Combination of XGBoost and PPLK method for improving the precipitation nowcasting

Xiongfa Mai; Haiyan Zhong; Ling Li

doi:10.1051/matecconf/202235503039

All issues

Volume 355 (2022)

MATEC Web Conf., 355 (2022) 03039

References

Open Access

Issue		MATEC Web Conf. Volume 355, 2022 2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)


Article Number		03039
Number of page(s)		5
Section		Computing Methods and Computer Application
DOI		https://doi.org/10.1051/matecconf/202235503039
Published online		12 January 2022

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Yu Liu, Dugang Xi, Zhaoling Li, et al. A new methodology for pixel-quantitative precipitation nowcastingusing a pyramid Lucas Kanade optical flow approach[J]. Journal of Hydrology, 2015,529:354-364. [CrossRef] [Google Scholar]
S. Xingjian, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Neural Information Processing Systems, 2015. [Google Scholar]
Ayzel, G. et al. “RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting.” DOI: 10.5194/gmd-2020-30 (2020). [Google Scholar]
Xiongfa Mai, Haiyan Zhong and Ling Li. “Research on rain or shine weather forecast in precipitation nowcasting based on XGBoost”, 2020 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. 2020.12.12, Xian, China. [Google Scholar]

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[1] Chen Mingxuan, Yu Xiaoding, et al. A brief review on the development of nowcasting for convective storms. Journal of applied meteorolo gical science, 2004,15(6):754-766(in Chinese). [Google Scholar]

[2] JOHNSON, J. T, MAcKEEN, et al. The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorith[J]. Weather & Forecasting, 1998. [Google Scholar]

[3] Rinehart R E, Garvey E T. Three-dimensional storm motion detection by conventional weather radar[J]. Nature, 1978, 273(5660):287-289. [CrossRef] [Google Scholar]

[4] Yu Liu, Dugang Xi, Zhaoling Li, et al. A new methodology for pixel-quantitative precipitation nowcastingusing a pyramid Lucas Kanade optical flow approach[J]. Journal of Hydrology, 2015,529:354-364. [CrossRef] [Google Scholar]

[5] S. Xingjian, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Neural Information Processing Systems, 2015. [Google Scholar]

[6] Ayzel, G. et al. “RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting.” DOI: 10.5194/gmd-2020-30 (2020). [Google Scholar]

[7] Xiongfa Mai, Haiyan Zhong and Ling Li. “Research on rain or shine weather forecast in precipitation nowcasting based on XGBoost”, 2020 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. 2020.12.12, Xian, China. [Google Scholar]