Revised Test of Precipitation Forecasting Product of RMAPS-ST System Based on U-Net
DOI:
https://doi.org/10.14738/aivp.121.16248Keywords:
Gridded data, Deep learning, Precipitation correction, U-NetAbstract
Using the U-Net algorithm idea in the field of artificial intelligence, a deep learning framework is designed and the loss function is redefined. This learning framework revised the precipitation forecast results of RMAPS-ST after training on two summer samples. Approved the revised precipitation forecast for June-July 2022 and precipitation on August 18, 2022, The test results show that the revised forecast level is significantly improved, especially for magnitude greater than 0.1mm/3 hours, and the CSI score is increased by an average of 30% compared with before revision.
References
Guo Hanyang, Chen Mingxuan, Han Lei, et al. 2019. Strong convective high-resolution nowcasting based on deep learning Experiment. Acta Meteorological Sinica,77(4):715-727
Zhang Yanbiao, Chen Mingxuan, Han Lei, et al., 2022 Multi factor depth learning fusion correction method for numerical weather forecast. Journal of Meteorology, 80 (1): 153-167QIAN Weihong. 2012. How to improve the skills of weather forecasting and climate prediction. Chinese Journal of Geophysics,55（5): 1532-1540.
Sun Jian, Cao Zhuo, Li Heng, et al. 2021. Application of artificial intelligence technology in numerical weather prediction. Apply gas Acta Elephant Sinica, 32(1): 1-11
Zeng Xiaoqing, Xue Feng, Zhao Ruixia, et al. 2019. Comparative study of several lattice-pointed temperature rolling revision forecasting schemes Research. Meteorological Meteorology,45(7):1009-1018
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation.2015, https://arxiv.org/abs/1505.04597.
Bauer P, Thorpe A, Brunet G. 2015. The quiet revolution of numerical weather prediction. Nature. 525 (7567): 47-Cho K, van Merriënboer B. Gulcehre C. et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation∥ Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing （EMNLP）. Doha Qatar: Association for Computational Linguistics，1724-1734
Glahn H R. Lowry D A. 1972. The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteor，11 (8): 1203-1211
Goodfellow I J. Pouget-Abadie J. Mirza M. et al. 2014. Generative adversarial nets∥ Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal， Canada： MIT Press， 2672-2680.
Han L. Sun J Z. Zhang W. et al. 2017. A machine learning nowcasting method based on real-time reanalysis data. J Geophys Res: Atmos, 122 (7): 4038-4051
Han L. Chen M X. Chen K K. et al. 2021. A deep learning method for bias correction of ECMWF 24-240 h forecasts. Adv Atmos Sci, 38 (9): 1444-1459
Hersbach H. Bell B. Berrisford P. et al. 2020. The ERA5 global reanalysis. Quart J Roy Meteor Soc. 146 (730): 1999-Homleid M. 1995. Diurnal corrections of short-term surface temperature forecasts using the Kalman filter. Wea Forecast，10 (4): 689-707
Kusiak A, Wei X P, Verma A P. et al. 2013. Modeling and prediction of rainfall using radar reflectivity data: A datamining approach. IEEE Trans Geosci Remote Sens. 51 (4): 2337-2342
Marzban C. 2003. Neural networks for postprocessing model output: ARPS. Mon Wea Rev. 131 (6): 1103-1111
Peng X D, Che Y Z, Chang J. 2013. A novel approach to improve numerical weather prediction skills by using anomaly integration and historical data. J Geophys Res: Atmos. 118 (16): 8814-8826
Rodrigues E R. Oliveira I. Cunha R L F. et al. 2018. Deep Downscale: A deep learning strategy for high-resolution weather forecast∥2018 IEEE 14th International Conference on e-Science (e-Science). Amsterdam Netherlands： IEEE,415-422
Shi X J. Chen Z R. Wang H. et al. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting∥ Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal. Canada: MIT Press，802-810
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Gao Hua, Liang Jie, Bao Dongling, Zaiwen Wang, Yajie Qi, Zheng Zuofang
This work is licensed under a Creative Commons Attribution 4.0 International License.
