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titleWORK IN PROGRESS...

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Identifier code:  JMA/MRI-CPS3CPS4

First operational forecast run: February 2022 January 2026

2. Configuration of the forecast model

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ModelJMA-GSM
Horizontal resolution and grid

TL319

(approx. 55km)

Atmosphere vertical resolutionL100L128
Top of atmosphere0.01hPa
Soil levels (layers)

7

Layer 1 : 0 - 0.02 m
Layer 2 : 0.02 - 0.07
Layer 3 : 0.07 – 0.19 m
Layer 4 : 0.19 – 0.49 m
Layer 5 : 0.49 – 0.99 m
Layer 6 : 0.99 – 1.99 m

Layer 7 : 1.99 – 3.49 m
Time step20 minutes

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2.2 Ocean and cryosphere

Ocean modelMRI.COM v4v5.60
Horizontal resolution0.25ºx0.25º on a tripolar grid
Vertical resolutionL60
Time step10 minutes
Sea ice modelpart of MRI.COM v4v5.60
Sea ice model resolutionsame as ocean model
Sea ice model levels5 categories + open water
Wave modelNone
Wave model resolutionN/A

Detailed documentation: 

TsujinoSakamoto, HK., H. Nakano, KS. SakamotoUrakawa, ST. UrakawaToyoda,  MY. HirabaraKawakami, H. IshizakiTsujino, and G. Yamanaka, 20172023: Reference manual for the Meteorological Re-search Research Institute Community Ocean Model version 4 5 (MRI.COMv4COMv5). Tech. Rep. MRI, 80.87

3. Initialization and initial condition (IC) perturbations

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HindcastForecast
Atmosphere initialization
Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)Global Analysis (GA)
Atmosphere IC perturbationsBreeding Growth Method (BGM)Singular Vectors (SV)Singular Vectors (SV) and Local Ensemble Transform Kalman Filter (LETKFBreeding Growth Method (BGM)

Land Initialization

Offline model runs forced by JRA-3QOffline model runs forced by JRA-3Q and GA
Land IC perturbationsNoneNone
Soil moisture initializationOffline model runs forced by JRA-3QOffline model runs forced by JRA-3Q and GA
Snow initializationOffline model runs forced by JRA-3QOffline model runs forced by JRA-3Q and GA
Unperturbed control forecast?YesYes

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Horizontal and vertical resolution of perturbations:  TL319L100  TL319L128 (LETKF), TL63L40 (SV)

Perturbations in +/- pairs: Yes

Detailed documentation: 
KobayashiKosaka, Y., S. Kobayashi, Y. Kosaka. Harada, C. Kobayashi, H. Naoe, K. Yoshimoto, M. Harada, N. Goto, J. Chiba, K. Miyaoka, R. Sekiguchi, M. Deushi, H. Kamahori, T. Nakaegawa; T. Y.Tanaka, T. Tokuhiro, Y. HaradaSato, CY. Kobayashi Matsushita, and HK. NaoeOnogi, 20212024: The JRA-3Q : Japanese Reanalysis for Three Quarters of a Century. WCRP-WWRP Symposium on Data Assimilation and Reanalysis/ECMWF annual seminar 2021reanalysis. J. Meteor. Soc. Japan, 102, 49–109.

3.2 Ocean and cryosphere


HindcastForecast
Ocean initialization

Multivariate Ocean Variational Estimation (MOVE)/MRI.COM Global 3 system

(MOVE/MRI.COM-G3)

Multivariate Ocean Variational Estimation (MOVE)/MRI.COM Global 3 system

(MOVE/MRI.COM-G3)

Ocean IC perturbationsEnsemble perturbations approximating analysis error covariances using minimization historiesEnsemble perturbations approximating analysis error covariances using minimization histories
Unperturbed control forecast?YesYes

Detailed documentation: 

Usui, N., Y. Fujii, K. Sakamoto, and M. Kamachi, 2015: Development of a Four-Dimensional Variational Assimilation System toward Coastal Data Assimilation around Japan. Mon. Wea. Rev., 143, 3874-3892Fujii, Y., T. Yoshida, H. Sugimoto, I. Ishikawa, and S. Urakawa, 2023: Evaluation of a global ocean reanalysis generated by a global ocean data assimilation system based on a Four-Dimensional Variational (4DVAR) method. Front Clim, 4, 1-20.
Niwa, Y. and Y. Fujii, 2020: A conjugate BFGS method for accurate estimation of a posterior error covariance matrix in a linear inverse problem. Quart. J. Roy. Meteor. Soc., 146, 3118-3143.

https://dswww.data.jma.go.jp/tccwmc/tcc/products/elnino/move_mricom-g3_doc.html

4. Model Uncertainties perturbations:

Model dynamics perturbationsNone
Model physics perturbationsStochastically Perturbed Parametrization Tendencies (SPPT) scheme
and
Stochastic Humidity Profile for Convective parameterization (SHPC)

If there is a control forecast, is it perturbed?

YesNo

Detailed documentation: 

Ota, Y., 2025: Introduction of Stochastic Humidity Profile for Convective parametrization (SHPC) method in JMA’s Global Ensemble Prediction System. WGNE Res. Activ. Earth Sys. Model., 55, 3-34.

Yonehara, H. and M. Ujiie, 2011: A stochastic physics scheme for model uncertainties in the JMA one-week ensemble prediction system. CAS/JSC WGNE Res. Act. Atmos. Ocean Model/WMO, 41, 6.09–6.10.

5. Forecast system and hindcasts

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7. Where to find more information

General information:

Kubo, Y., K. Ochi, J. Chiba, T. Yoshida, T. Takakura, R. Sekiguchi, Y. Adachi, M. Deushi, and S. Hirahara, 2025: Upgrade of the JMA Sub-Seasonal and Seasonal Ensemble Prediction System (JMA/MRI-CPS4). WGNE Res. Activ. Earth Sys. Model., 55, 1-14.

https://dswww.data.jma.go.jp/tcc/tccwmc/products/model/outline/cps3cps_description.html

https://dswww.data.jma.go.jp/tccwmc/tcc/products/elnino/move_mricom-g3_doc.html