Description of the Seasonal Ensemble Prediction System (JMA/MRI-CPS3)

JMA operates the seasonal ensemble prediction system for sub-seasonal to seasonal forecasts. The latest system, JMA/MRI-CPS3 (Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 3; "CPS3" in short), was implemented in February 2022 and has been used since for operational forecasts: the "Three-month prediction", "Warm/cold season outlook" and "El Niño outlook". This page outlines the specifications and operational time schedule of CPS3.

CPS3 is an atmosphere/ocean/land/sea ice-coupled prediction system consisting of an initialization system and a forecast model. The atmospheric model is based on GSM2003 (Yonehara et al. 2020), and additional improvements are added for physical parameterization including cumulus convection, cloud and gravity wave. Atmospheric conditions are initialized with JRA-3Q (Kobayashi et al. 2021) for re-forecasts, and Global Analysis (GA) for operation, in which atmospheric conditions are updated with a shorter time delay. CPS3 has a different land-sea mask from these analysis and a unique lake scheme which needs initialization. To avoid initial shock, offline surface simulation for the period to date is separately run and used for forecasts. Ocean and sea ice conditions are initialized with MOVE/MRI.COM-G3, a global ocean 4DVAR analysis (Usui et al. 2015) downscaled to an eddy-permitting resolution (0.25 by 0.25° in longitude and latitude). The ensemble prediction system involves a combination of LAF and initial perturbation as in the previous system (Takaya et al. 2018). Five-member ensemble predictions are made every day from 00UTC, and perturbed atmospheric conditions are determined for each initial time using BGM (Toth and Kalnay 1993, Chikamoto et al. 2007). Ocean perturbations are calculated using 4DVAR minimization history, by which daily analysis error covariance can be approximated (Niwa and Fujii 2020).

System Name		JMA/MRI-CPS2	JMA/MRI-CPS3
Operation Start Date		June 2015	February 2022
Atmospheric model	Model version	GSM1011C	GSM2003C
	Horizontal Resolution	Global TL159 reduced Gaussian grid(~110km)	Global TL319 reduced Gaussian grid(~55km)
	Vertical levels(model top)	60 levels(0.1hPa)	100 levels(0.01hPa)
Ocean model	Model version	MRI.COM v3.2	MRI.COM v4.6
	Horizontal Resolution	1° (longitude) x 0.3-0.5° (latitude)	0.25° (longitude) x 0.25° (latitude)
	Vertical levels	52 levels with a bottom boundary layer	60 levels
Forecast Frequency		13 ensemble members per 5 days	5 ensemble members per day
Initial condition	Atmosphere	JRA-55	JRA-3Q (hindcasts) and the Global Analysis (GA; forecasts only)
	Land Surface	JRA-55	Offline model runs forced by JRA-3Q and GA
	Ocean	MOVE/MRI.COM-G2(3DVAR)	MOVE/MRI.COM-G3(Low-res. 4DVAR+High-res. downscaling)
	Sea ice		MOVE/MRI.COM-G3(3DVAR)
Ensemble generation method	Initial Condition perturbation	The breeding of growing mode (BGM) method for the atmosphere The Lagged Average Forecast (LAF) method	BGM for the atmosphere Ocean perturbations calculated using 4DVAR minimization history The LAF method
	Model Perturbation	Stochastic physics parameterization for the atmosphere
Hindcast	Period	Two initial dates per month for 1979-2014	Two initial dates per month for 1991-2020
	Ensemble size	Five for each initial date

The figure below shows how CPS3 runs in operation. The five-member ensemble is generated once a day from 00UTC analysis. JMA currently uses only three members for 17 LAF dates to make 51-member model statistics products. Registered users can access all members at the download page.

Fig. 1 Schematic figure of ensemble configurations of operational suite.

To assess forecast performance, a set of hindcasts (re-forecasts) was carried out as in real-time operation (except for a smaller ensemble size of 10 per month). Verification scores are openly published on the hindcast verification page, and registered users can download hindcast data from the download page.

• Chikamoto, Y., H. Mukougawa, T. Kubota, H. Sato, A. Ito and S. Maeda, 2007: Evidence of growing bred vector associated with the tropical intraseasonal oscillation. Geophys. Res. Lett., 34, L04806.
• Tsujino, H., M. Hirabara, H. Nakano, T. Yasuda, T. Motoi and G. Yamanaka, 2020: Simulating present climate of the global ocean-ice system using the Meteorological Research Institute Community Ocean Model (MRI.COM): simulation characteristics and variability in the Pacific sector. Journal of Oceanography, 67, 449-479.
• World Meteorological Organization, 2020: Join WMO Technical Progress Report on the Global Data Processing and Forecasting System and Numerical Weather Prediction Research Activities for 2020, WMO, Geneva. [link]
• Japan Meteorological Agency, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO Numerical Weather Prediction Progress Report, Tokyo, Japan. [link]
• Kobayashi, S., Y. Kosaka, J. Chiba, T. Tokuhiro, Y. Harada, C. Kobayashi and H. Naoe, 2021: JRA-3Q: Japanese Reanalysis for Three Quarters of a Century. WCRP-WWRP Symposium on Data Assimilation and Reanalysis/ECMWF annual seminar 2021.
• 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.
• Usui, N., Y. Fujii, K. Sakamoto and M. Kamachi, 2015: Development of a Four-Dimensional Variational Assimilation System for Coastal Data Assimilation around Japan. Monthly Weather Review, 143, 10, 3874-3892.
• Takaya, Y., S. Hirahara, T. Yasuda, S. Matsueda, T. Toyoda, Y. Fujii, H. Sugimoto, C. Matsukawa, I. Ishikawa, H. Mori, R. Nagasawa, Y. Kubo, N. Adachi, G. Yamanaka, T. Kuragano, A. Shimpo, S. Maeda and T. Ose, 2018: Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere-land-ocean-sea ice coupled prediction system for operational seasonal forecasting. Clim. Dyn., 3-4, 751-765.
• 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. WGNE. Res. Activ. Earth. Sys. Modell., 50, 6-19.
• Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Amer. Met. Soc., 74, 12, 2317-2330.

Appendix: Upgrade history of JMA's coupled prediction system

Implemented date	Upgrades and Changes
Aug. 1998	The first JMA's CGCM for ENSO prediction implemented to operation.
Jul. 2003	The second CGCM for ENSO prediction (JMA-CGCM02) with an improved atmospheric model and sea-surface flux adjustment.
Jun. 2005	Frequent forecast update (from every 15 days to every 5 days).
Mar. 2008	The Third CGCM for ENSO prediction (JMA/MRI-CGCM1). Oceanic component changed to the new ocean general circulation model "MRI.COM" and data assimilation system "MOVE/MRI.COM-G".
Mar. 2009	Introduced an ocean initial perturbation method. The ensemble size increased from 12 to 30.
Mar. 2010	The first integrated forecast system "JMA/MRI-CPS1" that covers seasonal forecasts in addition to the "El Niño outlook". Introduced an atmospheric perturbation method. The ensemble size increased from 30 to 51.
Jun. 2015	JMA/MRI-CPS2: a dynamical sea ice model, land initialization, historical GHG concentration. The ensemble size per initial date increased from 9 to 13.
Feb. 2022	JMA/MRI-CPS3: 4DVAR ocean and 3DVAR sea ice initialization, an eddy-permitting ocean resolution, improved atmospheric physics, daily forecast update (5 ensemble members per day).