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Advances in Earth Science >

2022 , Vol. 37 >Issue 12: 1211 - 1222

DOI: https://doi.org/10.11867/j.issn.1001-8166.2022.096

Research, Development, and Application of the Unified Post-Processing System for the CMA-GEPS/REPS Ensemble Prediction

  • Li GAO ,
  • Jiawen ZHENG ,
  • Zuosen ZHAO ,
  • Yuelin LUO ,
  • Pengfei REN ,
  • Guohua YAO
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  • 1.Ensemble Prediction Division, CMA Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China
    2.Guangzhou Meteorological Service, Guangzhou Meteorological Bureau, Guangzhou 511430, China
    3.College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China
    4.Guangdong Meteorological Service, Guangdong Meteorological Bureau, Guangzhou 510150, China
GAO Li (1978-), female, Alashan Zuoqi, Inner Mongolia Autonomous Region, Professor. Research areas include weather dynamics and ensemble forecasting. E-mail: gaol@cma.gov.cn

Received date: 2022-09-17

  Revised date: 2022-10-25

  Online published: 2022-12-16

Supported by

the National Natural Science Foundation of China “Medium-range weather predictability and ensemble-based probabilistic forecasting method of extreme temperature event in China”(41875138);“Classification interpretation and probability correction methods of ensemble forecasting of summer rainfall in the eastern China”(42175015)

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Abstract

Ensemble prediction, one of the most rapid developments in numerical weather prediction, has presently become a vital basis for accurate forecasting and assessment of product abundance. In the past three decades, accompanied by the rapid developments in prediction research and techniques, a significant progress has been made in operational technology and systems for ensemble prediction. As the output end of the information facing downstream users in the ensemble prediction chain, the post-processing system has been an integrated platform for the generation of numerous ensemble data, the unification of product-making functions, and the intensification of multilevel forecasting approaches and techniques. In this study, a comprehensive local-to-global review was first conducted for the historical development, current stage, and future direction of the post-processing system and technology for ensemble prediction. Second, the following seven main functions of the post-processing system were summarized: ① Standardized output and distribution of ensemble data; ② Calculation of ensemble mean and spread statistics; ③ Analysis of synoptic and climatological diagnostics; ④ Generation and issuance of deterministic and stochastic ensemble prediction products; ⑤ Extraction and interpretation of big ensemble data and information; ⑥ Calibration and improvement of deterministic and stochastic ensemble forecasts; ⑦ User-customized product services and visualization. Finally, the unified post-processing system in the China Meteorological Administration-Global Ensemble Prediction System/Regional Ensemble Prediction System (CMA-GEPS/REPS) was discussed in terms of the above main functions. The focus was on finding ways to make better use of the big ensemble data and information from the CMA-GEPS/REPS real-time forecasts to study. Further, the intent was to develop a variety of new ensemble products, particularly including the extreme forecast index, Madden-Julian oscillation, western-Pacific subtropical high, and south-Asian high, as well as learning to apply them to realistic operational forecasting. Overall, the post-processing technique is becoming a predominant research and development direction, building on the advantages of ensemble prediction ranging from high forecast accuracy to actionable insights with significant social, environmental, and economic benefits.

Key words： CMA-GEPS/REPS; Ensemble prediction; Unified post-processing; Operational system; Forecast products

Cite this article

Li GAO , Jiawen ZHENG , Zuosen ZHAO , Yuelin LUO , Pengfei REN , Guohua YAO . Research, Development, and Application of the Unified Post-Processing System for the CMA-GEPS/REPS Ensemble Prediction[J]. Advances in Earth Science, 2022 , 37(12) : 1211 -1222 . DOI: 10.11867/j.issn.1001-8166.2022.096

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