Yosuke Fujii1, Norihisa Usui1, Takahiro Toyoda1,

Nariaki Hirose1, Hiromichi Igarashi2,

and Japan GODAE group

(1JMA/ Meteorological Research Institute,2JAMSTEC)

GODAE Ocean View Activities in

JMA (and Japan)

GOVST VIII, Nov. 6th, 2017, Bergen, Norway

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Targets of ODA Systems in JMA

Ocean state monitoring and predictions• For preventing natural disasters (e.g., high tides, rapid tides)• For search and rescue.• For predicting drift/advection of debris, oil spill, pollutant etc.• For marine transportation and marine construction• For fishery and sustaining marine eco-system• For monitoring and understanding of climate variations

Providing oceanic initial condition of the coupled model for subseasonal-seasonal-decadal predictions• Currently, only seasonal predictions are conducted with the coupled model• Coupled data assimilation will be required for seamless predictions

Providing sea surface conditions of atmospheric models for weather predictions• But observation-based analysis is used in weather predictions, currently• Reproduction of the diurnal cycle of SST will be required for improvement of

atmospheric predictions.

JMA loosely collaborates with other Japanese agencies (e.g., JAMSTEC, JAXA) and universities.

Operational Systems in JMA

MOVE/MRI.COM-G2

MOVE/MRI.COM-WNP*/SetoMOVE/MRI.COM-NP/WNP

3DVAR

Kuroshio/Oyashio Monitoring

Ocean Forecasting

(1 week-1 month)

NP(0.5º×0.5º)

WNP(0.1º×0.1º)

WNP* (4DVAR)

(0.1º×0.1º)

Seto (2km×2km)

Initialized with

WNP* through

IAU-DST (IAU as

a Down-Scaling

Technique).

Monitoring of Coastal Ocean and

abnormal tides

Ocean Forecasting (-1 week)

G2 (0.5º×1º, Tripolar Grid)

Tripolar Grid, 1º×0.5º

3DVAR-FGAT+ Bias Correction

Seasonal and ENSO predictions

(-7months by CGCM)

(semi-operation)

9/7 Initial

10/10 Initial

T200 in WNP* (4DVAR) T200 in WNP (3DVAR)

9/28 Initial

Actual Status (10/16, T200)

2017 Kuroshio large meander (Prediction for 10/16)

Predictions from 3DVAR tend to overestimate development and eastward advection of the meander, but predictions from 4DVAR adequately represent the large meander.

The prediction is successful even the lead time is longer than 1 month.

cloud server

Japanese commercial

fishing vessels

satellite

web site

HSI mapping

MOVE/MRI.COM-NP/WNP

HSI map 200m-d temperature

・100-400m temperature・SST,SSH, EKE,chl-a・squid HSI map

Web Delivery for squid fishery (with JAMSTEC)

Since 2015

4DVAR Ocean Reanalysis for the western North Pacific over 30 years

(FORA-WNP30)

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Usui et al. 2016, doi: 10.1007/s10872-016-0398-5

Ocean Reanalysis generated by MOVE/MRI.COM-WNP*.

Period: 1982-2014

Assimilated data: in situ TS profiles (0-1500m), JMA’s

gridded SST, and Satellite Sea Level Anomaly

Available for free (see http://synthesis.jamstec.go.jp/FORA/)

Satellite MODIS FORA-WNP30

From the web page of EORC:http://www.eorc.jaxa.jp/imgdata/topics/2005/tp050408.html

SST at Mar. 7th, 2005

FORA-WNP30 reproduces the narrow southward intrusion of Oyashio to 37.2ºN observed by MODIS.

Kuroshio extension is also well reproduced in FORA-WNP30.

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4DVAR model (10km)

Japan-area model (2km)

Systems currently developed in JMA/MRI

MOVE/MRI.COM-NP/JPNNorth Pacific 4DVAR System (NP*)• 10km resolution (eddy-resolving) Japan-area high resolution model (JPN)• 2km resolution (resolving coastal phenomena)• Initialized with NP 4DVAR analysis fields by IAU-DST

(IAU as a Down-Scaling Technique)

MOVE/MRI.COM-G3Global 4DVAR System (G3A)• Global, Tripolar grids, Resolution: 1º×0.3-0.5º

• Sea Ice 3DVAR (Sea Surf. T. is adjusted to SIC correction) Global forecast model (G3F)• Global, Tripolar grids, Resolution: 1/4º×1/4º

• Initialized with G3A 4DVAR analysis fields by IAU-DST

IAU-DST

MRI-CDA1Weakly coupled DA System (outer-loop coupling)• Based on the global ODA system (MOVE/MRI.COM-G2), the atmospheric 4DVAR

system, and CGCM used in JMA’s operation

MOVE/MRI.COM-NP/JPN

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NP* (10km, 4DVAR) JPN (2km, IAU-DST)

Satellite-based SST map(MGDSST ~25km)

Mesoscale features are well constrained

Fine-scale structures can be seen.

Comparison of SST fields (MOVE-MRI.COM-NP/JPN)

Rapid mesoscale variability is well estimated by the 4DVAR scheme.

The variation is very slow because of time-smoothing effects of the objective analysis (OI).

Sea Ice 3DVAR Scheme with Surf. Air T Correction

Sea surface Atmospheric Temperature (SAT) is affected by Sea Ice Concentration (SIC) due to warmer temperature at the sea surface than on the ice top. On the other hand, SAT strongly constrains the SIC fields.

Therefore, SAT should be corrected simultaneously when SIC is changed by DA.

Introducing SAT correction according to SIC changes (Exp. 3) improves SIC fields over the experiment without SAT correction (Exp. 2).

The correction of SAT is also consistent with other studies.

SIC Bias against observation data in March

Exp.1: Assim. TS only Exp. 2: Assim. TS + SIC Exp. 3: TS+SIC+SAT Corr.

SAT Correctionin Exp. 3

From Toyoda et al. 2015

TrueFirst GuessAssimilationObservation

Japanese Data Assimilation Summer School (Since 1995)

21st Japanese Data Assimilation Summer School was held for 3 days in August of this year. Many students and early-carrier and senior researchers participated from Oceanography,

Meteorology and other Geoscience fields The curriculum includes a lecture on DA basis, presentations on cutting-edge DA studies,

and an excise of developing a data assimilation system for very simple models (Lorenz three-parameter model, 1-dimensional advective-diffusive model).

The summer school really contributes to the development of Japanese data assimilation community.

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Future Plans and Visions

Short Term Plans

• The new ocean monitoring/prediction system (10-km North Pacific 4DVAR system + 2-kmJapan-area model) will be introduced in operation from 2020.

• The new seasonal forecasting system, which includes the global ocean DA system (1-1/2-deg. 4DVAR + ¼-deg. forecast model) will be introduced in operation from 2021.

Long Term Visions

• Develop multiple-very-high-resolution coastal models.

• Provide SST and the sea ice state for weather predictions- Improving the method of assimilating satellite SST (Direct assimilation, removing biases)- Aim to reproduce the diurnal cycle

• Continue to develop the coupled data assimilation system- Aim to introduce it in operational subseasonal predictions or reanalysis.

JMA will also contribute to the GODAE community by sharing results and experiences in operation and development of ODA systems, and through evaluation of observing systems.

Thank you

Satellite SST (Himawari) T200 in WNP* (4DVAR) T200 in WNP (3DVAR)

9/18

9/30

10/12

32oN

32oN

32oN

2017 Kuroshio Large Meander (Actual Status)

10/16

11/15

10/30

T200 in WNP* (4DVAR) T200 in WNP (3DVAR)

Future Outlook (Forecasts from 10/15)

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4DVAR model (10km)

Japan-area model (2km)

New System for Ocean Forecasting

MOVE/MRI.COM-NP/JPN

Constituted of a North Pacific 4DVAR model (NP) and a Japan-area high-resolution model (JPN)

North Pacific 4DVAR Model (NP)• 10km resolution (eddy-resolving)• Nonlinear 4DVAR assimilation scheme

(The model operator in the cost function is not linearized.)

• 4DVAR optimization starts from 3DVAR results• Target: Mesoscale Variability

Japan-area high-resolution model (JPN)• 2km (resolving coastal phenomena)• Embedded in NP by two-way nesting• Tidal components are also simulated.• Initialized with NP 4DVAR analysis fields by IAU-DST

(IAU as a Down-Scaling Technique)• Target: Coastal Phenomena, Abnormal Tides

IAU-DST

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NP* (10km, 4DVAR) JPN (2km, IAU-DST)

Satellite SST image(NOAA/AVHRR)

http://www.mpstpc.pref.mie.lg.jp

/sui/kaikyo/detail.htm

Comparison of SST snapshot with NOAA/AVHRR

• Forecast model• JPN-assim (with IAU-DST)• JPN-free (without IAU-DST)

• Observation• tide gauge data• 48 hour tide killer filter

(Hanawa and Mitsudera, 1985)

• Daily mean (2009/1/1~2009/12/31)

Kuroshio path is well reproduced

Hachijo-jima

JPN-assim reproduces the sea level variations mostly better than JPN-free

Seto Inland Sea (coastal area)Kobe

Comparison with Tide gages

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New System for Seasonal Forecasting

MOVE/MRI.COM-G3

Constituted of the analysis model (G3A) and the forecasts model (G3F)(similar to a inner-model-outer-model system which uses an incremental method)

Analysis Model (G3A)• Global, Tripolar, Resolution: 1º×0.3-0.5º

• Upgrade from 3DVAR to nonlinear 4DVAR assimilation scheme for the ocean state• 4DVAR optimization starts from 3DVAR results• New Sea Ice 3DVAR schemeSeparated from the 4DVARSurf. Air Temp. is modified.

Forecast model (G3F)• Global, Tripolar• Resolution: 0.25º×0.25º

• Initialized with G3A through IAU-DST.

• SIC data are directly assimilated through SI 3DVAR.

• For seasonal predictions as a part of a coupled model.

SST deviations from observational data (28Oct-01Nov, 2012)

First Guess (G3A) - Obs Pre-3DVAR result (G3A) - Obs

4DVAR result (G3A)- Obs 3DVAR result (G2) - Obs

First Guess (G3F) - Obs

Assimilation (G3F) - Obs

The first guess fields of G3A and G3F has a similar accuracy to the current operational system (G2).

In G3A, RMSEs are reduced by pre-3DVAR, and further reduced by 4DVAR.

In G3F, RMSEs are slightly reduced by IAU-DST using G3A fields, although RMSEs are slightly larger than in G3A.

Comparison of Sea Ice Concentration Fields

Arctic Region (30Jul-03Aug, 2012) Antarctic Region (30Jul-03Aug, 2012)

FG (New System) An (New System)

An (Old System) Observation

FG (New System) An (New System)

An (Old System) Observation

Sea Ice data are not assimilated in the current operational system By assimilating Sea Ice concentration data, the distribution of the sea ice field is much

improved in the new system. The extension of the sea ice area in red circles becomes much improved. And it is

effectively modified in the analysis step. The distribution in the Antarctic Pacific basin (in blue squares) is also effectively improved.

FG (New System) An (New System)

An (Old System) Observation

Development of a Coupled DA System

Weekly coupled DA system (MRI-CDA1)

Based on the operational ocean DA system, MOVE/MRI.COM-G2, as well as the operational atmosphere DA systems and the operational coupled model.

The coupled model is used as the outer model for the atmospheric 4DVAR.

Comparison of the coupled reanalysis with the uncoupled version of it indicates that precipitation fields are slightly improved by coupled DA.

Absolute Bias (UCPL-CDA) RMSE (UCPL-CDA) ACC (UCPL-CDA)

Comparison of 5-day averaged precipitation scores against CMAP for Dec. 2014-Nov. 2015 between the coupled reanalysis (CDA) and

uncoupled version (UCPL)