short range nwp strategy of jma and research activities at mri kazuo saito meteorological research...
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Short Range NWP Strategy of JMA and Research Activities at MRI
Kazuo SAITO Meteorological Research Institute,
1. Operational mesoscale NWP at JMA
2. Recent developments for operation
3. Near future plans
4. Research activities in MRI
IAMAS2005, 11 August 2005, Beijing
Essential factors in the mesoscale NWP
• Model (Domain, Resolution, Dynamics, Physical processes)
• Initial condition (Analysis method, Data)
• Boundary condition
1m 10m 100m 1km 100km 1000km 10000km10km
second
month
week
day
hour
minute
year
turbulence
front
extra-tropical cyclone
planetary wave
thunder storm
Scale of atmospheric phenomena
micro scale
mesoscale
Macro scale
local wind
heavy rain
typhoon
cumulus conventional aerological observation -300 km, 2/day
conventional NWP model 6x = 100-200 km, 2-4/day
Synoptic forcing
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 RSMRSM •20 km L40, 6480 km x 5120 km, 51 hours , 2 times a day•Hydrostatic spectral model, nested with GSM (60 km L40)
RSM
MSM
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)
04 UTC
1-h Forecast with MSM
OI Analysis +Physical
Initialization
Conventional DataPrecipitation Data
05 UTC 06 UTC03 UTC
3-h Forecast with RSM (20km L40) from 00UTC
Physical Initialization
Precipitation Data
(For Analysis at 06 UTC)
Conventional DataPrecipitation Data
Conventional DataPrecipitation Data
OI Analysis +Physical
Initialization
OI Analysis +Physical
Initialization
1-h Forecast with MSM
1-h Forecast with MSM
18-h Forecast with
MSM
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.
The Meso 4D-Var System(March 2002-)
First guess
analysis
observation
Jo
Jo
Jo
Jo
Jb
21UTC 00UTCAssimilation window
3 hrs
time
observation
observation
observation
Cost function : c
oobb
cob
JyHMxyHMxxxxx
JJJJ
0
1T
0001T
00 R2
1B
2
1
cxob
cxoxbxx
JyHMxx
JJJJ
x
00
1TT00
1
0000
RHMB
Gradient of cost function :
Adjoint model
Model
Penalty term
initial time
Observation parameter
bx0
0x
x
Time integration of NWP model
Concept of Concept of 44 D VarD Var
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 ObservationFT=15-18
3 hour accumulated rain for FT=18 hr Initial 12 UTC 9 September 2001
Ishikawa and Koizumi (2002)
0.25
0.30
0.35
0.40
0.45
0.50
0.55
3 6 9 12 15 180.00
0.05
0.10
0.15
0.20
0.25
0.30
3 6 9 12 15 18
Threat scores (40km verification grid)
June2001
0.25
0.30
0.35
0.40
0.45
0.50
0.55
3 6 9 12 15 180.00
0.05
0.10
0.15
0.20
0.25
0.30
3 6 9 12 15 18
Sep.2001
1mm/3h 10mm/3h
(h) (h)
(h) (h)
Red: 4D-VarBlue: routine
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
Observation (AMEDAS)
Shear line
Location of the observed local shear line near Tokyo is corrected with ACARS data.
WITHACARS
WITHOUTACARS DATA
Assimilation of precipitation and TPW data retrieved from TMI and SSM/I
(October 2003-)
Defense Meteorological Satellite ProgramSpecial Sensor Microwave / Imager
TRMM Microwave Imager
OSE for 00UTC, 25 Aug 2003
TPW by SSM/I and
TMI
With SSM/I and TMI
Without SSM/I and TMI
3 hour rain at FT=18
Observation
Water vapor field was improved
Sato (2003)
0.10
0.12
0.14
0.16
0.18
0.20
0.22
3 6 9 12 15 18
CNTL
TEST
0.28
0.30
0.32
0.34
0.36
0.38
0.40
3 6 9 12 15 18
CNTL
TEST
FT
FT
1m
m/3
hr
10m
m/3
hr
Period 2003 June 3 ~ 16 ( 2weeks 56 forecasts ) 10 km verification grid
Performance of MSM with TMI and SSM/I
Threat score
30 ゚ N
T0207( HALONG)
Observation
QuikSCAT
NASA
Assimilation of QuikSCAT SeaWinds July 2004 -
Threat scores 10km 30mm/3h, 3-19 June 2003
Precipitation FT=8-9. Initial: 12 UTC 18 July 2003
SeaWinds 10UTC 18 July 2003Ohashi (2004)
Non-hydrostatic MSM (JMA-NHM)September 2004-
Developed by joint work between MRI and NPD/JMA
HE-VI, stable computation with LF scheme t=40 secFully compressible, flux form 4th order advection with FCTDirect evaluation of buoyancy from density perturbation3-class bulk microphysics (water vapor, cloud water, rain, cloud ice, snow, graupel)Modified Kain-Fritsch convective parameterization schemeTargeted Moisture DiffusionBox-Lagrangian scheme for rain and graupel
Full paper submitted to M.W.R. (Saito et al., 2005)
Original K-F scheme. FT=12.
Modified K-F scheme. FT=12.
Observed 3 hour accumulated precipitation (mm) at 21 UTC.
Modification of the Kain-Fritsch convective parameterization
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.
MSM NHM R/A
Case Study of Non-hydrostatic MSM
Hydrostatic MSM Radar-AMeDAS observation
Snowfall (13 January 2004, FT=18h)
Heavy rainfall event (18 July 2003, FT=15h)
Non-hydrostatic MSM
Performance of Non-hydrostatic MSM
Five-month total scores over forecast time 03, 06, 09, 12, 15, 18h against 3hourly rain analysis at 20 km grid
NH-MSM
MSM
Five-month total scores at FT=18h against analysis of height
Performance of JMA Mesoscale Model
Bias scores 10km 10mm/3hr
High bias scores in winter were removed by NHM
NHM
Without DPR wind FT=15
With DPR winds FT=15
Observation
Threat Scores for winter10mm/ 3hour
0.05
0.075
0.1
0.125
3 6 9 12 15 18
Forecast time [hour]
Threat Scores for summer
0.125
0.15
0.175
0.2
0.225
3 6 9 12 15 18
Forecast time[hour]
Assimilation of Doppler radar radial winds March 2005-
Koizumi and Ishikawa (2005)
10mm/ 3h 10kmスレットスコア( メッシュ)
0
0.1
0.2
0.3
0.4
0.5
0.620
0103
2001
06
2001
09
2001
12
2002
03
2002
06
2002
09
2002
12
2003
03
2003
06
2003
09
2003
12
2004
03
2004
06
2004
09
2004
12
2005
03
2005
06
2001 0.13)年( 2002 0.17年( ) 2003 0.19年( ) 2004 0.24年( )
Performance of MSM has been improved
0.11
0.17
0.23
4D-VarNHM
Threat scores 10 km, 10mm/3hr for FT=6-9
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)
Boundary conditions for MSM
Major Operational Changes in RSM
• Enhancement of vertical resolution from L36 to L40 (Mar. 2001)
•4D-Var (Jun. 2003)
•Target moisture diffusion (Apr. 2004)
500 hPa Height
Improvement of GSM performance
500 hPa Temperature
3D-Var 4D-Var4D-Var
Cumulus, ATOVS,etc.
Significant improvement by major changes (cumulus, ATOVS, etc.) in May 2003.
Significant improvement by 3D-Var in September 2002.
Improvement in the recent 3 years (2002-2005) exceeds that in 10 years before 2002.
RMSE of 500 hPa Height 1991-20053 years11 years
Performance of GSM in RMSE region
Contributes to RSM forecast through the lateral B.C.
1 Day
2 Day
3D-OI 4D-Var Observation
6 hour accumulated precipitation for FT=6 (upper) and FT=12 (bottom) with RSM. Initial time 00UTC 17 June 2002.
4D-Var in RSM (June 2003-)
Threat Score R/ A 1mm/ 6hr( )
0.30
0.35
0.40
0.45
0.50
6 12 18 24 30 36 42 48Forecast Time
4D-Var
Threat Scores of RSM (Verified with 40km resolution, 1 month for June 2002)
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 conditionHigh 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-)
Radar-AMeDAS obs. 5km Nonhydro. MSM 10km MSM
(18 July 2004 21UTC, FT=6-9)
- 10kmL40 → 5km L50 (Mar. 2006)- 4 times a day → 8 times a day (Mar. 2006)- 33-hr forecast (Mar. 2007)
- 60kmL40 → 20kmL60 (Mar. 2007)- Twice a day → 4 times a day (Mar. 2007)- Supply latest B.C. to MSM directly
60km GSM 20km GSM Radar-AMeDAS 12-h rain
(19 Jun 2001 12UTC, FT=12)
20km (TL959) Global Model (2007-)
5 km L50, 3 hour assimilation windows
Incremental approach using a 10-km version of nonhydrostatic MSM for inner loop
Nonhyd r ostatic 4D-Var (2008-)
UL: Radar-AMeDAS 3-h rainUR: 12 hr forecast Meso 4DVarLL: 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
JMA AWSAMeDas・: 4-elements・: Rain gauge
Assimilation of GPS TPW data
AMeDAS (JMA) GPS Earth Observation Network(Geographical Survey Institute)
Assimilation of GPS TPW data
w/o GPS with GPS wsfc (with GPS) - wsfc (w/o GPS)
Analysis of TPW
Heavy rain event 30 June 2004
w/o GPS
with GPS
Observed heavy rain is predicted by assimilation of GPS TPW data. Shoji et al. (2005)
Impact of GPS TPW data
Assimilation of GPS occultation data
CHAMP/ISDC (GFZ) : Challenging Mini-Satellite Payload for Geoscientific Research and ApplicationInformation System and Data Center
GPS
CHAMP
occultation observation
Assimilation period 00-06 UTC 16 July 2004
grey :1st guess
black ;observation
Heig
ht
(k
m)
Reflection ×106
CNTL
Radar AMeDAS 09-12UTC
Impact of CHAMP
The CHAMP occultation data moisten the lower atmosphere and yield observed precipitation in MSM.
CNTL+CHAMP
FT=6
Initial 06UTC 16 July 2004
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
03UTCObservation 00UTC 13 July 2004
Routine hydrostatic MSM prediction from 12UTC 12 July 2004
06UTC
FT=12 FT=15 FT=18
Downscale experiment of weekly ensemble prediction Initial 12 UTC 12 July 2004 T106 Global EPS
CONTROL
Member M03p
RA
control
0
2
4
6
8
10
12
14
16
18
20
0 10 20 30 40 50
コントロール
01p-12p
01m-12m
RA
M07m
M03p
RA
0
20
40
60
80
100
120
140
160
180
200
0 20 40 60 80 100 120 140 160 180 200
コントロール
01p-12p
01m-12m
RA
Control
Precipitation in a rectangle over northern Japan 400×250km by Global EPS
FT=00-06
FT=
12-18
Mean precipitation extreme value
Only very weak rain in GSM
M03p
10 km MSM downscale experiment of EPS
10kmNHM Control
Member 'M03p'
FT=06 FT=18
RA
MARF
Control
0
2
4
6
8
10
12
14
16
18
20
0 10 20 30 40 50
コントロール01p-12p01m-12mm00m03p, m03pm00RAMARF
M03p
FT=00-06
FT=
12-18
Mean precipitation extreme value
RA
MARF
Control
0
20
40
60
80
100
120
140
160
180
200
0 20 40 60 80 100 120 140 160 180 200
コントロール01p-12p01m-12mm00m03p, m03pm00RAMARF
M03p
M07mM07m
Precipitation in a rectangle over northern Japan 400×250km by 10 km MSM downscale experiment of EPS
Location of precipitation is adjusted to south and line-shaped intense rain is reproduced
Downscaling experiment of the global EPS with MSM
FT=12 FT=15 FT=18
Observation 00UTC 13 July 2004 03UTC 06UTC
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.