KMA will extend medium Range forecast from 7day to 10 day on Oct A post processing technique, Ensemble Model Output Statistics (EMOS), was developed to remove systematic error of ensemble dynamic model and to provide weather forecaster with accurate guidance with uncertainty. ※ Status of Statistical Guidance in KMA A Study on the Ensemble MOS for Medium Range Prediction in Korea Meteorological Administration JunTae Choi Korean Meteorological Administration, Rep. of Korea Introduction 1 1 Result 4 4 Method 2 2  Assumption - The ensemble mean may represent the property of ensemble prediction  Ensemble MOS’s Equation - Deriving ONE equation for all ensemble members and Applying it to each member  Definition of element and observation data - Daily MAX/MIN temperature : 135 station(synoptic, meta, AWS) - Total cloud amount(12hrly mean) : 45 station(manned synoptic, meta)  NWP data : Ensemble Prediction System for Global(EPSG) - Model : Unified Model introduced from Met Office(N320) - Archived period : Jun ~ (more 3 years) - Ensemble size : 24 members (1 control + 23 perturbed members(ETKF))  Statistical method to derive equation - Multiple Linear Regression(MLR) screened by stepwise selection - Point equation : one equation for one station MLR with Stepwise Statistical Func. Verification of independent variable VIF, t-s, p-v, weight < 1δ of obs. 24 members from EPSG(NWP) 24 ensemble MOS prediction observation Median of 24 members 24 members from EPSG(NWP) projectionNWP modelmethodElementremark short range (upto 3day) Regional Model (12km, 87hr) MOSMax/Min T, Pop, etcMLR, for digital forecast Kalman filterMax/Min/spot T SSPS(UKPP)Spot T, RH, etcmountain, adjust medium range (upto 12day) Global Model (N512, 288hr) MOSMax/Min TMLR Kalman filterMax/Min T EPSG (N320) EMOSMax/Min T, cloudMLR MOS (SVR)Spot TSVR Experiment 3 3  Property of median and control member of EPSG prediction - mean and standard deviation between median of all members and control member are similar (t statistic value is 0.4 for temp and 0.9 for wind)  Eq. from the median can work on prediction of individual member  Enlargement of sample size to derive Eq. - According to above figures(red line), the statistics between D day and D+1 day prediction is very similar ( Apr. 2014)  Eq. for D day prediction is derived with D-1, D and D+1 day prediction data, instead of only D day prediction data  the size of sample to deriving eq. can be triple - RMSE of median of EMOS prediction according to the sampling way (6 fold cross validation, Jun ~ May 2014)  Great improvement in cloud amount prediction, and slight improvement for temperature T sfc RH sfc W. SPD sfc cloud amt two sample t-v Discussion 5 5 Sampling way for D day prediction MAX. Temp.MIN. Temp.cloud amount D day prediction 2.08 ℃ 2.17 ℃ 2.62 D-1, D, D+1 day prediction 2.07 ℃ 2.15 ℃ 2.27   Additional post-process for cloud amount MOS - Theoretically, MOS prediction is closer to climatic value, longer prediction. But cloud 0 and 10 is most frequently observed.  Need post process(Y.K., Seo and J.T. Choi, 2013 ECAM) - post process : Fitting the percentile between MOS and OBS. distribution POST P.  Verification : 6 fold cross validation, Jun ~ May 2014 ※ CRPS : Continuous Rank Probability Score ※ BIAS is calculated with median of EMOS predict - BIAS of EPSG was successfully removed by MOS. - CRPS was decreased 0.6 ℃ for MAX/MIN temp. and 0.6 for cloud, - Spread of EMOS prediction being equal or greater than that of EPSG  Comparing the other models (Jun ~ May 2014, 46 point) GDAPS : Global Model( N512 ) - The MOSs are better than direct output of ECMWF model - Ensemble method is more important than resolution of NWP model  EMOS with simple statistical method can provide reasonable guidance in form of uncertainty.  EMOS could be more accurate than direct output of ensemble model, while its ensemble spread being not reduced.  Ensemble MOS based on low resolution model is better than deterministic style MOS based on high resolution model. 7 day Forecast 10 day Forecast