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Journal of Meteorological Research

, Volume 33, Issue 5, pp 989–992 | Cite as

How Much Can AI Techniques Improve Surface Air Temperature Forecast? —A Report from AI Challenger 2018 Global Weather Forecast Contest

  • Lei Ji
  • Zaiwen WangEmail author
  • Min Chen
  • Shuiyong Fan
  • Yingchun Wang
  • Zhiyuan Shen
News and Views
  • 1 Downloads

Abstract

In August 2018, the Institute of Urban Meteorology (IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest (WFC)—one of the AI (artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than 1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height. Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2% and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.

Key words

artificial intelligence (AI) analog ensemble weather forecast surface meteorological elements AI model 

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Notes

Acknowledgments

We thank Mr. Kaifu Li and Mr. Yonggang Wang, the CEO and CTO of Sinovation Ventures, respectively, and Mr. Zhuohao Wu and Ms. Jing Dong, as well as all their team members who participated in the WFC, for their great support to make the WFC accomplished. We also thank all contestants around the world who enthusiastically dedicated their wisdom to the WFC.

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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Lei Ji
    • 1
  • Zaiwen Wang
    • 1
    Email author
  • Min Chen
    • 1
  • Shuiyong Fan
    • 1
  • Yingchun Wang
    • 1
    • 2
  • Zhiyuan Shen
    • 3
  1. 1.Institute of Urban MeteorologyBeijingChina
  2. 2.Beijing Meteorological ServiceBeijingChina
  3. 3.Sinovation Ventures AI InstituteBeijingChina

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