Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 12
Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3611-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-3611-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 12
Development and technical paper
 | 
30 Jun 2023
Development and technical paper |  | 30 Jun 2023

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao

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Chaudhuri, S. and Middey, A.: Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms, Meteorol. Atmos. Phys., 114, 139–149, https://doi.org/10.1007/s00703-011-0158-4, 2011. a
Danandeh Mehr, A., Rikhtehgar Ghiasi, A., Yaseen, Z. M., Sorman, A. U., and Abualigah, L.: A novel intelligent deep learning predictive model for meteorological drought forecasting, J. Amb. Intel. Hum. Comp., 1–15, https://doi.org/10.1007/s12652-022-03701-7, 2022. a
Doswell, C. A. (Ed.): Severe convective storms – An overview, in: Severe convective storms, American Meteorological Society, Boston, MA, 1–26, https://doi.org/10.1007/978-1-935704-06-5_1, 2001. a
Doswell, C. A., Brooks, H. E., and Maddox, R. A.: Flash Flood Forecasting: An Ingredients-Based Methodology, Weather Forecast., 11, 560–581, https://doi.org/10.1175/1520-0434(1996)01160;0560:fffaib62;2.0.co;2, 1996. a
Duan, M., Xia, J., Yan, Z., Han, L., Zhang, L., Xia, H., and Yu, S.: Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations, Remote Sensing, 13, 3330, https://doi.org/10.3390/rs13163330, 2021. a
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
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Fusion of radar polarimetry and numerical atmospheric modelling...