[논문]기상청 기후예측시스템(GloSea5)의 과거기후장 앙상블 확대에 따른 예측성능 평가

박연희; 현유경; 허솔잎; 지희숙

doi:10.14191/atmos.2021.31.5.511

기상청 기후예측시스템(GloSea5)의 과거기후장 앙상블 확대에 따른 예측성능 평가
Assessment of the Prediction Performance of Ensemble Size-Related in GloSea5 Hindcast Data 원문보기

대기 = Atmosphere, v.31 no.5, 2021년, pp.511 - 523

박연희 (국립기상과학원 현업운영개발부 기후모델개발팀) , 현유경 (국립기상과학원 현업운영개발부 기후모델개발팀) , 허솔잎 (국립기상과학원 현업운영개발부 기후모델개발팀) , 지희숙 (국립기상과학원 현업운영개발부 기후모델개발팀)

Abstract ▼ AI-Helper

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

주제어

표/그림 (11)

그림 Fig. 1. Comparison of ensemble spread produced by stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. The blue and orange bar indicates SKEB2 and time-lagged ensemble spread, for (a) JJA and (b) DJF during hindcast period (1991~2010).
그림 Fig. 2. RMSE (top) and ACC (bottom) by ensemble size over global, high-, mid-, and low-latitude. The solid and dashed lines represent JJA and DJF, respectively. (OPER: 12-member, EXP: 56-member).
그림 Fig. 3. Improvements of (a) MSLP and (b) precipitation according to the ensemble expansion from 12 to 56 members. Left panel is for JJA and right is DJF (Top panel: RMSE, Bottom panel: ACC).
그림 Fig. 4. 23 regions to assess the impact of ensemble expansion [refer to Giorgi and Francisco (2000)].
그림 Fig. 5. Normalized ACC by regions in Fig. 4 as ensemble size increases during (a) JJA and (b) DJF (HGH: high-latitude, MID: mid-latitude, LOW: low-latitude).
그림 Fig. 6. Ensemble spreads by regions in Fig. 4 during (a) JJA and (b) DJF (HGH: high-latitude, MID: mid-latitude, LOW: low-latitude).
표 Table 1. Optimal number of ensembles based on a normalized ACC of 0.95.
그림 Fig. 7. Ratio of Predictable Components (RPC), Correlation (CORR), and Signal-to-Noise Ratio (SNR) as a function of ensemble size for global mean. Solid and dashed lines represent JJA and DJF, respectively.
그림 Fig. 8. The relationship between RMSE and ensemble spread as a function of ensemble size for global mean. Solid line represents results of a 'perfect' model. Values shown in the plots are the ratio of ensemble spread to RMSE (%).
그림 Fig. 9. (a) RMSE and (b) ACC of 1.5 m temperature as a function of ensemble size for each initial time. Black line shows the result using all initial dates and each color represents each initial time.
그림 Fig. 10. (a) RMSE and (b) ACC of 1.5 m temperature as a function of the number of initial times. Color lines represent the results using 1 to 4 initial dates, based on the targeting forecast date.

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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