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Realization of the National QPF Master Blender: A Big Data Approach

  • Jian TangEmail author
  • Kan DaiEmail author
  • Zhiping Zong
  • Yong Cao
  • Couhua Liu
  • Song Gao
  • Chao Yu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

With the development of the weather forecast modernization, forecasters are facing challenges brought by the explosion of meteorological data, the increasing demand of service front-end and the wide use of objective forecasting technology. Tradition quantitative precipitation forecast (QPF) routine, which is mainly based on manually plotting of precipitation areas, can no longer assist forecasters in demonstrating added value at a higher level. To support the forecasters’ central role in the QPF routine, a subjective and objective QPF blender has been designed and developed. This platform helps forecasters to take control of the whole forecast process with the following five steps: selection from massive forecast data, integration of multi-source QPF, adjustment and correction of QPF, grid processing and service product production. The intelligence of the platform is secured by the development of a number of key supporting techniques. Based on the Meteorological Information Comprehensive Analysis and Processing System Version 4 (MICAPS4), the main functions of this QPF platform are realized. The “QPF Master Blender 1.0” version was released and put into operational use in May 2017, which has yielded good feedback and effectiveness. Based on different weather forecast scenarios, five stages of work mode are provided, which are demonstrated by corresponding examples and verifications. At the end of this paper, the future development of the platform is prospected, including the development of numerical model verification tools and the research on the fusion technologies of multi-scale model information.

Keywords

Gridded quantitative precipitation forecast Big data Intelligent forecast Weather forecast scenarios 

Notes

Acknowledgment

This paper was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China under grant No. 2017YFC1502004 and the Key Project in the National Science & Technology Pillar Program of China under grant No. 2015BAC03B01.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Meteorological Center of CMABeijingChina

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