Transcript IAMAS2005, 11 August 2005, Beijing Short Range NWP Strategy of JMA and Research Activities at MRI Kazuo SAITO Meteorological Research Institute, [email protected] 1.

IAMAS2005, 11 August 2005, Beijing
Short Range NWP Strategy of JMA and
Research Activities at MRI
Kazuo SAITO
Meteorological Research Institute,
[email protected]
1. Operational mesoscale NWP at JMA
2. Recent developments for operation
3. Near future plans
4. Research activities in MRI
Essential factors in the mesoscale NWP
• Model (Domain, Resolution, Dynamics,
Physical processes)
• Initial condition (Analysis method, Data)
• Boundary condition
Scale of atmospheric phenomena
year
Synoptic forcing
planetary
wave
extra-tropical
cyclone
typhoon
front
month
week
day
mesoscale
micro scale
hour
heavy
thunder rain
storm
cumulus
local
turbulence
wind
minute
Macro scale
conventional aerological
observation -300 km, 2/day
conventional NWP model
6Dx = 100-200 km, 2-4/day
second
1m
10m
100m
1km
10km
100km 1000km 10000km
Mesoscale NWP at JMA (March 2001-)
MSM
•10 km L40, 3600 km x 2880 km, 18 hours forecast, 4 times a day
• Hydrostatic spectral model (March 2001-August 2004)
• Nonhydrostatic (September 2004-)
nested with RSM
RSM
•20 km L40, 6480 km x 5120 km, 51 hours , 2 times a day
•Hydrostatic spectral model, nested with GSM (60 km L40)
MSM
RSM
Performance of JMA Mesoscale Model
Threat scores 40km 10mm/6hr
Threat scores 10km 10mm/3hr
Performance of MSM has been improving for both weak and
moderate rains
2. Recent developments for
operational meso NWP
•Start of Mesoscale NWP (Mar. 2001)
•Wind profiler data (Jun. 2001)
•4D-Var in MSM (Mar. 2002)
•Domestic ACARS data (Aug. 2002)
•4D-Var in RSM (Jun. 2003)
•SSM/I precipitable amount (Oct. 2003)
•QuikSCAT Seawinds (Jul. 2004)
•Nonhydrostatic model (Sep. 2004)
•Doppler radar radial winds ( Mar. 2005)
Wind Profiler Network of JMA
JMA deployed 25 wind
profilers in 2001, and
their data have been
assimilated since June
2001.
Wind profilers measure
the low level winds up
to 5 km with a vertical
resolution of 300m .
Currently, 31 wind
profilers measure wind
successively in addition
to the 18 aerological
sondes.
Initial Assimilation System for MSM
(March 2001-March 2002)
03 UTC
04 UTC
05 UTC
06 UTC
3-h Forecast with
RSM (20km L40)
from 00UTC
1-h Forecast
with MSM
1-h Forecast
with MSM
1-h Forecast
with MSM
18-h Forecast
with MSM
Physical
Initialization
Precipitation Data
OI Analysis +
Physical
Initialization
OI Analysis +
Physical
Initialization
OI Analysis +
Physical
Initialization
Conventional Data
Precipitation Data
Conventional Data
Precipitation Data
Conventional Data
Precipitation Data
(For Analysis at 06 UTC)
The Meso 4D-Var System
(March 2002-)

2 x 3 hour assimilation windows.

Incremental approach using a 20-km version of
MSM for inner loop.
Inner forward
: nonlinear full-physics model
Inner backward : reduced-physics adjoint model
(grid-scale condensation, moist convective adjustment,
vertical diffusion, simplified radiation)

Precipitation analysis by radar and AMeDAS
observation are assimilated.

Boundary condition in assimilation window is
controlled.
Concept of ４D Var
Cost
function
:
J  Jb  Jo  Jc

Gradient of
cost
function :

Model


 

 x0 J   x0 Jb   x0 J o   x0 J c



Penalty term

 B1 x0  x0  M T H T R 1 HMx0  y o   x 0 J c
Observation x
parameter
b
observation
Jo
Adjoint model
initial time
Jo
First guess
observation
x0b
x0
Jo
Jb
observation
Time integration
of NWP model
analysis
Jo
observation
21UTC
Assimilation window
３hrs


T
T
1
1
x0  x0b B1 x0  x0b  HMx 0  y o R 1 HMx 0  y o  J c
2
2
00UTC
time
Radar-AMeDAS Precipitation Analysis
•Hourly precipitation amount data with 2.5km resolution.
•Radar-observed precipitation intensity is accumulated,
calibrated with 1,300 AMeDAS rain-gauges.
•More than 3,000 rain-gauges (not from JMA) added in 2003.
・：4-elements
・：Rain gauge
4D-Var in MSM
RUC with OI
4D-Var
Observation
FT=15-18
3 hour accumulated rain for FT=18 hr
Initial 12 UTC 9 September 2001
Ishikawa and Koizumi (2002)
Threat scores
(40km verification grid)
1mm/3h
0.55
10mm/3h
0.30
0.50
0.25
June 0.45
2001 0.40
0.20
0.35
0.10
0.30
0.05
0.15
0.25
3
Sep.
2001
6
9
12
15
18
(h)
0.00
3
0.55
0.30
0.50
0.25
0.45
0.20
0.40
0.15
0.35
0.10
0.30
0.05
6
9
12
15
18 (h)
Red: 4D-Var
Blue: routine
0.00
0.25
3
6
9
12
15
18
(h)
3
6
9
12
15
18
(h)
Domestic ACARS Data
(August 2002-)
Domestic ACARS data
from the Japan Air Line
have been assimilated in
addition to the
conventional AIREP and
AMDAR data.
The ANA data have been
added since September
2003.
More than 10,000 reports
per day.
Impact of ACARS Data
WITHOUT
ACARS DATA
WITH
ACARS
Observation (AMEDAS)
Shear line
Location of the observed local shear line near
Tokyo is corrected with ACARS data.
Assimilation of precipitation and TPW
data retrieved from TMI and SSM/I
(October 2003-)
Defense Meteorological Satellite Program
Special Sensor Microwave / Imager
TRMM Microwave Imager
OSE for 00UTC, 25 Aug 2003
Water vapor field was improved
Without SSM/I and TMI
TPW by SSM/I
and TMI
3 hour rain at FT=18
Observation
With SSM/I and TMI
Sato (2003)
Performance of MSM with TMI and SSM/I
Period 2003 June 3～16 （2weeks 56 forecasts）10 km verification grid
Threat score
0.40
CNTL
1mm/3hr
0.38
TEST
0.36
0.34
0.32
0.30
0.28
3
6
9
FT
12
15
18
10mm/3hr
0.22
CNTL
0.20
TEST
0.18
0.16
0.14
0.12
0.10
3
6
9
FT
12
15
18
Assimilation of QuikSCAT SeaWinds
July 2004 -
QuikSCAT
Observation
30ﾟN
NASA
T0207
（HALONG）
Precipitation FT=8-9. Initial: 12 UTC 18 July 2003
Threat scores 10km 30mm/3h, 3-19 June 2003
SeaWinds 10UTC 18 July 2003
Ohashi (2004)
Non-hydrostatic MSM (JMA-NHM)
September 2004Developed by joint work between MRI and NPD/JMA

HE-VI, stable computation with LF scheme Dt=40 sec
th order advection with FCT
Fully compressible, flux form 4
Direct evaluation of buoyancy from density perturbation
3-class bulk microphysics (water vapor, cloud water, rain,
cloud ice, snow, graupel)
Modified Kain-Fritsch convective parameterization scheme
Targeted Moisture Diffusion
Box-Lagrangian scheme for rain and graupel

Full paper submitted to M.W.R. (Saito et al., 2005)

Modification of the Kain-Fritsch convective parameterization
Observed 3 hour accumulated
precipitation (mm) at 21 UTC.
Original K-F scheme. FT=12.
Several points (updraft property,
trigger function, closure assumption) in
the K-F scheme have been modified to
prevent unnatural orographic rainfall and
excessive stabilization .
Submitted to MWR.
Modified K-F scheme. FT=12.
Case Study of Non-hydrostatic MSM
Heavy rainfall event (18 July 2003, FT=15h)
MSM
NHM
R/A
Snowfall (13 January 2004, FT=18h)
MSM
Hydrostatic MSM
NHM
Non-hydrostatic MSM
R/A
Radar-AMeDAS
observation
Performance of Non-hydrostatic MSM
MSM
NH-MSM
Five-month total scores over forecast time 03, 06, 09, 12, 15, 18h
against 3hourly rain analysis at 20 km grid
Five-month total scores at FT=18h against analysis of height
Performance of JMA Mesoscale Model
Bias scores 10km 10mm/3hr
NHM
High bias scores in winter were removed by NHM
Assimilation of Doppler radar radial winds
March 2005Without DPR wind FT=15
Observation
With DPR winds FT=15
Threat Scores for summer
Threat Scores for winter
10mm/3hour
0.225
0.125
0.2
0.1
0.175
0.075
0.15
0.05
0.125
3
6
9
12
Forecast time [hour]
15
18
3
6
9
12
15
18
Forecast time[hour]
Koizumi and Ishikawa (2005)
Performance of MSM has been improved
Threat
scores 10 km, 10mm/3hr
for FT=6-9
スレットスコア（10mm/3h
10kmメッシュ）
0.6
2001年（0.13)
2002年（0.17）
2003年（0.19）
2004年（0.24）
0.5
0.4
0.3
0.23
0.2
0.17
0.1
0.11
NHM
4D-Var
200506
200503
200412
200409
200406
200403
200312
200309
200306
200303
200212
200209
200206
200203
200112
200109
200106
200103
0
Boundary conditions for MSM
Major Operational Changes in GSM
•Enhancement of vertical resolution from L36 to L40 (Mar. 2001)
•3D-Var (Sep. 2001)
•QuikSCAT Seawinds, ATOVS radiances (May 2003)
•Modification of the cumulus parameterization (May 2003, Jul. 2004)
•MODIS Arctic wind data (May 2004, Sep. 2004)
•4D-Var (Feb. 2005)
•Semi-Lagrangian scheme (TL319; Feb. 2005)
Major Operational Changes in RSM
• Enhancement of vertical resolution from L36 to L40 (Mar. 2001)
•4D-Var (Jun. 2003)
•Target moisture diffusion (Apr. 2004)
Improvement of GSM performance
500 hPa Height
Cumulus,
ATOVS,etc.
500 hPa Temperature
4D-Var
Significant improvement by major
changes (cumulus, ATOVS, etc.) in
May 2003.
3D-Var
4D-Var
Significant improvement by 3D-Var
in September 2002.
RMSE of 500 hPa Height 1991-2005
11 years
3 years
Improvement in the recent 3 years (2002-2005) exceeds that
in 10 years before 2002.
Performance of GSM in RMSE region
2 Day
1 Day
Contributes to RSM forecast through the lateral B.C.
4D-Var in RSM (June 2003-)
3D-OI
4D-Var
6 hour accumulated precipitation for FT=6 (upper) and
FT=12 (bottom) with RSM. Initial time 00UTC 17 June 2002.
Observation
Threat Scores of RSM
(Verified with 40km resolution, 1 month for June 2002)
Threat Score（R /A 1mm/6hr）
0.50
0.45
0.40
4D-Var
0.35
0.30
6 12 18 24 30 36 42 48
Forecas t Ti me
Performance of RSM improved
4D-Var
Time series of RMSE for 500 hPa field
Contribute to MSM forecast through the lateral B.C.
3. Near Future Plans for 2006-2008
• Model
High resolution MSM (5 km L50) (Mar. 2006-)
- execute 8 times / day
• Boundary condition
High resolution GSM (TL959=20km L60) (2007-)
- execute 4 times / day
• Initial condition
Non-hydrostatic 4D-Var (JNoVA) (2008-)
- 3 hour assimilation window execute 8 times / day,
inner 10 km
5 km Nonhydrostatic MSM
(2006-)
- 10kmL40 → 5km L50 (Mar. 2006)
- 4 times a day → 8 times a day (Mar. 2006)
- 33-hr forecast (Mar. 2007)
Radar-AMeDAS obs.
5km Nonhydro. MSM
10km MSM
(18 July 2004 21UTC, FT=6-9)
20km (TL959) Global Model
(2007-)
- 60kmL40 → 20kmL60 (Mar. 2007)
- Twice a day → 4 times a day (Mar. 2007)
- Supply latest B.C. to MSM directly
(19 Jun 2001 12UTC, FT=12)
60km GSM
20km GSM
Radar-AMeDAS 12-h rain
Nonhydｒostatic 4D-Var (2008-)

5 km L50, 3 hour assimilation windows

Incremental approach using a 10-km version of
nonhydrostatic MSM for inner loop
UL: Radar-AMeDAS 3-h rain
UR: 12 hr forecast Meso 4DVar
LL: Nonhydrostatic 4D-Var
Initial time 12 UTC 17, July 2004
Honda et al. (2005)
4. Research activities at MRI
• Model
- Cloud resolving NWP model
• Initial condition
- GPS data, Direct assimilation of satellite data
- Cloud resolving 4D-Var
• Boundary condition
- Global nonhydrostatic model
• Meso-ensemble
Assimilation of GPS TPW data
JMA AWS
AMeDas
・：4-elements
・：Rain gauge
AMeDAS (JMA)
GPS Earth Observation Network
(Geographical Survey Institute)
Assimilation of GPS TPW data
Heavy rain event 30 June 2004
Analysis of TPW
w/o GPS
with GPS
wsfc
(with GPS)
- wsfc
(w/o GPS)
Impact of GPS TPW data
w/o GPS
with GPS
Shoji et al. (2005)
Observed heavy
rain is predicted
by assimilation of
GPS TPW data.
Assimilation of GPS occultation data
CHAMP/ISDC (GFZ) :
Height (ｋ
m)
Challenging Mini-Satellite Payload
for Geoscientific Research and Application
Information System and Data Center
grey：１ｓｔ guess
black；observation
occultation observation
Reflection
×106
Assimilation period
00-06 UTC 16 July 2004
GPS
CHAMP
Impact of CHAMP
CNTL
Radar AMeDAS 09-12UTC
CNTL+CHAMP
FT=6
Initial 06UTC 16 July 2004
The CHAMP occultation data moisten the lower
atmosphere and yield observed precipitation in MSM.
Seko et al. (2005)
Further activities MRI/JMA
• Asian THORPEX
• WWRP Beijing Olympic 2008 Forecast
Demonstration Program /Research and
Development Program
- participate in MEP component
Meso ensemble experiment for Niigata heavy rain in July 2004
Observation 00UTC 13 July 2004
FT=12
FT=15
03UTC
06UTC
FT=18
Routine hydrostatic MSM prediction from 12UTC 12 July 2004
Downscale experiment of weekly ensemble prediction
Initial 12 UTC 12 July 2004 T106 Global EPS
CONTROL
Member M03p
Precipitation in a rectangle over northern Japan
400×250km by Global EPS
20
200
18
コントロール
180
01p-12p
01p-12p
01m-12m
01m-12m
160
RA
16
FT=
-
12
18
コントロール
RA
14
140
12
120
10
100
M03p
8
80
RA
6
RA
60
M07m
40
4
control
M03p
20
2
Control
0
0
0
10
20
30
Mean precipitation
40
50
0
FT=00-06
20
40
60
80
100
120
140
160
180
200
extreme value
Only very weak rain in GSM
10 km MSM downscale experiment of EPS
10kmNHM Control
Member 'M03p'
FT=06
FT=18
Precipitation in a rectangle over northern Japan
400×250km by 10 km MSM downscale experiment of EPS
20
200
コントロール
01p-12p180
01m-12m
m00m03p, m03pm00
160
RA
MARF
18
16
FT=
-
12
18
14
140
12
120
10
100
コントロール
01p-12p
01m-12m
m00m03p, m03pm00
RA
MARF
RA
M03p
8
80
RA
M03p
MARF
6
60
M07m
M07m
4
MARF
40
2
Control
20
Control
0
0
0
10
20
30
Mean precipitation
40
50
0
FT=00-06
20
40
60
80
100
120
140
160
180
200
extreme value
Downscaling experiment of the global EPS with MSM
Observation 00UTC 13 July 2004
FT=12
03UTC
FT=15
06UTC
FT=18
Location of precipitation is adjusted to south and line-shaped intense rain is reproduced
Summary
•JMA Mesoscale NWP started 2001. Several factors (model, initial
and lateral boundary conditions) have been modified, and the
performance has improved.
•Data assimilation of mesoscale data using variational method is the
key factor.
•Significant improvement of GSM and RSM also contributed to
MSM through the LBC.
•Further updates are scheduled in the operational system by 2008.
• Research and developments are underway to realize dynamical
prediction of heavy rain.
•Mesoscale NWP is now entering a new stage.