How Much Can AI Techniques Improve Surface Air Temperature Forecast? —A Report from AI Challenger 2018 Global Weather Forecast Contest
- 1 Downloads
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 wordsartificial intelligence (AI) analog ensemble weather forecast surface meteorological elements AI model
Unable to display preview. Download preview PDF.
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.
- Bi, B. G., 2017: Progresses and thoughts on weather forecasting using artificial intelligence technology. Proc. National Conference of Weather Forecast Center Directors, Yinchuan, China, 12 October 2017. (in Chinese)Google Scholar
- Burrows, W. R., and C. J. Mooney, 2018: Automated products for forecasting arctic blizzard conditions. J36.4 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin, Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper336043.html. Accessed on 16 August 2019.
- Collins, W., M. Prabhat, E. Racah, et al., 2018: Deep learning for detecting extreme weather and climate patterns. TJ7.1 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin. Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper328029.html. Accessed on 16 August 2019.
- Dai, Y., N. He, Z. Y. Fu, et al., 2019: Beijing intelligent grid temperature objective prediction method (BJTM) and verification of forecast result. J. Arid Meteor., 37, 339–344, doi: https://doi.org/10.11755/j.isssn.1006-7639(2019)-02-0339. (in Chinese)Google Scholar
- EarthRisk Technologies, 2013: TempRisk Apollo White Paper. Available at https://doi.org/www.earthrisktech.com/resouces/reports/white_papers/TempRiskApollo_WhitePaper_Oct2013.pdf. Accessed on 16 August 2019.
- Kneringer, P., S. J. Dietz, G. J. Mayr, et al., 2018: An ordered hurdle model for probabilistic low-visibility nowcasting to support decisions at airports. J36.6 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin, Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper325064.html. Accessed on 16 August 2019.
- Kunkel, K. E., J. C. Biard, and E. Racah, 2018: Automated detection of fronts using a deep learning algorithm. TJ7.4 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin. Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper333480.html. Accessed on 16 August 2019.
- Lagerquist, R. A. McGovern, M. B. Richman, et al., 2018: Using machine learning to forecast severe thunderstorm winds on a CONUS-Wide grid. 3.1 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin. Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper335039.html. Accessed on 16 August 2019.
- Mattioli, C. J., M. S. Veillette, and H. Iskenderian, 2018: Dual application of convolutional neural networks: Forecasts of radar precipitation intensity and offshore radar-like mosaics. 695 in Proc. Annual Meeting of the Amer. Meteor. Soc., Austin. Texas, 6–11 January 2018. Available at https://doi.org/ams.confex.com/ams/98Annual/webprogram/Paper323735.html. Accessed on 16 August 2019.
- Wang, Y., M. Long, J. Wang, et al., 2018: PredRNN: Recurrent neural networks for predictive leaning using spatiotemporal LSTMs. Proc. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017. Available at https://doi.org/papers.nips.cc/paper/6689-predrnn-recurrent-neural-networks-for-predictive-learning-using-spatiotemporal-lstms. Accessed on 16 August 2019.
- Yao, Y. C., and Z. J. Li, 2017: Short-term precipitation forecasting based on radar reflectivity images. Proc. International Conference on Information and Knowledge Management, Singapore, 6–10 November 2017. Available at https://doi.org/github.com/yaoyichen/CIKM-Cup-2017/blob/master/CIKM_AnalytiCup_2017_Team_Marmot.pdf. Accessed on 3 August 2019.