气候变化领域集成服务门户(CCPortal): CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China

CCPortal

DOI	10.1016/j.atmosres.2020.105337
	CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China
	Xue M.; Hang R.; Liu Q.; Yuan X.-T.; Lu X.
发表日期	2021
ISSN	0169-8095
卷号	250
英文摘要	Real-time precipitation estimation from satellite observations is still a challenging issue. In this paper, we propose an effective and efficient model based on convolutional neural network (CNN) to estimate precipitation over Xinjiang, the driest region in China, from Fengyun-2 satellite data. Our network mainly consists of two modules: the precipitation identification module and the precipitation estimation module. The first module aims at identifying the given region as precipitation or non-precipitation, while the second one focuses on estimating the specific precipitation amount of the identified region. In addition, considering that topography generally have effects on the quantitative precipitation estimation (QPE) task, we therefore incorporate them into the second module. To evaluate the effectiveness of our proposed model over the national stations, we compare it with not only the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG), but also another two near-real-time products named Global Satellite Mapping of Precipitation Near-Real-Time product (GSMaP_NRT) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Besides, two precipitation events are selected to validate the generalization ability of our proposed model in Xinjiang, and the verification experiment is conducted upon the precipitation product IMERG Final run. Experimental results show that, in comparison with the rain gauges records, the precipitation identification accuracy and correlation coefficient of our proposed model are 0.93 and 0.21, while the precipitation estimation RMSE and MSE of our proposed model are 0.25 and 0.038 mm/h, respectively. For the two precipitation events, our proposed model demonstrates high consistency with IMERG Final run in precipitation identification, but it shows a tendency to underestimate the heavy rainfall estimated by IMERG Final run. More importantly, the implementation time of our proposed model on the hourly testing dataset is only 5.5 s. Therefore, our proposed model can achieve near-real-time results, which is valuable for precipitation nowcasting applications. © 2020 Elsevier B.V.
英文关键词	Convolutional neural network (CNN); Fengyun-2 satellite; Near-real-time estimation; Quantitative precipitation estimation (QPE)
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/236886
作者单位	Jiangsu Key Laboratory of Big Data Analysis Technology, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830000, China
推荐引用方式 GB/T 7714	Xue M.,Hang R.,Liu Q.,et al. CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China[J],2021,250.
APA	Xue M.,Hang R.,Liu Q.,Yuan X.-T.,&Lu X..(2021).CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China.Atmospheric Research,250.
MLA	Xue M.,et al."CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China".Atmospheric Research 250(2021).