APCC MME: Multi-Model Ensemble Forecast (High Resolution) Tutorial

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 1°×1°

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

APCC High Resolution MME Download Tutorial

Acknowledgement

When you use the APCC MME and/or individual model data in any documents or publications, please acknowledge us by including the following text, “The authors acknowledge the APCC MME Producing Centers for making their hindcast/forecast data available for analysis, the APEC Climate Center for collecting and archiving the data, as well as for producing APCC MME predictions.”

MME data download


url: https://download.apcc21.org/MME-HRES/[leadMonth]/[type]/[method]/[month]/[year]/[file name]
             leadMonth: 3-MON, 6-MON
             type: FORECAST
             method: SCM, GAUS
             month: JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC
             year: 2022, 2021,...
             file name: [variable name].nc (prec.nc),
                        variable name : prec, slp, sst, t2m, t850, z500

Sample (MME) :

wget https://download.apcc21.org/MME-HRES/3-MON/FORECAST/SCM/NOV/2022/prec.nc
wget https://download.apcc21.org/MME-HRES/6-MON/FORECAST/GAUS/NOV/2022/prec.nc
Model data download

url: https://sdownload.apcc21.org/MODEL-HRES/[type]/[model]/[month]/[year]/[file name]
             type: FORECAST, HINDCASTyyyy
             model: APCC_SCOPS, BCC_CSM1.1M, BOM_ACCESS-S2, CMCC_SPS3.5, CWA_TCWA1Tv1.1, ECCC_CANSIPSv3, HMC_SL-AV,
                    KMA_GLOSEA6GC3.2, METFR_SYS8, MGO_MGOAM-2, NASA_GEOS-S2S-2.1, NCEP_CFSv2, PNU-RDA_CGCMv2.0, UKMO_GLOSEA6
             month: JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC
             year: FORECAST - 2022, 2021,...
                   HINDCAST - APCC(1983~2013), BCC(1991~2015), BOM(1981~2018), CMCC(1991~2016), CWA(1991~2020),
                              ECCC(1980~2020), HMC(1990~2015), KMA(1991~2016), METFR(1993~2018), MGO(1979~2004),
                              NASA(1981~2016), NCEP(1982~2010), PNU-RDA(1980~2021), UKMO(1993~2016)
             file name: [variable name].nc (prec.nc),
             variable name : prec, slp, sst, t2m, t850, z500

Sample (MODEL):

wget https://sdownload.apcc21.org/MODEL-HRES/FORECAST/APCC_SCOPS/NOV/2022/prec.nc
wget https://sdownload.apcc21.org/MODEL-HRES/HINDCAST2022/METFR_SYS8/NOV/1993/sst.nc
wget https://sdownload.apcc21.org/MODEL-HRES/HINDCAST2023/APCC_SCOPS/JAN/1983/prec.nc
How to use CLIK API 
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': '[timestep]',
                           'level': '[level]',
                           'variable': '[variable name]',
                           'year': '[YYYY]',
                           'month': '[MM]',
          },
          '[file name to save]'
)

timestep: DAILY, MONTHLY, HOURLY
level: pressure level, single level
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'DAILY',
                           'level': 'single level',
                           'variable': 't2m',
                           'year': '2020',
                           'month': '12',
          },
          't2m_202012.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'HOURLY',
                           'level': 'single level',
                           'variable': 'tp',
                           'year': '2020',
                           'month': '12',
          },
          'tp_202012.nc'
)
													
import apccapi

c = apccapi.Client()

c.retrieve(
          {
                 'jobtype': 'ERA5',
                 'dataset': 'ERA5',
                 'timestep': 'MONTHLY',
                           'level': 'single level',
                           'variable': 't2m',
                           'year': '2020',
          },
          't2m_2020.nc'
)