Transcript 2.3-20111116-2_Japanx

National Forecasting System
(MOVE/MRI.COM)
and Japan Working Team
- Progress in 2010-2011 Masa Kamachi (Japan Met. Agency/ Met. Res. Inst.)
Toshiyuki Awaji (Kyoto Univ., JAMSTEC/DrC)
& Hajime Nishimura (JAMSTEC/DrC)
1
Contents
1. Japan Working Team of GOV (JWoT.GOV)
Outreach
2. Japan National System (JMA & MRI )
MOVE/MRI.COM system (Ocean-Wea., S-I)
Recent developments (and Future Plan):
3. JAMSTEC/DrC
New Center and New Projects
2
Contents
1. Japan Working Team of GOV (JWoT.GOV)
Outreach: (Japan) Ocean Data Assimilation Summer School
from 1997-
3
Japan Working Team of GOV
(JWoT.GOV)
ver. 2011/11/13
Group
Kyoto Univ.
& Jpn. Mar.
Sci.
Foundation
JAMSTEC
(DrC:
Data Res.
Center)
& Kyoto Univ.
JAMSTEC
(Appl. Lab.)
& Tokyo Univ.
Kyushu Univ.
(RIAM)
Japan
Fisheries
Agency
& Fisheries
Research
Institute
JMA & MRI
JMA & MRI
System’s
name
KU-JMSF
K-7 system
J-COPE2
RIAMOM
ROMS-FRA
JADE
MOVE/MRI.COM-WNP
MOVE/MRI.COM-G
(JMA - operation
MRI - research.)
(JMA- operation
MRI - research)
3DVAR/4DVAR
3DVAR
method
4DVAR
Coupled 4DVAR
3DVAR
EnKF
Kalman Filter
Kalman Filter,
3DVAR
Aim
Ocean Weather
Coastal prediction
Climate
Pacific-reanalysis
(1993-2004)
Model improvement.
90’s ElNino
Ocean Weather
Variability &
Ppredictability
of Kuroshio
Ocean Weather
Japan Sea
Predictability
Oil spill
Jelly fish
Ocean Weather
in the Western
North Pacific
Predictability
Jelly fish
Ocean Weather
(Kuroshio, Oyashio),
Forecasting of ocean state,
oil spill, sea ice
Reanalysis (1985-2010)
Disaster prevention (Surges)
Climate
El Nino variability
Initial Condition for CGCM
for El Nino &
seasonal forecating
Reanalysis (1949-2010)
On-going
Developm
ent
Coastal process
Environmental
Pollution detection
(JAEA)
Biogeochemical &
ecosystem
FukushimaNPP
polution
Marine Debris
(JAEA)
Coupled assimilation
and Dec-prediction
Environmental
Pollution detection
(JAEA)
Biogeochemical &
ecosystem
FukushimaNPP
polution
Marine Debris (JAEA)
Coastal process
Wind-wave
interaction
Tide
FukushimaNPP
polution (MEXT)
Regional Air-sea
interaction
Coastal
phenomena
Biogeochemical
process
Ecosystem
Coastal prediction of
physical and
ecological fields
OSE/sensitivity/SV
Sea-ice prediction
Wind-wave interaction
4DVAR
Coastal disast. prevention
(MOVE-Jpn)
Tide assimilation
Marine debris (JAEA)
OSE/OSSE/sensitivity
Seasonal forecast
Coupled assimilation
(MOVE-C)
4
15th Japan Data Assimilation Summer School
It continues from 1997 under the support of Japan
Marine Science Foundation & JAMSTEC
3 days course, 56 participants
From fundamental lectures to applications
1.
2.
3.
4.
Methods (VAR, EnKF, Particle Filter)
Operational systems
Reanalyses
Other applications
(Fisheries, Flood control, Aerodynamics)
5
Contents
2. JMA & MRI
Japan National System:
MOVE/MRI.COM system (Ocean-Wea., S-I)
Recent developments (and Future Plan):
6
JMA-MRI Ocean Data Assimilation System:
MOVE/MRI.COM
MRI continues to develop ocean data assimilation systems (MOVE/MRI.COM:
Multivariate Ocean Variational Estimation).
Aims
1.
Optimum Initial Conditions for operational forecasting in JMA/HQ
Ocean Climate: Seasonal - Interannual (ElNino) prediction
Ocean Weather: Ocean state estimstion & prediction around Japan
2.
Analysis-reanalysis (3 types) for understanding climate variability:
Western North Pacific : 1985-2010+ (0.1deg)
North Pacific : 1948-2010+ (0.5deg)
Global : 1948-2010+ (1.0deg)
Reanalysis dataset is opened through JMA NEAR-GOOS database
3.
4.
5.
Observing System Experiment for evaluating observing systems
(OSE, OSSE, SV analyses with 4DVAR-adjoint system)
Coupled atmosphere-ocean data assimilation (CDAS) for S-I prediction
Coastal application for disaster prevention
(search & rescue, oil spill, high tide, wind wave)
7
JMA-MRI Ocean Data Assimilation System:
MOVE/MRI.COM
MRI MOVE/MRI.COM (Multivariate Ocean Variational Estimation) system
OGCM: MRI.COM (MRI Community Ocean Model) (similar to MOM)
Method: Multivariate 3D-VAR
with vertical coupled T-S Empirical Orthogonal Function (EOF) modal
decomposition with area partition (control variable: amp. of EOF mode)
horizontal Gaussian function (inhomogeneous decorrelation scales)
nonlinear constraints (dynamic QC, density inversion)
bias
correction
Source Data:
Satellite Altimetry (TOPEX/POSEIDON, ERS, ENIVISAT, Jason)
SST (COBESST or MGDSST(Jpn-GHRSST) ) with sea ice concentration
in situ T & S (GTSPP, ARGO, Tao/Triton, drifter)
(satellite SSS (Aquarius & SMOS) )
with QC in each data centers
Atmospheric forcing (CORE, NCEP-R1&R2, ERA40, JRA25 and operational, JRA55)
4DVAR, Quasi-Coupled GCM 3DVAR, coupled breeding
8
MOVE systems
Regional
JMA-MRI
MOVE-WNP
Global
MRI
MOVE-G
Regional
JMA-MRI
MOVE-NP
Global Model-1 :
(1×1 deg.: 1/3°tropical region,
54 Layers)
Nested-1 N-Pac Model：
15S-65N, 100E-75W
( 0.5×0.5 deg., 54 Layers)
Nested-2 Kuroshio Model：
15N-65N, 115E-160W
(0.1×0.1 deg., 54 Layers)
Nested-3 Coastal Model:
2km mesh, 54 layers
9
Recent developments (and Future Plan):
Tripolar (full) Global System (IPCC_AR5, S-I)
Reanalysis & Reforecasting,
Non-Gaussian type constraints
Sea ice concentration assimilation (EVP, 5-category)
OSE for S-I prediction
Coastal disaster prevention (2km resolution)
and incremental 4DVAR
Sea ice assimilation in the arctic ocean
Prediction of marine debris from Tohoku
(wave-current interaction)
(tide assimilation)
10
Assimilation of sea ice concentration
nudging of MGDSST (Jpn GHRSST)
simulation: no
ice
case study (2004 1/25)
Forecast (CNTL)
Ice in
Abashiri
Forecast (MOVE)
Observation
New MOVE-G
Tripolar coordinate,
Assimilation of sea ice
concentration (JMA
product)
Arctic Ocean
Time series of the area in
the arctic ocean:
Seasonal, interannual OK
Winter: over-estimation at
the ice edge.
Summer: under-estimation
at the central area, overestimation at the edge
TS assim corrects edge
(not enough).
obs
model
TS assim
ice-assim
Toyoda et al., (2011)
Inter-annual variability of the anomaly
Model recovers well (2003/9)
(Even TS-assim has better results
(RMSE blue→green) )
Toyoda et al., (2011)
Motivation
・We estimate the impact of Argo on accuracy of data assimilation fields quantitatively, with a new
strategy.
Observing System Experiment (OSE)
Assimilation System
MOVE/MRI.COM-NP
・Area: North Pacific (15°S~65°N, 100°E~75°W)
・Grid: 0.5°x0.5°, L54
・OGCM: MRI.COM (MRI community ocean model); z-s coordinate MOM type
・Analysis scheme: 3DVAR with vertical coupled TS-EOF modes
・Model insertion scheme: Incremental analysis updates (IAU)
・Observation data for assimilation:
- In situ (T, S): Argo, CTD, XBT, Buoy
- Satellite: SST (MGDSST/Japan GHRSST)
SSH (TOPEX/Poseidon, Jason-1/2, ERS-2, Envisat)
Ogawa et al., (2011)
Experimental Setup
Observing System Experiment
・Change the number of Argo data. Calculate the following 9 jobs.
・Experimental period: 2000 ~ 2009 (10 years)
・Assimilation cycle: 5 days
Argo data
Reference Argo
(20% of whole Argo)
Argo 100%
Other observations
1
Argo 80%
Other observations
2
Argo 60%
Other observations
3
Argo 40%
Other observations
4
Argo 20%
Other observations
5
Argo 0%
Other observations
6
Assimilation Results
(5 cases)
Independent of Reference Argo
Argo not assimilated
Same Reference Argo
Assimilation Result
Argo 80%
Other observations
7
Assimilation Result
Argo 100%
Other observations
8
Assimilation Result
other observations not assimilated
N
1
RMSE 
N
 (x
y
assim
x%
ref
i
)
2
i
assim
xx%
: assimilation result of T or S
calculated by:
1
~
,7
9
yiref : data of reference Argo
: number of reference Argo
T
Depth [m]
N
S
Complementary
effect of Argo
Correction by
Altimetry etc.
Correction by
Argo ONLY
Whole North Pacific
[°C]
Ogawa et al., (2011)
Result 3 (Improvement Rate)
Estimate Improvement Rates defined as
Improvemen
t Rate[%]
Improvement Rate [%]
RMSE(0%)
 RMSE(x%)
RMSE(0%) 6
6
The results show mean values in a
subsurface layer
(upper than 500m depth)
Results
・The impact on S is larger
than that on T, in particular
in the Western Subtropics.
2
~
5
Whole North Pacific
Subarctic
Kuroshio Extension
Western Subtropics
Impacts are not saturated, especially
in the Kuroshio Extension region (T & S)
and Western Subtropics (S).
・ The impact still increase
from 60% to 80% of ARGO.
Improvement rate
[%]
・Suggestion: the more Argo,
the better improvement.
10
%
Ogawa et al., (2011)
S
T
Argo 20 40
60 80%
100
Argo 20 40
60 80%
10
%
Impacts on SST Indices
See Peter Oke’s Report
Improvement of ACCs by assimilating Buoys or Floats
Fujii et al.(2011)
LT: Lead Time
Summary
TAO/TRITON Array
・ Remarkable positive impact on NINO3 and NINO4 areas for 0-6 month
SST forecast.
・ The Impact is not clear on western tropical Pacific and for 7-12 month
forecast.
Argo Floats
・Positive impact on NINO3, NINO4, western tropical Pacific, and Western
Indian Ocean for 0-6 month forecast.
・The positive impact remains for 7-12 month forecasts.
Satellite Altimetry
・Negative impact on the central equatorial Pacific for 0-6 month forecast.
・ This negative impact may be caused by the ignorant of the increase of
the fresh water mass.
Now developing a new scheme.
Development of MOVE-4DVAR
Use of 3DVAR B matrix (TS-EOF)
Incremental 4DVAR: Pred.(2km model)+Anal.(10km, TLM, adjoint)
=>2014 oper. demonstration
2000/1/29
3DVAR
Obs (SST)
4DVAR
Coastal-Shelf Sea model MRI.COM_Jpn (2km)

Region
 127E-167E, 25N-50N (for
research, smaller for operation)

21
JTOPO30 (around Japan) and other
area GEBCO30
Kuroshio

2/9 – 5/10, daily SST
 Warm water intrusion happens when
Kuroshio flows near the coast
Tide

Good reproduction of time evolution of tide
 Pattern, max-value: ok
Instantaneous tide field (2001/6/20/0：00)
No-assimilation (new model)
22
Assimilated sat SSH
(Matsumoto et al. 2000)
future issues
Satellite SSS assimilation
(Aquarius, SMOS)
Coastal prediction by 4DVAR
(coupling wind wave and OGCM
for coastal disaster prevention)
Wave-current interaction
High Resolution coupled system
(Typhoon-NHM +wind wave
+OGCM)
NEMURO (ecosystem)
assimilation (parameter est.)
Typhoon 23, in Aug 30, 2004
23
Contents
1. Japan Working Group of GOV (JWG.GOV)
2. JMA/MRI
MOVE/MRI.COM system,
Recent developments: Reanalysis,
prediction of oil spill (near operation)
sea ice assimilation
future system for coastal disaster prevention
3. JAMSTEC
New Research and Data Center (DrC)
24
DIAS: Japan GEOSS project (Tokyo U., JAXA, JAMSTEC)
Visualization
On-demand Ocean View System
by a multi-level virtual ocean
development based on 4DVAR
ODA and CDA systems on ES
For practical use
of value-added
datasets toward
scientific and
societal benefits
New National Project: Initiative of Adaptation for Climate Change
Experiments toward Operational Prediction
of Ocean Environment and Fisheries
Resources under Climate Variability
Toshiyuki Awaji
(JAMSTEC)
Target Areas: Aomori Prefecture
Aomori Prefecture Fisheries Institute
Fisheries Research Agency (FRA)
JMA/MRI
Hokkaido Univ.
Kyoto Univ.
Aomori Prefecture
Environment Simulation Institute
2015年7月20日
26
Coupled Atmos-Ocean-Ecosyst
Data Assimilation/Prediction
System & Statistical Multivariate
Prediction System for Fisheries
Resources
Ecosystem model: NEMURO
SeaWiFS chl-a
2015年7月20日
Ocean Reanalysis
Coupling
JAMSTEC/K7 4D-VAR
Atmos-Ocean Coupled
System
Model result
Chl-a in the LaNina period （1999/10）
27
End
28