Presentation on theme: "KMA will extend medium Range forecast from 7day to 10 day on Oct. 2014 A post processing technique, Ensemble Model Output Statistics (EMOS), was developed."— Presentation transcript:
KMA will extend medium Range forecast from 7day to 10 day on Oct. 2014 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 (firstname.lastname@example.org) 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. 2011 ~ (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. 2011 ~ 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-v0.150.400.600.42 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. 2011 ~ 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. 2013 ~ 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
Your consent to our cookies if you continue to use this website.