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
Data cut-off time : 2h20min.
Cycle Analysis
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)
w/o AP-RARS
06 UTC 25 Sep. 2006
Data from Beijing and Crib Point
were provided by AP-RARS
with 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
Weighting Function
100
200
300
Pressure(hPa)
• Infrared 3 channel (6.5-7.0 μm)
• Averaging radiances of cloudfree 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
0
400
500
600
700
800
900
1000
0
0.001
0.002
dτ/dp
第5.3.1.1　CSRの平均的な加重関数
0.003
0.004
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 CSR
BLUE: 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 strategy
GSM-DA
MSM-DA
ALL AMVs
ALL AMVs
IR-NH,SH & WV-NH,SH AMVs
225hPa
IR-NH,SH
AMVs
275hPa
ALL AMVs
ALL AMVs
QI threshold
Meteosat-7
Meteosat-9
GOES11/12
MTSAT-1R
175hPa
Thinning:
One datum in a 2 degree x 2
degree box in the assimilation
window (6 hours)
400hPa
Data not used mainly due to
irremovable biases of data (or
model)
825hPa
975hPa
ALL AMVs
extratropics(NH/SH)
tropics
HL
ML
LL
HL
ML
LL
IR
94/94
94/94
86/85
84
88
85
VIS
-/-
-/-
-/88
-
-
84
WV
95/95
-/-
-/-
88
-
-
IR
94/90
90/90
80/80
82
88
85
VIS
-/-
-/-
82/82
-
-
82
WV
94/94
-/-
-/-
84
-
-
IR
60/60
60/60
60/60
60
60
60
VIS
-/-
-/-
60/60
-
-
60
WV
60/60
-/-
-/-
60
-
-
IR
98/96
96/94
84/84
84
84
85
VIS
-/-
-/-
84/84
-
-
84
WV
95/90
-/-
-/-
88
-
-
Comparison of RMSE scores
(winning % among 30 forecasts in Sep. 2005 and Jan. 2006)
September 2005
January 2006
Typhoon track forecasts
(Typhoon center position errors in Sep. 2005)
TEST
CNTL
Num. of
Samples
RED: with BUFR AMVs
BLUE: 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
operational 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 strategy
GSM-DA
MSM-DA
ALL AMVs
ALL AMVs
IR-NH,SH & WV-NH,SH AMVs
175hPa
225hPa
IR-NH,SH
AMVs
Data not used mainly due to
irremovable biases of data (or
model)
275hPa
ALL AMVs
ALL AMVs
400hPa
825hPa
975hPa
Thinning:
One datum in a 200 km x 200 km box,
-in 6-hour assimilation window (test 1)
-in every one hour (test 2)
ALL AMVs
QI threshold
HL
ML
LL
IR
95
95
86
-
86
-
-
MTSA
VIS T-1R
WV 96
Results of an experiment in 1-15 July 2007
Threat scores of 3-hour precipitation
forecast against analyzed
precipitationAgainst R／A
0.5
RMSE of wind speed forecasts at
ft=3 against radiosonde observation
inAgainst
Japan Japan
AgainstSonde
R／A Wind
1.00
200
0.98
0.96
400
0.94
Bias
hPaScore
0.92
0.3
0.90
600
0.88
0.2
hPa
Threat Score
0.4
0.86
800
0.84
0.82
0.1
0
5
10
15
20
25
30
mm/3hour
35
40
45
50
0.80
1000
0.78
0
2.8
10
53.0 103.2 15 3.4 20 3.6 25 3.8
30
35
4.0
mm/3hour
RMSE（m/s）
RED: with SATOB AMVs
GREEN: with BUFR AMVs (one datum per six hours)
BLUE: with BUFR AMVs (one datum per one hour)
40
4.2
45
4.4
50
Weather Radars of JMA
PINK Doppler radar used
in the analysis for
MesoScale Model
Sapporo
Kushiro
Hakodate
Akita
Niigata
Matsue
Tanegashima
Okinawa
Ishigaki-jima
Doppler radar
planned to be used
in the analysis for
MesoScale Model
Tokyo
Nagano
Shizuoka
Nagoya
Oosaka
Murotomisaki
Naze
Sendai
Fukui
Hiroshima
Fukuoka
YELLOW
CYAN
Not yet Doppler-ized
Preprocessing of the data
Original data
Averaged data
3D volume scan
(resolution)
-500m (radius)
-0.7deg.(azimuth)
-15 pre-set elevation
angles
(resolution)
-5km (radius)
-5.625 deg.(azimuth)
-15 pre-set elevation
angles
Thinning
&
Quality
control
Thinning (2D or 3D)
All data
2D thinning
Considering only twodimensional data
distribution on a cone
of an elevation angle
Easy to implement but
too dense near the
radar
3D thinning
Considering threedimensional
distribution of all data
20km horizontally
0.5km vertically
Quality Control
Following 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 slowmoving 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)
Threat scores of 3-hour precipitation
Height (hPa)
RMSE of wind speed of six-hour
forecasts against radiosondes
Threshold value (mm/3hour)
RMSE (m/s)
Green: with Doppler velocity of Tokyo radar (w. 3D thinning)
Red: w/o Doppler velocity of Tokyo radar
Impact of different thinning method
Threat scores of 3-hour precipitation
Green: 3D thinning
Red: 2D thinning
Threshold value (mm/3hour)
An example of 3-hour precipitation forecast
Observation
w. Tokyo radar Doppler vel.
(3D thinning)
w/o Tokyo radar Doppler vel.
FT=9
Observation
FT=12
w. Tokyo radar Doppler vel.
(3D thinning)
w/o Tokyo radar Doppler vel.
Ground-based GPS observation
• 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.
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
C
A
B
Model topography
Actual topography
Statistical scores for 3-hour precipitation
(1 to 13 Sep. 2006)
The experiment was performed with the hydrostatic spectral version of
MSM and the same 4D-Var as in the other experiments except for 3hour assimilation window
Positive impact at FT=9 and after
Precipitation is suppressed in early stage
An example of 3-hour precipitation forecast
(FT=6-9 from 00 UTC 6 Sep. 2006)
Observation
with GPS PW
w/o GPS PW
mm
10
Seems good, however …
When an integrated
value is assimilated,
the increment
distribution depends
on the system
Height (km)
2 4 6 8
insufficient(?)
0
Analysis increments of
specific humidity
(for positive departure of
PW)
-6
-4
-2
0
2
4
6
8 (g/kg)
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