气候变化领域集成服务门户(CCPortal): Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations

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DOI	10.1016/j.atmosres.2021.105774
	Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations
	Kim D.-K.; Suezawa T.; Mega T.; Kikuchi H.; Yoshikawa E.; Baron P.; Ushio T.
发表日期	2021
ISSN	0169-8095
卷号	262
英文摘要	In this paper, a three-dimensional convolutional neural network model (3DCNN) is proposed to nowcast a short-lived, local convective storm event by using unique 3-D observations of Multi-Parameter Phased Array Weather Radar (MP-PAWR) over Tokyo, Japan on 1 August 2019. Using statistics and forecast skill scores, nowcast skills of 3DCNN were examined with those of a three-dimensional advection nowcast model (3DNOW) which generates extrapolation-based forecasts with lead time up to 10 min. In analyzing the skill scores, two groups of a total rain area and convective rain area were made by different radar reflectivity (Z) thresholds of 10 dBZ and 37.5 dBZ, respectively. For the total rain area, it is found that 3DCNN outperformed both the 3DNOW and persistence forecast, showing the higher threat scores for all lead times. For the convective rain area, the 3DCNN and 3DNOW's performances were similar at early lead times, showing almost the same threat scores. However, the threat score of 3DCNN dropped lower than that of 3DNOW at a lead time of 10 min, indicating that 3DNOW has the better skill at relatively long lead times. Nowcasts of 3DNOW showed a limitation to yield a larger saturated Z area related to increased errors in advection vectors at longer lead times although this led to increasing the threat score. © 2021 Elsevier B.V.
英文关键词	Convective storm; Convolutional neural network; Multi-Parameter Phased Array Weather Radar; Precipitation nowcast
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236622
作者单位	Division of Electrical, Electric and Information Engineering, Graduate School of Engineering, Osaka University, Osaka, Japan; Department of Aerospace Engineering, Tokyo Metropolitan University, Tokyo, Japan; Center for Space Science and Radio Engineering, The University of Electro Communications, Tokyo, Japan; Aeronautical Technology Directorate, Japan Aerospace Exploration Agency, Tokyo, Japan
推荐引用方式 GB/T 7714	Kim D.-K.,Suezawa T.,Mega T.,et al. Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations[J],2021,262.
APA	Kim D.-K..,Suezawa T..,Mega T..,Kikuchi H..,Yoshikawa E..,...&Ushio T..(2021).Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations.Atmospheric Research,262.
MLA	Kim D.-K.,et al."Improving precipitation nowcasting using a three-dimensional convolutional neural network model from Multi Parameter Phased Array Weather Radar observations".Atmospheric Research 262(2021).