1. Forecast system version

Identifier code:  JMA/MRI-CPS4

First operational forecast run: January 2026

2. Configuration of the forecast model

Is the model coupled to an ocean model?  Yes

Coupling frequency: 1 hour

2.1 Atmosphere and land surface

ModelJMA-GSM
Horizontal resolution and grid

TL319

(approx. 55km)

Atmosphere vertical resolutionL128
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

Detailed documentation: 

JMA, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Technical Progress Report on the Global Data-processing and Forecasting System and Numerical Weather Prediction Research. Appendix to WMO Technical Progress Report on the Global Data-processing and Forecasting System and Numerical Weather Prediction, Tokyo, Japan.

Yonehara, H., C. Matsukawa, T. Nabetani, T. Kanehama, T. Tokuhiro, K. Yamada, R. Nagasawa, Y. Adachi, and R. Sekiguchi, 2020: Upgrade of JMA’s Operational Global Model. Research activities in Earth system modelling. Working Group on Numerical Experimentation. Report No. 50. WCRP Report No.12/2020. WMO, Geneva. Section 6, page 19.

2.2 Ocean and cryosphere

Ocean modelMRI.COM v5.0
Horizontal resolution0.25ºx0.25º on a tripolar grid
Vertical resolutionL60
Time step10 minutes
Sea ice modelpart of MRI.COM v5.0
Sea ice model resolutionsame as ocean model
Sea ice model levels5 categories + open water
Wave modelNone
Wave model resolutionN/A

Detailed documentation: 

Sakamoto, K., H. Nakano, S. Urakawa, T. Toyoda, Y. Kawakami, H. Tsujino, and G. Yamanaka, 2023: Reference manual for the Meteorological Research Institute Community Ocean Model version 5 (MRI.COMv5). Tech. Rep. MRI, 87

3. Initialization and initial condition (IC) perturbations

3.1 Atmosphere and land


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

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 GA
Unperturbed control forecast?YesYes

Data assimilation method for control analysis: 4D-VAR

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

Perturbations in +/- pairs: Yes

Detailed documentation: 
Kosaka, Y., S. Kobayashi, Y. 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. Sato, Y. Matsushita, and K. Onogi, 2024: The JRA-3Q reanalysis. 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: 

Fujii, 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://www.data.jma.go.jp/wmc/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?

No

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

Forecast frequencydaily
Forecast ensemble size5 per day
Hindcast years1991-2020
Hindcast ensemble size10 (5 members with 15-day Lagged Average Forecast)
On-the-fly or static hindcast set?static

6. Other relevant information

The available hindcast start dates are as follows:

Start MonthAvailable Start Days
January

16 and 31

February10 and 25
March12 and 27
April11 and 26
May16 and 31
June15 and 30
July15 and 30
August14 and 29
September13 and 28
October13 and 28
November12 and 27
December12 and 27

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://www.data.jma.go.jp/wmc/products/model/outline/cps_description.html

https://www.data.jma.go.jp/wmc/products/elnino/move_mricom-g3_doc.html