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12 members run members run 00Z and after bred and stochastic scheme. Ensembles are based on 50 membersN/N/128 T63L40 T63L40 , has been implemented Soil moisture is initialized realistically by JMA’s soil moisture analysis based on Simplified Extended Kalman Filter (SEKF). Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA)TL479The global snow depth is analyzed using SYNOP snow depth data on the day. The forecasted snow depth corrected by satellite-estimated snow area is employed as first guess of the snow depth analysis. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is converted to In addition, snow depth on Japan land grids is replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.The global snow depth with 1.0 degree latitude/longitude resolution is analyzed using SYNOP snow depth data on the day. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is interpolated to . In addition, snow depth on Japan land grids are replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.The global snow depth with 1.0 degree latitude/longitude resolution A two-dimensional optimum interpolation (OI) is employed for the analysis interpolated TL479 are Initial snow data is also produced by using the offline simulation of the land surface model, and no horizontal interpolation is introduced. In the offline simulation, snow depth data is updated once a day (00UTC) by the two-dimensional Optimal Interpolation using the first guess is calculated by snow depth data from the offline simulation and snow cover data estimated by satellite observation. Forecast model calculates the snow water equivalent, so snow depth data is converted to the snow water equivalent. Snow density is set as a function related to the snow water equivalent (Verseghy 1991). Snow albedo is set as a function of wavelength and snow temperature. The age of snow is not considered.Soil temperature cycled from the GA model forecast with resolution of TL959 It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature 2020 the S2S database with 2 date attributes:

hdate which corresponds to the actual starting date of the re-forecast
date which correspond tot he ModelVersionDate.Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20140304. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.

4. Model uncertainties perturbations
1. Ensemble version
Ensemble identifier code	JMA CPS3	JMA GEPS2203	JMA GEPS2103	JMA GEPS2003	JMA GEPS1701	GSM1403C
Short Description	Coupled Prediction System that simulates initial atmospheric uncertainties using the Breeding Growth Mode (BGM), its oceanic uncertainties approximating the analysis error covariance using oceanic 4DVAR minimization history and model uncertainties due to physical parameterizations using a stochastically perturbed physics tendencies (SPPT) scheme.	Global ensemble system that simulates initial uncertainties using the Local Ensemble Transform Kalman Filter (LETKF), the singular vectors and lagged averaging forecasts, model uncertainties due to physical parameterizations using a stochastically perturbed physics tendencies (SPPT) scheme, and uncertainties on surface boundary conditions using sea surface temperature (SST) perturbation. Although it is uncoupled model, SST is relaxed to the ensemble-mean SST based on prediction using coupled model after lead time of	6 days. Ensembles are based on 50	members run once a week with 2 start dates (Tuesday and Wednesday at 12Z) up to day 34.	Global ensemble system that simulates initial uncertainties using the Local Ensemble Transform Kalman Filter (LETKF), the singular vectors and lagged averaging forecasts, model uncertainties due to physical parameterizations using a stochastically perturbed physics tendencies (SPPT) scheme, and uncertainties on surface boundary conditions using sea surface temperature (SST) perturbation. Although it is uncoupled model, SST is relaxed to the ensemble-mean SST based on prediction using coupled model after	lead time of 12 days.	Ensembles are based on 50	members run once a week with 2 start dates (Tuesday and Wednesday at	12Z) up to day 34.	Global ensemble system that simulates initial uncertainties using the Local Ensemble Transform Kalman Filter (LETKF), the singular vectors and lagged averaging forecasts, model uncertainties due to physical parameterizations using a stochastically perturbed physics tendencies (SPPT) scheme, and uncertainties on surface boundary conditions using sea surface temperature (SST) perturbation. Although it is uncoupled model, SST is relaxed to the ensemble-mean SST based on prediction using coupled model	after lead time of 12 days. Ensembles are based on 50 members run once a week (Tuesday and Wednesday at 00Z and 12Z) up to day 34.	Global ensemble system that simulates initial uncertainties using the	Local Ensemble Transform Kalman Filter (LETKF), the singular vectors and lagged averaging forecasts	, model uncertainties due to physical parameterizations using a	stochastically perturbed physics tendencies (SPPT) scheme, and uncertainties on surface boundary conditions using sea surface temperature (SST) perturbation. Ensembles are based on 50 members run once a week (Tuesday and Wednesday at 00Z and 12Z) up to day 34.	Global ensemble system that simulates initial uncertainties using the bred vectors and lagged averaging forecasts and model uncertainties due to physical parameterizations using a stochastic scheme. Ensembles are based on 50 members	, run once a week (Tuesday, Wednesday at 12Z) up to day 34.
Research or operational	Operational	Operational	Operational	Operational	Operational	Operational
Data time of first forecast run	19/02/2023	15/03/2022	30/03/2021	24/03/2020	22/03/2017	05/03/2014
2. Configuration of the EPS
Is the model coupled to an ocean model?	Yes, from day 0	No	No	No	No	No
If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied	N/A	Ocean model is MRI.COMv4.6 with a 0.25-degree horizontal resolution, 60 vertical levels, initialized from MOVE-G3 (Fujii et al. 2023) Analysis + 4 perturbed analyses produced by approximating the analysis error covariance using oceanic 4DVAR minimization history (Niwa and Fujii, 2020). Frequency of coupling is hourly.	N/A	N/A	N/A	N/A	N/A
If no, please describe the sea surface temperature boundary conditions (climatology, reanalysis ...)
Is the model coupled to a sea Ice model?	Yes	No	No	No	No	No
If yes, please describe sea-ice model briefly including any ensemble perturbation applied	N/A	Interactive sea-ice model (MRI.COMv4.6). Initial perturbations of sea-ice from the 5-ensemble ocean. No stochastic perturbations.	N/A	N/A	N/A	N/A	N/A
Is the model coupled to a wave model?	No	No	No	No	No	No
If yes, please describe wave model briefly including any ensemble perturbation applied	N/A	N/A	N/A	N/A	N/A	N/A
Ocean model	MRI.COM 0.25-degree resolution	N/A	N/A	N/A	N/A	N/A
Horizontal resolution of the atmospheric model	TL319 (about 55 km).	TQ479 (about 27 km) up to 18 days, TQ319 (about 40 km) after 18 days.	TL479 (about 40 km) up to 18 days, TL319 (about 55 km) after 18 days.	TL479 (about 40 km) up to 18 days, TL319 (about 55 km) after 18 days.	TL479 (about 40 km) up to 18 days, TL319 (about 55 km) after 18 days.	TL319 (about 55 km)
Number of model levels	100	128	128	100	100	60
Top of model	0.01 hPa	0.01 hPa	0.01 hPa	0.01 hPa	0.01 hPa
Type of model levels	hybrid (sigma-p) coordinate	hybrid (sigma-p) coordinate	hybrid (sigma-p) coordinate	hybrid (sigma-p) coordinate	hybrid (sigma-p) coordinate	sigma
Forecast length	34 days (816 hours)	34 days (816 hours), but archived up to 32.5 days (780 hours)	34 days (816 hours), but archived up to 32.5 days (780 hours)	34 days (816 hours), but archived up to 32.5 days (780 hours)	34 days (816 hours), but archived up to 32.5 days (780 hours)	34 days (816 hours)
Run Frequency	every day at 00Z	once a week (combination of Tuesday and Wednesday at 12Z)	once a week (combination of Tuesday and Wednesday at 12Z)	once a week (combination of Tuesday and Wednesday at 00Z and 12Z)	once a week (combination of Tuesday and Wednesday at 00Z and 12Z)	once a week (combination of Tuesday and Wednesday at 00Z and 12Z)
Is there an unperturbed control forecast included?	Yes	Yes	Yes	Yes	Yes	Yes
Number of perturbed ensemble members	4 (1 control)	48 (totally 2 controls from 2 initial dates)	48 (totally 2 controls from 2 initial dates)	46 (4 controls from each initial date), but archived as 49 (1 control from each initial date)	46 (4 controls from each initial date), but archived as 49 (1 control from each initial date)	48 (2 controls from each initial date)
Integration time step	20 minutes	10 minutes up to 18 days and 12 minutes after 18 days	12 minutes up to 18 days and 20 minutes after 18 days	12 minutes up to 18 days and 20 minutes after 18 days	12 minutes up to 18 days and 20 minutes after 18 days	20 minutes
3. Initial conditions and perturbations
Data assimilation method for control analysis	hybrid 4D Var-LETKF	hybrid 4D Var-LETKF	hybrid 4D Var-LETKF	hybrid 4D Var	-LETKF	4D Var	4D Var
Resolution Resolution of model used to generate Control Analysis	TL959L128	TL959L128	TL959L128	TL959L100	TL959L100	TL959L100
Ensemble initial perturbation strategy	Bred vectors (Northern Hemisphere, Tropics and Southern Hemisphere)	LETKF + singular vectors (initial SV; Northern Hemisphere, Tropics and Southern Hemisphere) + Lagged Average Forecasting	LETKF + singular vectors (initial SV; Northern Hemisphere, Tropics and Southern Hemisphere) + Lagged Average Forecasting	LETKF + singular vectors (initial SV; Northern Hemisphere, Tropics and Southern Hemisphere) + Lagged Average Forecasting	LETKF + singular vectors (initial SV; Northern Hemisphere, Tropics and Southern Hemisphere) + Lagged Average Forecasting	Bred vectors (extratropics (NH) plus tropics) + Lagged Average Forecasting
Horizontal and vertical resolution of perturbations	TL319L100	TL319L128 (LETKF),	TL63L40 (SV)	TL319L128 (LETKF), TL63L40 (SV)	TL319L100 (LETKF),	TL63L40 (SV)	TL319L100 (LETKF), T63L40 (SV)	TL319L60
Perturbations in +/- pairs	Yes	Yes (SV), No (LETKF), No (SST)	Yes (SV), No (LETKF), No (SST)	Yes (SV), No (LETKF), No (SST)	Yes (SV), No (LETKF), No (SST)	Yes
Initialization of land surface
3.1 What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM?	See Land Surface Processes Chapter 3.2.10 by JMA (2022).	LSM is the JMA SIB. The JMA SIB is based on the Simple Biosphere (SiB) developed by Sellers et al.(1986) and implemented by Sato et al.(1989a) and Sato et al.(1989b) for the land surface process in forecast model, and overall specifications are comprehensively updated and refined schemes are introduced. Significant changes from SiB are; replacement the force-restore method for soil temperature prediction with a multilayer soil heat and water flux model and separate layers for snow introduction of an equation of heat conduction with seven soil levels consideration of the release or absorption of latent heat from phase change for soil temperature prediction introduction of a new snow model with up to four layers with consideration for thermal diffusion, increased density from snow compaction and reduction of albedo due to snow aging The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a)	LSM is the JMA SIB. The JMA SIB is based on the Simple Biosphere (SiB) developed by Sellers et al.(1986) and implemented by Sato et al.(1989a) and Sato et al.(1989b) for the land surface process in forecast model, and overall specifications are comprehensively updated and refined schemes are introduced. Significant changes from SiB are; replacement the force-restore method for soil temperature prediction with a multilayer soil heat and water flux model and separate layers for snow introduction of an equation of heat conduction with seven soil levels consideration of the release or absorption of latent heat from phase change for soil temperature prediction introduction of a new snow model with up to four layers with consideration for thermal diffusion, increased density from snow compaction and reduction of albedo due to snow aging The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a)	LSM is the JMA SIB. The JMA SIB is based on the Simple Biosphere (SiB) developed by Sellers et al.(1986) and implemented by Sato et al.(1989a) and Sato et al.(1989b) for the land surface process in forecast model, and overall specifications are comprehensively updated and refined schemes are introduced. Significant changes from SiB are; replacement the force-restore method for soil temperature prediction with a multilayer soil heat and water flux model and separate layers for snow introduction of an equation of heat conduction with seven soil levels consideration of the release or absorption of latent heat from phase change for soil temperature prediction introduction of a new snow model with up to four layers with consideration for thermal diffusion, increased density from snow compaction and reduction of albedo due to snow aging The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a)	LSM is the JMA SIB. The JMA SIB is based on the Simple Biosphere (SiB) developed by Sellers et al.(1986)	and implemented by Sato et al.(1989a) and Sato et al.(1989b)	for the land surface process in forecast model, and overall specifications are comprehensively updated and refined schemes are introduced.	3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other)? If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? If all model soil layers are not initialized in the same way or from the same source, please describe.	Soil moisture is initialized realistically by JMA’s soil moisture analysis based on Simplified Extended Kalman Filter (SEKF). Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA). To prevent unrealistic soil moisture drifting after long-term integration, initial soil moisture deeper than the 4th layer is set to the climatological value. See Ochi (2020) for details.	Significant changes from SiB are; replacement the force-restore method for soil temperature prediction with a multilayer soil heat and water flux model and separate layers for snow introduction of an equation of heat conduction with seven soil levels consideration of the release or absorption of latent heat from phase change for soil temperature prediction introduction of a new snow model with up to four layers with consideration for thermal diffusion, increased density from snow compaction and reduction of albedo due to snow aging The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a)	The Simple Biosphere (SiB) developed by Sellers et al.(1986), Sato et al.(1989a) and Sato et al.(1989b) has been implemented for the land surface process in forecast model.
3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other)? If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? If all model soil layers are not initialized in the same way or from the same source, please describe.	Soil moisture cycled from JMA’s offline surface simulation forced by JMA Global Analysis (GA) and JRA-3Q (Kobayashi et al. 2021) is separately run and used for forecasts. No horizontal and vertical interpolation are applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in GA. To prevent unrealistic soil moisture drifting after long-term integration, initial soil moisture deeper than or equal to the 4th layer is set to the climatological value. See Ochi (2020) for details.	Soil moisture is initialized realistically by JMA’s soil moisture analysis based on Simplified Extended Kalman Filter (SEKF). Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (	TQ479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA). To prevent unrealistic soil moisture drifting after long-term integration, initial soil moisture deeper than or equal to the 4th layer is set to the climatological value. See Ochi (2020) for details.	Initial soil moisture data (which consists of three layers) is produced by the offline simulations of the land surface model. The land processes in this simulations are similar to the one set in forecast model, and no horizontal and vertical interpolations are introduced in this analysis. Soil ice is handled as soil water, and no soil ice is used as initial condition specifically.	3.3 How is snow initialized in the forecasts? (climatology / realistic / other)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties?	Soil moisture is initialized realistically by JMA’s soil moisture analysis based on Simplified Extended Kalman Filter (SEKF). Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA). To prevent unrealistic soil moisture drifting after long-term integration, initial soil moisture deeper than or equal to the 4th layer is set to the climatological value. See Ochi (2020) for details.	Soil moisture is initialized with climatology derived from the offline simulations of the LSM with resolution of TL959. Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479).	No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA).	Soil moisture is initialized with climatology derived from the offline simulations of the LSM with resolution of TL959. Soil moisture and other initialization data are interpolated from the resolution of the deterministic system (TL959) to the resolution of the EPS forecast model grid (TL479). No vertical interpolation is applied. The LSM defines two soil moisture state variables, liquid and ice. The amounts of liquid and ice are determined by soil temperature and the ratio of the two soil moisture state variables cycled from the model forecast in the JMA Global Analysis (GA).	Initial soil moisture data (which consists of three layers) is produced by the offline simulations of the land surface model. The land processes in this simulations are similar to the one set in forecast model, and no horizontal and vertical interpolations are introduced in this analysis. Soil ice is handled as soil water, and no soil ice is used as initial condition specifically.
3.3 How is snow initialized in the forecasts? (climatology / realistic / other)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties?	The global snow depth	is analyzed using SYNOP snow depth data on the day.	The forecasted snow depth corrected by satellite-estimated snow area is employed as first guess of the snow depth analysis. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is	converted to the EPS forecast model grid (	TL319). In addition, snow depth on Japan land grids	is replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.	The global snow depth is analyzed using SYNOP snow depth data on the day. The	3.4 How is soil temperature initialized in the forecasts? (climatology / realistic / other) Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) If all model soil layers are not initialized in the same way or from the same source, please describe.	Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast	forecasted snow depth corrected by satellite-estimated snow area is employed as first guess of the snow depth analysis. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is converted to the EPS forecast model grid (TQ479). In addition, snow depth on Japan land grids is replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.	The global snow depth is analyzed using SYNOP snow depth data on the day. The forecasted snow depth corrected by satellite-estimated snow area is employed as first guess of the snow depth analysis. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is converted to the EPS forecast model grid (TL479). In addition, snow depth on Japan land grids is replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM.	Snow age is cycled from the GA model forecast	and albedo is calculated with the snow age.	The global snow depth with 1.0 degree latitude/longitude resolution is analyzed using SYNOP snow depth data on the day. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is interpolated to the EPS forecast model grid (TL479).	In addition, snow depth on Japan land grids are replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast	Soil temperature is also initialized by the offline simulations of the land surface model. No horizontal and vertical interpolation is implemented. Note that soil layers are three for soil moisture, while it is only one layer for soil temperature. Snow cover is updated at each 00UTC based on the snow depth analysis. At the same time, soil temperature for all grids where snow exists is set as less than 0 deg. Once the soil temperature becomes less than 0deg, soil water changes soil ice (No consideration about freeze latent heat). No soil water scatters or moves in the freezing soil.
3.5 How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)
3.6 What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM?	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	The source of soil properties is different depending on the property. For example, soil porosity is set as outer parameters for each type of vegetation, while soil heat conductivity is set as a function related to the porosity and soil moisture in the first soil layer. In addition, some parameters are not considered such as difference of soil texture.
3.7 If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences.	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	There is no difference between re-forecast and operational forecast about the procedure for initialization of land surface. For the re-forecast, land analysis data of JRA-55 is utilized as the land initial data and it is derived from the offline system forced by JRA-55 atmospheric field. This system is similar to the operational system, but note that the atmospheric forcing for operational offline system is given from the operational Global Analysis.
Is model physics perturbed? If yes, briefly describe methods	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastic physics
Do all ensemble members use exactly the same model version?	Same	Same	Same	Same
Is model dynamics perturbed?	No	No	No	No
Are the above model perturbations applied to the control forecast?	No	No	No	No
5. Surface boundary perturbations
Perturbations to sea surface temperature?	Yes	Yes	Yes	No
Perturbation to soil moisture?	No	No	No	No
Perturbation to surface stress or roughness?	No	No	No	No
Any other surface perturbation?	No	No	No	No
Are the above surface perturbations applied to the Control forecast?	No	No	No	No
Additional comments	None	None	None	None
6. Other details of the models
and albedo is calculated with the snow age.	The global snow depth with 1.0 degree latitude/longitude resolution is analyzed using SYNOP snow depth data on the day. A two-dimensional optimum interpolation (OI) is employed for the analysis method. The analyzed snow depth is interpolated to the EPS forecast model grid (TL479). In addition, snow depth on Japan land grids are replaced with interpolated latest observation value of AMeDAS (Automated Meteorological Data Acquisition System) snow depth as well as Japanese SYNOP. The analyzed snow depth is converted to snow mass using snow density cycled from the GA model forecast and the snow mass is used as an initial condition for the LSM. Snow age is cycled from the GA model forecast and albedo is calculated with the snow age.	Initial snow data is also produced by using the offline simulation of the land surface model, and no horizontal interpolation is introduced. In the offline simulation, snow depth data is updated once a day (00UTC) by the two-dimensional Optimal Interpolation using the SYNOP snow depth data. The first guess is calculated by snow depth data from the offline simulation and snow cover data estimated by satellite observation. Forecast model calculates the snow water equivalent, so snow depth data is converted to the snow water equivalent. Snow density is set as a function related to the snow water equivalent (Verseghy 1991). Snow albedo is set as a function of wavelength and snow temperature. The age of snow is not considered.
3.4 How is soil temperature initialized in the forecasts? (climatology / realistic / other) Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) If all model soil layers are not initialized in the same way or from the same source, please describe.	Soil temperature cycled from JMA’s offline surface simulation forced by GA and JRA-3Q is separately run and used for forecasts. No horizontal and vertical interpolation are applied. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast.	Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast	Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast	Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast	Soil temperature cycled from the GA model forecast with resolution of TL959 is used as an initial condition for the LSM. It is interpolated to the EPS forecast model grid. The liquid and ice content of the soil initialized in the forecasts are determined by the soil temperature cycled from the GA model forecast	Soil temperature is also initialized by the offline simulations of the land surface model. No horizontal and vertical interpolation is implemented. Note that soil layers are three for soil moisture, while it is only one layer for soil temperature. Snow cover is updated at each 00UTC based on the snow depth analysis. At the same time, soil temperature for all grids where snow exists is set as less than 0 deg. Once the soil temperature becomes less than 0deg, soil water changes soil ice (No consideration about freeze latent heat). No soil water scatters or moves in the freezing soil.
3.5 How are time-varying vegetation properties represented in the LSM? Is phenology predicted by the LSM? If so, how is it initialized? If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)	There is no consideration about time-varying of vegetation properties. The climatology for each vegetation based on Dorman and Sellers (1989) is set as outer parameters (Some parameters are monthly data, such as LAI, the ratio of green leaves, the ratio of vegetation)
3.6 What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM?	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	Soil thermal and hydraulic properties are calculated using soil texture from the HWSD (Harmonized World Soil Database).	The source of soil properties is different depending on the property. For example, soil porosity is set as outer parameters for each type of vegetation, while soil heat conductivity is set as a function related to the porosity and soil moisture in the first soil layer. In addition, some parameters are not considered such as difference of soil texture.
3.7 If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences.	Land surface values are estimated with the offline land-surface model in the CPS3 using atmospheric forcing from JRA-3Q.	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-3Q (Kobayashi et al. 2021)	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	Land surface values are estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55 (Kobayashi et al. 2015)	There is no difference between re-forecast and operational forecast about the procedure for initialization of land surface. For the re-forecast, land analysis data of JRA-55 is utilized as the land initial data and it is derived from the offline system forced by JRA-55 atmospheric field. This system is similar to the operational system, but note that the atmospheric forcing for operational offline system is given from the operational Global Analysis.
4. Model uncertainties perturbations
Is model physics perturbed? If yes, briefly describe methods	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastically perturbed physics tendencies (SPPT) scheme	Stochastic physics
Do all ensemble members use exactly the same model version?	Same	Same	Same	Same	Same	Same
Is model dynamics perturbed?	No	No	No	No	No	No
Are the above model perturbations applied to the control forecast?	No	No	No	No	No	No
5. Surface boundary perturbations
Perturbations to sea surface temperature?	Yes	Yes	Yes	Yes	Yes	No
Perturbation to soil moisture?	No	No	No	No	No	No
Perturbation to surface stress or roughness?	No	No	No	No	No	No
Any other surface perturbation?	No	No	No	No	No	No
Are the above surface perturbations applied to the Control forecast?	No	No	No	No	No	No
Additional comments	None	None	None	None	None	None
6. Other details of the models
Description of model grid	Linear grid	Quadratic grid	Linear grid	Linear grid	Linear grid	Linear grid
List of model levels in appropriate coordinates	See appendix	See appendix	See appendix	See appendix	See appendix	http://jra.kishou.go.jp/JRA-55/document/JRA-55_handbook_TL319_v2_en.pdf
What kind of large scale dynamics is used?	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian
What kind of boundary layer parameterization is used?	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2022)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2022)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2019)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2019)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, Yonehara et al. (2014)	Mellor and Yamada level 2.5
What kind of convective parameterization is used?	Arakawa and Schubert (1974), Tokioka et al. (1988), Bechtold et al. (2008), Komori et al. (2020), JMA (2022)	Arakawa and Schubert (1974), JMA (2022)	Arakawa and Schubert (1974), JMA (2019)	Arakawa and Schubert (1974), JMA (2019)	Arakawa and Schubert (JMA 2013), Yonehara et al. (2014), Yonehara et al. (2017)	Arakawa and Schubert (JMA 2013)
What kind of large-scale precipitation scheme is used?	Sundqvist (1978), JMA (2022)	Sundqvist (1978), JMA (2022)	Sundqvist (1978), JMA (2019)	Sundqvist (1978), JMA (2019)	Sundqvist (1978), Yonehara et al. (2017)	Sundqvist (1978)
What cloud scheme is used?	Smith (1990), Kawai et al. (2017), Chiba and Kawai (2021)	Smith (1990), Kawai and Inoue (2006), JMA (2022)	Smith (1990), Kawai and Inoue (2006), JMA (2019)	Smith (1990), Kawai and Inoue (2006), JMA (2019)	Smith (1990), Kawai and Inoue (2006), Yonehara et al. (2017)	Smith (1990), Kawai and Inoue (2006), Yonehara et al. (2017)
What kind of land-surface scheme is used?	JMA-SIB, JMA (2022), Yonehara et al. (2020)	JMA-SIB, JMA (2022), Yonehara et al. (2020)	JMA-SIB, JMA (2019), Yonehara et al. (2020)	JMA-SIB, JMA (2019), Yonehara et al. (2020)	JMA-SIB, Yonehara et al. (2017)	SiB (Sato et al. 1989)
How is radiation parametrized?	Longwave radiation: JMA (2022) Shortwave radiation: JMA (2022)	Longwave radiation: JMA (2022) Shortwave radiation: JMA (2022)	Longwave radiation: JMA (2019) Shortwave radiation: JMA (2019)	Longwave radiation: JMA (2019) Shortwave radiation: JMA (2019)	Longwave radiation: Yabu (2013) Shortwave radiation: JMA (2013), Nagasawa (2012)	Outline of the operational numerical weather prediction at the Japan Meteorological Agency
7. Re-forecast configuration
Number of years covered	30 years (1991-2020)	30 years (1991-2020)	40 years (1981-2020)	30 years (1981-2010)	32 years (1981-2012)	30 years (1981-2010)
Produced on the fly or fix re-forecasts?	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance
Frequency	2 start dates lagged by 15 days	The re-forecasts consist of a 13-member ensemble starting the 15th and the last dates of calendar months.	The re-forecasts consist of a 13-member ensemble starting the 15th and the last dates of calendar months.	The re-forecasts consist of a 13-member ensemble starting the 15th and the last dates of calendar months.	The re-forecasts consists of a 5 member ensemble starting the 10th, 20th, the last dates of calendar months.	The re-forecasts consists of a 5 member ensemble starting the 10th, 20th, the last dates of calendar months.
Ensemble size	5 members	13 members	13 members	13 members	5 members	5 members
Initial conditions	JRA-3Q (TL479L100) + land surface values estimated with the land-surface model in the CPS3 using atmospheric forcing from JRA-3Q + MOVE-G3 ocean initial conditions (0.25 degree)	JRA-3Q (TL479L100) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-3Q	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + JRA-55 land analysis (TL319)
Is the model physics and resolution the same as for the real-time forecasts	Yes	Yes	Yes	Yes	Yes	Yes
If not, what are the differences	N/A	N/A	N/A	N/A	N/A	N/A
Is the ensemble generation the same as for real-time forecasts?	Yes	No	No	No	No	Yes, except for lagged average forecasting.
If not, what are the differences		LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	N/A
Other relevant information	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 5-member ensemble running twice a month from 1991 to 2020. The start dates are the following list. 16/31 January - 10/25 February - 12/27 March - 11/26 April - 16/31 May - 15/30 June - 15/30 July - 14/29 August - 13/28 September - 13/28 October - 12/27 November and 12/27 December 1991-2020 The S2S database contains the complete JMA re-forecast dataset. The JMA re-forecasts are archived in the S2S database with 2 date attributes: hdate which corresponds to the actual starting date of the re-forecast date which corresponds to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20220930. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 13-member ensemble running twice a month from 1991 to 2020. The start dates correspond to 1st and 16st of each month at 00Z minus 12 hours (28 February instead of 29 February). Here is the complete list of re-forecast start dates: 15/31 January - 15/28 February - 15/31 March - 15/30 April - 15/31 May - 15/30 June - 15/31 July - 15/31 August - 15/30 September - 15/31 October - 15/30 November and 15/31 December 1991-2020 The S2S database contains the complete JMA re-forecast dataset. The JMA re-forecasts are archived in the S2S database with 2 date attributes: hdate which corresponds to the actual starting date of the re-forecast date which corresponds to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20220331. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.
Description of model grid	Linear grid	Linear grid	Linear grid	Linear grid
List of model levels in appropriate coordinates	See appendix	See appendix	See appendix	http://jra.kishou.go.jp/JRA-55/document/JRA-55_handbook_TL319_v2_en.pdf
What kind of large scale dynamics is used?	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian	Spectral semi-lagrangian
What kind of boundary layer parameterization is used?	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2019)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, JMA (2019)	Mellor and Yamada level 2 and diffusive coefficients based on Han and Pan (2011) for stable BL, Yonehara et al. (2014)	Mellor and Yamada level 2.5
What kind of convective parameterization is used?	Arakawa and Schubert (1974), JMA (2019	Arakawa and Schubert (1974), JMA (2019)	Arakawa and Schubert (JMA 2013), Yonehara et al. (2014), Yonehara et al. (2017)	Arakawa and Schubert (JMA 2013)
What kind of large-scale precipitation scheme is used?	Sundqvist (1978), JMA (2019)	Sundqvist (1978), JMA (2019)	Sundqvist (1978), Yonehara et al. (2017)	Sundqvist (1978)
What cloud scheme is used?	Smith (1990), Kawai and Inoue (2006), JMA (2019)	Smith (1990), Kawai and Inoue (2006), JMA (2019)	Smith (1990), Kawai and Inoue (2006), Yonehara et al. (2017)	Smith (1990), Kawai and Inoue (2006), Yonehara et al. (2017)
What kind of land-surface scheme is used?	JMA-SIB, JMA (2019), Yonehara et al. (2020)	JMA-SIB, JMA (2019), Yonehara et al. (2020)	JMA-SIB, Yonehara et al. (2017)	SiB (Sato et al. 1989)
How is radiation parametrized?	Longwave radiation: JMA (2019) Shortwave radiation: JMA (2019)	Longwave radiation: JMA (2019) Shortwave radiation: JMA (2019)	Longwave radiation: Yabu (2013) Shortwave radiation: JMA (2013), Nagasawa (2012)	Outline of the operational numerical weather prediction at the Japan Meteorological Agency
7. Re-forecast configuration
Number of years covered	40 years (1981-2020)	30 years (1981-2010)	32 years (1981-2012)	30 years (1981-2010)
Produced on the fly or fix re-forecasts?	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance	Fixed re-forecasts in advance
Frequency	The re-forecasts consist of a 13-member ensemble starting the 15th and the last dates of calendar months.	The re-forecasts consist of a 13-member ensemble starting the 15th and the last dates of calendar months.	The re-forecasts consists of a 5 member ensemble starting the 10th, 20th, the last dates of calendar months.	The re-forecasts consists of a 5 member ensemble starting the 10th, 20th, the last dates of calendar months.
Ensemble size	13 members	13 members	5 members	5 members
Initial conditions	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + land surface values estimated with the land-surface model in the Global EPS using atmospheric forcing from JRA-55	JRA-55 (TL319L60) + JRA-55 land analysis (TL319)
Is the model physics and resolution the same as for the real-time forecasts	Yes	Yes	Yes	Yes
If not, what are the differences	N/A	N/A	N/A	N/A
Is the ensemble generation the same as for real-time forecasts?	No	No	No	Yes, except for lagged average forecasting.
If not, what are the differences	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	LETKF perturbations are not used and singular vectors (initial SV + evolved SV) are used	N/A
Other relevant information	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 13-member ensemble running twice a month from 1981 to 2020.The start dates correspond to 1st and 16st of each month at 00Z minus 12 hours (28 February instead of 29 February). Here is the complete list of re-forecast start dates: 15/31 January - 15/28 February - 15/31 March - 15/30 April - 15/31 May - 15/30 June - 15/31 July - 15/31 August - 15/30 September - 15/31 October - 15/30 November and 15/31 December 1981-2020 The S2S database contains the complete JMA re-forecast dataset. The JMA re-forecasts are archived in the S2S database with 2 date attributes: hdate which corresponds to the actual starting date of the re-forecast date which corresponds to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20210331. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 13-member ensemble running twice a month from 1981 to	2010. The start dates correspond to 1st and 16st of each month at 00Z minus 12 hours (28 February instead of 29 February). Here is the complete list of re-forecast start dates: 15/31 January - 15/28 February - 15/31 March - 15/30 April - 15/31 May - 15/30 June - 15/31 July - 15/31 August - 15/30 September - 15/31 October - 15/30 November and 15/31 December 1981-2010 The S2S database contains the complete JMA re-forecast dataset. The JMA re-forecasts are archived in the S2S database with 2 date attributes: hdate which corresponds to the actual starting date of the re-forecast date which corresponds to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20200331. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented. The JMA real-time ensemble forecasts include 4 start dates (Tuesdays at 00Z and 12Z + Wednesdays at 00Z + 12Z). The ensemble size is 13 members for Tuesdays 12Z and Wednesdays 00 and 12Z and 11 members for Tuesdays 00Z. For user convenience, ensemble data from 4 start dates are archived as a single 50-member ensemble starting on Wednesdays at 12Z. List of initialize date and index of archived member in final 50 member ensemble are as follows; Wednesday 12 Z : 0,1, …, 12 Wednesday 00 Z : 13,14, …, 25 Tuesday 12 Z : 26, 27, …, 38 Tuesday 00 Z : 39, 40, …, 49 Note that member 0, 13, 26 and 39 are control forecast from each initial date, however, they are encoded as pf for user convenience.	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 5-member ensemble running three times a month from 1981 to 2012. The start dates correspond to 1st / 11th and 21st of each month at 00Z minus 12 hours (28 February instead of 29 February). here is the complete list of re-forecast start dates: 10/20/31 January - 10/20/28 February - 10/20/31 March - 10/20/30 April - 10/20/31 May - 10/20/30 June - 10/20/31 July - 10/20/31 August - 10/20/30 September - 10/20/31 October - 10/20/30 November and 10/20/31 December 1981-2012 The S2S database contains the complete JMA re-forecast dataset. The JMA real-time ensemble forecasts include 4 start dates (Tuesdays at 00Z and 12Z + Wednesdays at 00Z + 12Z). The ensemble size is 13 members for Tuesdays 12Z and Wednesdays 00 and 12Z and 11 members for Tuesdays 00Z. For user convenience, ensemble data from 4 start dates are archived as a single 50-member ensemble starting on Wednesdays at 12Z. List of initialize date and index of archived member in final 50 member ensemble are as follows; Wednesday 12 Z : 0,1, …, 12 Wednesday 00 Z : 13,14, …, 25 Tuesday 12 Z : 26, 27, …, 38 Tuesday 00 Z : 39, 40, …, 49 Note that member 0, 13, 26 and 39 are control forecast from each initial date, however, they are encoded as pf for user convenience. The JMA re-forecasts are archived in the S2S database with 2 date attributes: hdate which corresponds to the actual starting date of the re-forecast date which correspond to the ModelVersionDate. Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20170131. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.	The JMA re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast. The JMA re-forecasts consist of a 5-member ensemble running three times a month from 1981 to 2010. The start dates correspond to 1st / 11th and 21st of each month at 00Z minus 12 hours (28 February instead of 29 February). here is the complete list of re-forecast start dates: 10/20/31 January - 10/20/28 February - 10/20/31 March - 10/20/30 April - 10/20/31 May - 10/20/30 June - 10/20/31 July - 10/20/31 August - 10/20/30 September - 10/20/31 October - 10/20/30 November and 10/20/31 December 1981-2010 The S2S database contains the complete JMA re-forecast dataset. As for the other models, JMA re-forecasts are archived in

8. References

the S2S database with 2 date attributes:

hdate which corresponds to the actual starting date of the re-forecast
date which correspond tot he ModelVersionDate.Since the JMA re-forecasts are "fixed" re-forecasts this ModelVersiondate is the same for all the re-forecasts and equal to 20140304. This variable will change when a new version of the JMA model for extended-range forecasts will be implemented.

8. References

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Chiba, J. and H. Kawai, 2021: Improved SST-shortwave radiation feedback using an updated stratocumulus parameterization. WGNE blue book, Res. Activ. Earth Sys. Modell., 51, 4–03Arakawa, A. and W. H. Schubert, 1974: Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I. J. Atmos. Sci., 31, 674-701.
Dorman, J. L. and P. J. Sellers, 1989: A global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the simple biosphere model (SiB). J. Appl.Meteor., 28, 833–855.Han
Fujii, JY. and H-L, Pan. 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather and Forecasting, 26.4, 520-533.
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Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya,H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan. 93, 5-48.
Lott, F. and M.J. Miller, 1997: A new subgrid-scale orographic drag parameterization: Its formulation and testing. Quart. J. Roy. Meteor. Soc., 123, 101–127.
Ochi, K., 2020: Preliminary results of soil moisture data assimilation into JMA Global Analysis. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 50, 1.15-1.16.
Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989a: Effects of implementing the simple biosphere model (SiB) in a general circulation model. J. Atmos. Sci., 46, 2757–2782.
Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989b: Implementing the simple biosphere model (SiB) in a general circulation model: Methodologies and results. NASA contractor Rep. 185509, NASA. 76pp.
Scinocca, J. F. 2003: An accurate spectral nonorographic gravity wave drag parameterization for general circulation models. J. Atmos. Sci., 60(4), 667-682.
Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43, 505–531.
Simmons, A. J. and D. M. Burridge, 1981: An Energy and Angular-Momentum Conserving Vertical Finite-Difference Scheme and Hybrid Vertical Coordinates. Mon. Wea. Rev., 109, 758-766.
Takakura, T., and T. Komori, 2020: Two-tiered sea surface temperature approach implemented to JMA’s Global Ensemble Prediction System, CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 50, 6.15-6.16.
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, accepted.
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Hirahara, S., Y. Kubo, T. Yoshida, T. Komori, J. Chiba, T. Takakura, T. Kanehama, R. Sekiguchi, K. Ochi, H. Sugimoto, Y. Adachi, I. Ishikawa, and Y. Fujii, 2023: Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 3 (JMA/MRI-CPS3). J. Meteor. Soc. Japan, accepted.
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Japan Meteorological Agency, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency.
Kawai, H., T. Koshiro, and M. J. Webb, 2017: Interpretation of factors controlling low cloud cover and low cloud feedback using a unified predictive index. J. Climate, 30, 9119–9131.
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, WMO/WCRP, O4–2, available at https://symp-bonn2021.sciencesconf.org/data/355900.pdf.
Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya,H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan. 93, 5-48.
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Appendix. Hybrid coordinates

Model level fields are produced for 128 hybrid levels. Each hybrid level is defined with half-levels as the boundary;

where is the surface pressure. Coefficients A and B are given in Table A for k = 0, 1, 2, …, 128. The following equation by Simmons and Burridge (1981) gives full-level pressure;

where C=1 and k=1, 2, …, 127. The full-level pressure for the uppermost level (k=128) is given by

Table A gives half-level and full-level pressures with a surface pressure of 1000hPa.

Table A. Model level from 1 to 128.

Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989a: Effects of implementing the simple biosphere model (SiB) in a general circulation model. J. Atmos. Sci., 46, 2757–2782.
Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y-T Hou, and E. Albertazzi, 1989b: Implementing the simple biosphere model (SiB) in a general circulation model: Methodologies and results. NASA contractor Rep. 185509, NASA. 76pp.
Scinocca, J. F. 2003: An accurate spectral nonorographic gravity wave drag parameterization for general circulation models. J. Atmos. Sci., 60(4), 667-682.
Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43, 505–531.
Simmons, A. J. and D. M. Burridge, 1981: An Energy and Angular-Momentum Conserving Vertical Finite-Difference Scheme and Hybrid Vertical Coordinates. Mon. Wea. Rev., 109, 758-766.
Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435–460.
Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104, 677–690.
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Tokioka, T., K. Yamazaki, A. Kitoh, and T. Ose,1988: The equatorial 30-60 day oscillation and the Arakawa-Schubert penetrative cumulus parameterization. J. Meteor. Soc. Japan, 66, 883–901.
Tsujino, H., H. Nakano, K. Sakamoto, S. Urakawa, M. Hirabara, H. Ishizaki, and G. Yamanaka, 2017: Reference manual for the Meteorological Institute Community Ocean Model version 4 (MRI.COMv4), Technical Reports of the Meteorological Research Institute, 80, doi:10.11483/mritechrepo.80.
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. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 50, 6.19-6.20.

Appendix. Hybrid coordinates

Model level fields are produced for 100 hybrid levels. Each hybrid level is defined with half-levels 𝑝_𝑘_{+ 1} as the boundary;

𝑝_𝑘_{+ 1/2} = 𝐴_𝑘_{+ 1/2} + 𝐵_𝑘_{+ 1/2} 𝑝_s

where 𝑝_s is the surface pressure. Coefficients A and B are given in Table A for k = 0, 1, 2, …, 100. The following equation by Simmons and Burridge (1981) gives full-level pressure;

𝑝_𝑘 = 𝑒𝑥𝑝 [ 1 /𝛥𝑝_𝑘 (𝑝_𝑘_{− 1/2} 𝑙𝑛 𝑝_𝑘_{− 1/2} − 𝑝_𝑘_{+ 1/2} 𝑙𝑛 𝑝_𝑘_{+ 1/2} ) − 𝐶]

where C=1 and k=1, 2, …, 99. The full-level pressure for the uppermost level (k=100) is given by

𝑝₁₀₀ = 1/2 𝑝_99.5.

Table A gives half-level and full-level pressures with a surface pressure of 1000hPa.

Table A. Model level from 1 to 120.

128129

k	A[Pa]	B	Ph [Pa]	Pf [Pa]
1	0.000000000000	1.000000000000	100000.000000000000	99904.290840579200
2	0.381960202384	0.998082302745	99808.612234744800	99670.173246024500
3	2.282910582686	0.995295154130	99531.798323590500	99347.919685101000
4	7.263029910790	0.991568913910	99164.154420887900	98932.524217222700
5	17.501408483548	0.986835732358	98701.074644256400	98419.772700848500
6	35.837785954245	0.981029007209	98138.738506895400	97806.231077202200
7	65.788528045194	0.974083114968	97474.100024808800	97089.234993263200
8	111.534392415342	0.965933434382	96704.877830603100	96266.880120736900
9	177.878399880397	0.956516672775	95829.545677392600	95338.012570768100
10	270.172962859622	0.945771497843	94847.322747197800	94302.218827432700
11	394.216325080918	0.933639468705	93758.163195576900	93159.814637500400
12	556.119328049108	0.920066250467	92562.744374765500	91911.832303038100
13	762.144528509426	0.905003086587	91262.453187183600	90560.005835827300
14	1018.520729177840	0.888408493058	89859.370034940100	89106.753449905900
15	1331.237027835890	0.870250128253	88356.249853163600	87555.156896884000
16	1705.821506076210	0.850506782430	86756.499749027800	85908.937189891400
17	2147.110630500550	0.829170421862	85064.152816714400	84172.426318215500
18	2659.016282299730	0.806248214805	83283.837762814500	82350.534627023000
19	3244.298018195740	0.781764460392	81420.744057349000	80448.713625802900
20	3904.348647413270	0.755762337749	79480.582422272000	78472.914092492300
21	4639.001437858670	0.728305391427	77469.540580591400	76429.539458441800
22	5446.367197228410	0.699478671155	75394.234312718700	74325.394586804000
23	6322.709077375450	0.669389449218	73261.653999201500	72167.630192822300
24	7262.362201659950	0.638167447649	71079.106966589700	69963.683291679100
25	8257.704110039350	0.605964519813	68854.156091381300	67721.214196734600
26	9299.180569138790	0.572953746817	66594.555250834700	65448.040719169400
27	10375.389539015100	0.539327927949	64308.182333870500	63152.070338304300
28	11473.224080521700	0.505297465547	62002.970635264500	60841.231211752800
29	12578.072802905200	0.471087667443	59686.839547171500	58523.402974039900
30	13674.074183704900	0.436935513458	57367.625529535900	56206.348326637900
31	14744.418848362500	0.403085955334	55053.014381784300	53897.646448443100
32	15771.691788626800	0.369787840613	52750.475849926500	51604.629252389200
33	16738.244640660300	0.337289569439	50467.201584557700	49334.321479924800
34	17626.586642451900	0.305834607737	48210.047416192100	47093.385561382200
35	18419.781837842800	0.275656989982	45985.480836070300	44888.072078245200
36	19101.839561775500	0.246976949037	43799.534465497900	42724.176546770400
37	19658.085271638000	0.219996808968	41657.766168447000	40607.003104238100
38	20075.526860069500	0.194896994550	39565.226315109000	38541.335525382200
39	20348.203855336900	0.171782286883	37526.432543591400	36531.415831917500
40	20482.864348214500	0.150624878506	35545.352198787400	34580.930588915300
41	20488.765176917900	0.131366272807	33625.392457601600	32693.004813927300
42	20375.969116023400	0.113934288681	31769.397984114500	30870.203263861700
43	20155.124803274900	0.098245309992	29979.655802511500	29114.538716257400
44	19837.251425764500	0.084206555089	28257.906934664100	27427.486730006300
45	19433.533168591100	0.071718310588	26605.364227357400	25810.006259845500
46	18955.127764879100	0.060676079296	25022.735694518300	24262.565411487900
47	18412.992715443800	0.050972599092	23510.252624625100	22785.171561890400
48	17817.731910349000	0.042499697435	22067.701653843300	21377.405032399900
49	17179.464524154800	0.035149954572	20694.459981371100	20038.455490812100
50	16507.717210577500	0.028818156934	19389.532904015600	18767.160270414400
51	15811.339825110100	0.023402530452	18151.592870315500	17562.043827637700
52	15098.444184614000	0.018805751134	16979.019298027200	16421.357612071200
53	14376.364752906400	0.014935737065	15869.938459396100	15343.119690217800
54	13651.639635522500	0.011706231774	14822.262812901500	14325.153543749600
55	12930.009882453200	0.009037193620	13833.729244465200	13365.125550654600
56	12216.434835214000	0.006855009366	12901.935771811700	12460.580749913600
57	11515.121108602900	0.005092552507	12024.376359264000	11608.976583855000
58	10829.562757481700	0.003689108260	11198.473583477900	10807.714403949500
59	10162.590230693600	0.002590187498	10421.608980493000	10054.168612764700
60	9516.425841105990	0.001747251473	9691.150988404960	9345.713394664660
61	8892.743664671200	0.001117368110	9004.480475650450	8679.747058445850
62	8292.732003956980	0.000662819064	8359.013910400600	8053.714074693860
63	7717.156794907570	0.000350674853	7752.224280169770	7465.124937566750
64	7166.424582956140	0.000152353280	7181.659910971780	6911.574013559450
65	6640.643930896120	0.000043174299	6644.961360805330	6390.755557203870
66	6139.684332954040	0.000001922387	6139.876571614700	5900.478074345070
67	5664.274455875150	0.000000000000	5664.274455875150	5438.677196337180
68	5216.157067389680	0.000000000000	5216.157067389680	5003.427192121430
69	4793.670459729050	0.000000000000	4793.670459729050	4592.951188831610
70	4395.114269365740	0.000000000000	4395.114269365740	4205.630095053620
71	4018.949974054140	0.000000000000	4018.949974054140	3840.010124851320
72	3663.807671750400	0.000000000000	3663.807671750400	3494.808707459600
73	3328.491104611250	0.000000000000	3328.491104611250	3168.918441738340
74	3011.980522341890	0.000000000000	3011.980522341890	2861.408623813800
75	2713.432848941730	0.000000000000	2713.432848941730	2571.523752587070
76	2432.178500685180	0.000000000000	2432.178500685180	2298.678317361130
77	2167.714119867010	0.000000000000	2167.714119867010	2042.447116130520
78	1919.690462218170	0.000000000000	1919.690462218170	1802.550367850080
79	1687.894733586160	0.000000000000	1687.894733586160	1578.832995713020
80	1472.226842483650	0.000000000000	1472.226842483650	1371.237698850850
81	1272.669345516910	0.000000000000	1272.669345516910	1179.771818607930
82	1089.251329298170	0.000000000000	1089.251329298170	1004.468550946980
83	922.007094507183	0.000000000000	922.007094507183	845.343744829332
84	770.931257919772	0.000000000000	770.931257919772	702.350312854100
85	635.932704095892	0.000000000000	635.932704095892	575.333082759937
86	516.790597980398	0.000000000000	516.790597980398	463.987615559623
87	413.116273736551	0.000000000000	413.116273736551	367.826956159841
88	324.325080743701	0.000000000000	324.325080743701	286.160302444110
89	249.622034667881	0.000000000000	249.622034667881	218.087038143596
90	188.004271574095	0.000000000000	188.004271574095	162.508397634113
91	138.281806013939	0.000000000000	138.281806013939	118.157251081607
92	99.116049913469	0.000000000000	99.116049913469	83.644292565773
93	69.073210161771	0.000000000000	69.073210161771	57.516604928473
94	46.687438729531	0.000000000000	46.687438729531	38.322593975469
95	30.526924411285	0.000000000000	30.526924411285	24.676084366670
96	19.255421744700	0.000000000000	19.255421744700	15.312305272479
97	11.682279666395	0.000000000000	11.682279666395	9.129700358650
98	6.795855105220	0.000000000000	6.795855105220	5.213807582741
99	3.777949731857	0.000000000000	3.777949731857	2.842405863603
100
k	A[Pa]	B	Ph [Pa]	Pf [Pa]
1	0.000000000000	1.000000000000	100000.000000000000	99906.149328801500
2	0.367279053361	0.998119607566	99812.328035645300	99707.078615143900
3	1.651295433209	0.996002149193	99601.866214743700	99480.841827595100
4	4.263935162251	0.993556025633	99359.866498482900	99219.297574726400
5	8.818642865415	0.990699763605	99078.795003366000	98915.476159705600
6	16.153564274720	0.987360935873	98752.247151551600	98563.497767488800
7	27.352655050102	0.983475161368	98374.868791829000	98158.495693304200
8	43.760653705889	0.978985208099	97942.281463645000	97696.543075913600
9	66.989186167242	0.973840213666	97451.010552792600	97174.582116438100
10	98.912437237234	0.967995031094	96898.415546672800	96590.355173546100
11	141.651809373079	0.961409701475	96282.621956901400	95942.337440485500
12	197.549799934382	0.954049049521	95602.454752054800	95229.671150837900
13	269.133969108446	0.945882393768	94857.373345948900	94452.101429317500
14	359.072347557138	0.936883359805	94047.408328036600	93609.914021442900
15	470.121954299976	0.927029782579	93173.100212176600	92703.875201491000
16	605.072274485724	0.916303682526	92235.440527052200	91735.174189560300
17	766.685600539283	0.904691299836	91235.815584124600	90705.368407497400
18	957.636088881686	0.892183171556	90175.953244477800	89616.331877189700
19	1180.449250079540	0.878774237209	89057.872970937400	88470.207023681400
20	1437.443395530380	0.864463960067	87883.839402250300	87269.360090425000
21	1730.674330567250	0.849256452964	86656.319626988400	86016.340312364600
22	2061.884332303790	0.833160599383	85377.944270597100	84713.842927258300
23	2432.456198180200	0.816190162450	84051.472443213600	83364.676041107800
24	2843.372912386710	0.798363876191	82679.760531518600	81971.731299931800
25	3295.183263110750	0.779705514941	81265.734757235400	80537.958263339100
26	3787.973561424270	0.760243938082	79812.367369670600	79066.342322563600
27	4321.345466897210	0.740013108242	78322.656291106200	77559.885961130400
28	4894.399817081290	0.719052081756	76799.607992665500	76021.593118835800
29	5505.726286743880	0.697404970581	75246.223344854400	74454.456390393700
30	6153.398665018050	0.675120874964	73665.486161433700	72861.446768227600
31	6834.975529818620	0.652253786076	72060.354137414400	71245.505624873700
32	7547.506113219540	0.628862457583	70433.751871486600	69609.538623254500
33	8287.541182649680	0.605010244769	68788.565659596100	67956.411242983100
34	9051.148804124690	0.580764909450	67127.639749119800	66288.945616008800
35	9833.934898763650	0.556198388528	65453.773751564300	64609.918376429900
36	10631.068546365100	0.531386523789	63769.720925242600	62922.059244645800
37	11437.312024228700	0.506408750331	62078.187057363800	61228.050086197400
38	12247.055590877300	0.481347741058	60381.829696701600	59530.524208817900
39	13054.357028916700	0.456289004840	58683.257512887600	57832.065687358200
40	13852.985946051900	0.431320436397	56985.029585744600	56135.208534621000
41	14636.472796443700	0.406531816609	55289.654457338000	54442.435566027300
42	15398.162525485300	0.382014262845	53599.588809964400	52756.176837013500
43	16131.272660216100	0.357859630043	51917.235664546200	51078.807563275800
44	16828.955566715800	0.334159864585	50244.942025257400	49412.645465099300
45	17484.364477778600	0.311006314494	48584.995927132800	47759.947507769900
46	18090.722762945100	0.288489001104	46939.622873350700	46122.906038724200
47	18641.395773489400	0.266695859038	45310.981677293800	44503.644350057200
48	19129.964452997900	0.245711952979	43701.159750914300	42904.211719761400
49	19550.299766124400	0.225618681387	42112.167904844900	41326.578007900800
50	19896.636870744900	0.206492978760	40545.934746732300	39772.627903007100
51	20163.980086975300	0.188403206951	39004.300782028000	38244.154930225900
52	20351.944344566100	0.171370679911	37489.012335655400	36742.855344864500
53	20463.342581962100	0.155383728414	36001.715423361600	35270.322043458200
54	20501.374046673500	0.140425756613	34543.949708016400	33828.038628992400
55	20469.538812267800	0.126476038663	33117.142678580100	32417.373767254300
56	20371.603412767300	0.113510007752	31722.604187988600	31039.575968092000
57	20211.564215127300	0.101499572658	30361.521480941200	29695.768918140300
58	19993.608937901800	0.090413458959	29034.954833767600	28386.947481304800
59	19722.076787294600	0.080217571292	27743.833916544400	27113.974469751500
60	19401.417732755300	0.070875372401	26488.954972838800	25877.578272274200
61	19036.151481018200	0.062348274144	25270.978895384400	24678.351408969000
62	18630.826728478400	0.054596035287	24090.430257184200	23516.750061661900
63	18189.981276200600	0.047577160614	22947.697337580200	22393.094609378300
64	17718.103579492800	0.041249295828	21843.033162334100	21307.571177633000
65	17219.596275363200	0.035569612810	20776.557556335600	20260.234190198700
66	16698.742187492900	0.030495180003	19748.260187780400	19251.009892823500
67	16159.673251338300	0.025983313127	18758.004564073600	18279.700800639100
68	15606.342733896800	0.021991901889	17805.532922819900	17345.991005115600
69	15042.501046123400	0.018479709003	16890.471946455900	16449.452262833600
70	14471.675363861100	0.015406638528	16012.339216706500	15589.550777349100
71	13897.153188406700	0.012733971269	15170.550315345700	14765.654577045400
72	13321.969893417000	0.010424565774	14364.426470857500	13977.041386529500
73	12748.900223548600	0.008443024230	13593.202646582800	13222.906886473900
74	12180.453634514400	0.006755823303	12856.035964764400	12502.373257022700
75	11618.873296265500	0.005331410662	12152.014362450400	11814.497902710800
76	11066.138522444300	0.004140268568	11480.165379269900	11158.282262021800
77	10523.970341313200	0.003154946421	10839.464983428800	10532.680612040700
78	9993.839886724720	0.002350064638	10228.846350531500	9936.608787727700
79	9476.979262550330	0.001702292571	9647.208519692700	9368.952745786510
80	8974.394520051310	0.001190303424	9093.424862452510	8828.576914590710
81	8486.880384221110	0.000794709276	8566.351311844540	8314.332283627920
82	8015.036371079950	0.000497979401	8064.834311166120	7825.064198118570
83	7559.283951848330	0.000284345023	7587.718454119770	7359.619836325270
84	7119.884440234330	0.000139693594	7133.853799600960	6916.855358210030
85	6696.957303929350	0.000051455511	6702.102855046850	6495.642724032220
86	6290.498628904240	0.000008486026	6291.347231482130	6094.876189775000
87	5900.493980737070	0.000000000000	5900.493980737070	5713.478492448980
88	5528.481630297230	0.000000000000	5528.481630297230	5350.406741907360
89	5174.285933388900	0.000000000000	5174.285933388900	5004.658036279840
90	4836.925350720490	0.000000000000	4836.925350720490	4675.274815057110
91	4515.466275302810	0.000000000000	4515.466275302810	4361.349956723390
92	4209.028002503950	0.000000000000	4209.028002503950	4062.031616233200
93	3916.787433562190	0.000000000000	3916.787433562190	3776.527781221010
94	3637.983481907540	0.000000000000	3637.983481907540	3504.110504425720
95	3371.921127743180	0.000000000000	3371.921127743180	3244.119743480640
96	3117.975037623500	0.000000000000	3117.975037623500	2995.966708364220
97	2875.592632860050	0.000000000000	2875.592632860050	2759.136582338770
98	2644.296454685800	0.000000000000	2644.296454685800	2533.190445678590
99	2423.685637127690	0.000000000000	2423.685637127690	2317.766195254820
100	2213.436263278170	0.000000000000	2213.436263278170	2112.578220408930
101	2013.300350929100	0.000000000000	2013.300350929100	1917.415570860470
102	1823.103194212330	0.000000000000	1823.103194212330	1732.138341042090
103	1642.738784867620	0.000000000000	1642.738784867620	1556.672003523160
104	1472.163056683520	0.000000000000	1472.163056683520	1390.999458895190
105	1311.384746465990	0.000000000000	1311.384746465990	1235.150637357940
106	1160.453751007800	0.000000000000	1160.453751007800	1089.189593919690
107	1019.446986748220	0.000000000000	1019.446986748220	953.199187899828
108	888.451928822532	0.000000000000	888.451928822532	827.263627663412
109	767.548215932542	0.000000000000	767.548215932542	711.449386955591
110	656.787947376753	0.000000000000	656.787947376753	605.785246379858
111	556.175551249932	0.000000000000	556.175551249932	510.242460795943
112	465.648342355864	0.000000000000	465.648342355864	424.716270813441
113	385.059081069088	0.000000000000	385.059081069088	349.010127626377
114	314.161951254557	0.000000000000	314.161951254557	282.824045691573
115	252.603356889854	0.000000000000	252.603356889854	225.748400577188
116	199.918760089339	0.000000000000	199.918760089339	177.264223682977
117	155.536429527503	0.000000000000	155.536429527503	136.750604725015
118	118.788443103480	0.000000000000	118.788443103480	103.499217296971
119	88.928627808076	0.000000000000	88.928627808076	76.735284754437
120	65.156391730376	0.000000000000	65.156391730376	55.643586362110
121	46.644705017873	0.000000000000	46.644705017873	39.397472794456
122	32.569931284291	0.000000000000	32.569931284291	27.188427315847
123	22.140906946455	0.000000000000	22.140906946455	18.253569151567
124	14.624692644256	0.000000000000	14.624692644256	11.898702444718
125	9.366805063801	0.000000000000	9.366805063801	7.515060935547
126	5.804438422468	0.000000000000	5.804438422468	4.588710053849
127	3.472094398676	0.000000000000	3.472094398676	2.702510420768
	2.000000000000	0.000000000000	2.000000000000	1.000000000000
101	0.000000000000	0.000000000000	0.000000000000

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Versions Compared

Old Version 3

New Version Current

Key

1. Ensemble version

2. Configuration of the EPS

3. Initial conditions and perturbations

4. Model uncertainties perturbations

5. Surface boundary perturbations

6. Other details of the models

4. Model uncertainties perturbations

5. Surface boundary perturbations

6. Other details of the models

7. Re-forecast configuration

7. Re-forecast configuration

8. References

8. References

Appendix. Hybrid coordinates

Appendix. Hybrid coordinates