improves the forecasts

Data selection strategyMSM-DAGSM-DA MSM-DAGSM-DA

175hPa

225hPa

275hPa

400hPa

825hPa

975hPa

IR-NH,SH & WV-NH,SH AMVs

IR-NH,SH AMVs

ALL AMVs

ALL AMVs

ALL AMVs ALL AMVs

ALL AMVs

HL ML LL HL ML LL

I R 94/ 94 94/ 94 86/ 85 84 88 85

VIS -/ - -/ - -/ 88 - - 84

WV 95/ 95 -/ - -/ - 88 - -

I R 94/ 90 90/ 90 80/ 80 82 88 85

VIS -/ - -/ - 82/ 82 - - 82

WV 94/ 94 -/ - -/ - 84 - -

I R 60/ 60 60/ 60 60/ 60 60 60 60

VIS -/ - -/ - 60/ 60 - - 60

WV 60/ 60 -/ - -/ - 60 - -

I R 98/ 96 96/ 94 84/ 84 84 84 85

VIS -/ - -/ - 84/ 84 - - 84

WV 95/ 90 -/ - -/ - 88 - -

MTSAT-1R

Meteosat-9

GOES-11/ 12

extratropics(NH/ SH) tropics

Meteosat-7

Thinning: One datum in a 2 degree x 2 degree box in the assimilation window (6 hours)

Data not used mainly due to irremovable biases of data (or model)

QI threshold

Comparison of RMSE scores(winning % among 30 forecasts in Sep. 2005 and Jan. 2006)

September 2005

January 2006

TEST

CNTL

Num. of Samples

TEST

CNTL

Num. of Samples

Typhoon track forecasts(Typhoon center position errors in Sep. 2005)

RED: with BUFR AMVsBLUE: with SATOB AMVs

Regional Experiments Specification(except for GPS experiment)

• Model: MesoScale Model– Non-hydrostatic grid model with 5km grid distance

• Assimilation:– 4D-Var system based on a hydrostatic spectral model (former op

erational model)– Outer/ Inner resolution: 10km/20km– Assimilation window: six hours– Three-hourly cycle

• Experiment period: one or two weeks in a rainy season• Forecasts: 33 hour forecasts were made six-hourly (03,

09, 15 and 21 UTC initials)

Data selection strategyMSM-DAGSM-DA MSM-DAGSM-DA

175hPa

225hPa

275hPa

400hPa

825hPa

975hPa

IR-NH,SH & WV-NH,SH AMVs

IR-NH,SH AMVs

ALL AMVs

ALL AMVs

ALL AMVs ALL AMVs

ALL AMVs

Thinning: One datum in a 200 km x 200 km box,-in 6-hour assimilation window (test 1)-in every one hour (test 2)

HL ML LL

I R 95 95 86

VIS - - 86

WV 96 - -

MTSAT-1R

QI threshold

Data not used mainly due to irremovable biases of data (or model)

Results of an experiment in 1-15 July 2007

Against Japan Sonde Wind

hP

a

RMSE（m/s）

1000

800

600

400

200

2.8 3.0 4.0 4.2 4.43.2 3.4 3.6 3.8

Against Japan Sonde Wind

hP

a

ME（m/s）

1000

800

600

400

200

－1.2 －1.0 －0.8 －0.6 －0.4 －0.2 0.0 0.2－１.4

Against Japan Sonde Wind

hP

a

RMSE（m/s）

1000

800

600

400

200

2.8 3.0 4.0 4.2 4.43.2 3.4 3.6 3.8

Against Japan Sonde Wind

hP

a

ME（m/s）

1000

800

600

400

200

－1.2 －1.0 －0.8 －0.6 －0.4 －0.2 0.0 0.2－１.4

Against R／A

Th

rea

tS

core

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35 40 45 50

mm/3hour

Against R／A

Bia

sS

core

mm/3hour0 5 10 15 20 25 30 35 40 45 50

0.80

0.90

0.78

0.82

0.84

0.86

0.88

0.92

0.96

0.98

1.00

0.94

Against R／A

Th

rea

tS

core

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35 40 45 50

mm/3hour

Against R／A

Bia

sS

core

mm/3hour0 5 10 15 20 25 30 35 40 45 50

0.80

0.90

0.78

0.82

0.84

0.86

0.88

0.92

0.96

0.98

1.00

0.94

RED: with SATOB AMVsGREEN: with BUFR AMVs (one datum per six hours)BLUE: with BUFR AMVs (one datum per one hour)

Threat scores of 3-hour precipitation forecast against analyzed precipitation

RMSE of wind speed forecasts at ft=3 against radiosonde observation in Japan

Weather Radars of JMA

Kushiro

Sendai

Tokyo

ShizuokaNagano

NagoyaOosaka

Murotomisaki

Sapporo

Akita

Niigata

FukuiMatsue

Hiroshima

Fukuoka

Tanegashima

Naze

OkinawaIshigaki-jima

Hakodate

PINK Doppler radar used in the analysis for MesoScale Model

YELLOW

Doppler radar planned to be used in the analysis for MesoScale Model

CYAN

Not yet Doppler-ized

Preprocessing of the data

Original data3D volume scan(resolution)-500m (radius)-0.7deg.(azimuth)-15 pre-set elevation angles

Averaged data

(resolution)-5km (radius)-5.625 deg.(azimuth)-15 pre-set elevation angles

Thinning &Quality control

Thinning (2D or 3D)

All data 　　　　　　　　　　 2D thinning 　　　　 　　　　 3D thinning

Considering only two-dimensional data distribution on a cone of an elevation angle

Easy to implement but too dense near the radar

Considering three-dimensional distribution of all data

20km horizontally0.5km vertically

Quality ControlFollowing data are rejected• Number of samples in an averaging volume is smaller

than or equal to 10• Range of velocity in an averaging volume is larger

than 10m/s• Departure from first-guess is larger than 10m/s• Velocity is lower than 5m/s

– Coherent MTI algorithm sometimes works wrong with slow-moving particles

• Within 10km from the radar– To avoid backscattering noise

• Elevation angle is larger than 5.9 degree– To avoid contamination from raindrop falling

Statistical scores(8-17 June 2006)

Threshold value (mm/3hour)

Green: with Doppler velocity of Tokyo radar (w. 3D thinning)Red: w/o Doppler velocity of Tokyo radar

Threat scores of 3-hour precipitation RMSE of wind speed of six-hour forecasts against radiosondes

RMSE (m/s)

Hei

ght

(hP

a)

Impact of different thinning method

Threshold value (mm/3hour)

Green: 3D thinningRed: 2D thinning

Threat scores of 3-hour precipitation

Observationw. Tokyo radar Doppler vel.(3D thinning) w/o Tokyo radar Doppler vel.

w/o Tokyo radar Doppler vel.Observation

An example of 3-hour precipitation forecast

FT=9

FT=12

w. Tokyo radar Doppler vel.(3D thinning)

• Over 1,000 GPS receivers are owned by Geographical Survey Institute

• A real-time analysis system of ZTD and PW has been installed in JMA headquarter.

Ground-based GPS observation

GPS real-time analysis shows good agreement with radiosonde observation

(August 2005 and January 2006)

Quality control etc.• PW value is modified

according to model topography

• PW smaller than 1mm or larger than 90mm is rejected

• A datum is rejected when the departure from first guess is larger than 8mm

• A datum is rejected when the departure is larger than 5mm and differs from the averaged departures of surrounding data (within 20km) for 5mm or larger

• No thinning applied

Actual topographyModel topography

A B

C

Statistical scores for 3-hour precipitation(1 to 13 Sep. 2006)

Positive impact at FT=9 and afterPrecipitation is suppressed in early stage

The experiment was performed with the hydrostatic spectral version of MSM and the same 4D-Var as in the other experiments except for 3-hour assimilation window

An example of 3-hour precipitation forecast(FT=6-9 from 00 UTC 6 Sep. 2006)

Observation with GPS PW w/o GPS PW

Seems good, however …

Analysis increments of specific humidity (for positive departure of PW)

-6 -4 -2 0 2 4 6 8 (g/kg)

Hei

ght (

km)

0

2

4

6

8

10

When an integrated value is assimilated, the increment distribution depends on the system

insufficient(?)

mm

Summary• RARS

– Improve the operational forecast– Impact depends on the amount of available data

• CSR of MTSAT-1R– Improve the forecast especially in boreal summer– Improve typhoon track forecast

• BUFR AMV– Advantage to SATOB AMV in data amount and QI– “more strict data selection from larger volume of candidates” is

preferable to the forecast• Doppler velocity

– Impact is sensitive to data thinning• Ground-based GPS

– Positive impact can be acquired even from the near real-time data– Since the vertical distribution of analysis increment from vertically

integrated observation (such as ZTD or PW) depends on the assimilation system, some modifications to the assimilation system might be able to enhance the impacts of the data