The APCC seasonal forecast is based on multi-model ensemble (MME) prediction system and disseminated to APEC member economics around 15th of every month. Currently, 15 operational centers and research institutes from 11 countries around the world participate in the APCC MME operational prediction system by routinely providing their predictions in the form of ensembles of global forecast fields. The APCC's real-time operational forecasts are issued in both deterministic (based on ensemble mean) and probabilistic (based on full set of ensemble members) forms and more detailed description of the methods is as follows.

1. Deterministic MME Forecast

   The deterministic forecast is based on a simply average of bias-corrected ensemble means from each model with equal weight to create a multi-model forecast. The ensemble mean anomaly forecasts for each individual model is calculated by their own climatology from the hindcasts.
   
   

2. Probabilistic MME Forecast

   The probabilistic forecast is based on an uncalibrated MME with model weights being proportional to the square root of ensemble size, and a Gaussian fitting method for the estimation of the tercile-based categorical probabilities, that is, the probability of below-normal (BN), near-normal (NN), and above-normal (AN) categories with respect to climatology (Min et al. 2009). The procedure for the probabilistic forecast consists of following two steps.
   
   

   • Estimate the individual model probabilities

     The upper and lower terciles are determined separately for each model using their mean and standard deviation of hindcasts. Then, the forecast probability for each category is estimated as a portion of the cumulative probability of their forecast sample associated with the category.

     
     
     
   • Multi-model combination

     The forecast probabilities for each model are averaged together with model weights being inversely proportional to the random errors in the forecast probability associated with the standard error of the ensemble mean (i.e., proportional to the square root of ensemble size) to create a probabilistic multi-model ensemble forecast.

     
     

MME participating models

Table 1. Organization

Center/Institution	Country	System name
APCC	Korea	SCoPS
BCC	China	BCC_CSM1.1m
BoM	Australia	ACCESS-S2
CMCC	Italy	CMCC-SPS3.5
CWA	Chinese Taipei	TCWA1Tv1.1
ECCC	Canada	CANSIPSv3
HMC	Russia	SL-AV
JMA	Japan	MRI-CPS3
KMA	Korea	GloSea6GC3.2
METFR	France	SYS8
MGO	Russia	MGOAM2.4
NASA	United States of America	GEOS-S2S-2.1
NCEP	United States of America	CFSv2
PNU-RDA	Korea	PNU-RDA CGCMv2.0
UKMO	United Kingdom	GloSea6

List of parameters

Table 2. Parameter name

Name	Abbreviation for file name	Unit
Precipitation	prec	mm day-1
Sea level pressure	slp	hPa
Sea surface temperature	sst	K
Temperature at 2m	t2m	K
Temperature at 850hPa	t850	K
Zonal wind at 200hPa	u200	m s-1
Zonal wind at 850hPa	u850	m s-1
Meridional wind at 200hPa	v200	m s-1
Meridional wind at 850hPa	v850	m s-1
Geopotential height at 500hPa	z500	m

[Note] available parameters depends on models.

Temporal Coverage and resolution

Monthly and seasonal data up to 6 months lead time
*Lead time means the length of time between the issuance of a forecast and the occurrence of the phenomena that were predicted.

Spatial coverage and resolution

Global data whose resolution is 2.5^°×2.5^°

Data Format

NetCDF(CF-1.4)

More information on each model and MME methods is available at:  Model Description ,  Methodolgy,   Outlook Summary

References

1. (Probabilistic Forecast) Min, Y.-M., V. N. Kryjov, C.K. Park, 2009: Probabilistic Multimodel Ensemble Approach to Seasonal Prediction. Weather and Forecasting, 24, 812-828
2. (APCC MME Forecast) Min, Y. M., V. N. Kryjov, S. M. Oh, and H. J. Lee, 2017: Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015. Clim. Dyn., 49, 4141-4156
3. (APCC MME Forecast) Min, Y. M., V. N. Kryjov, and S. M. Oh, 2014: Assessment of APCC multi-model ensemble prediction in seasonal climate forecasting: Restrospective (1983-2003) and real-time forecast (2008-2013). J. Geophys. Res., 119, 12, 132