Estimation of precipitation intensity based on small wisely network (SW-Net)

ABSTRACT

Precipitation estimation with high spatial and temporal resolution is very important for monitoring floods and natural disasters. At present, a couple of quantitative precipitation estimation products and research methods can successfully estimate precipitation at one hourly temporal resolution. In this study, a deep learning model based on Convolutional Neural Network (CNN) was proposed to estimate the precipitation intensity based on the hyperspectral satellite FengYun-4/Advanced Geostationary Radiation Imager (FY-4A), and the temporal resolution is reduced to half an hour. Firstly, the importance of different channels and channel differences for precipitation intensity estimation was determined by ablation experiments. Secondly, compared with the existing model Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks (PERSIANN-CNN) and U-Net. The experimental results show that Small Wisely Network (SW-Net) provides more accurate precipitation intensity estimation, compared with PERSIANN-CNN (U-Net) in the same spatial and temporal resolutions. SW-Net outperformed PERSIANN-CNN (U-Net) by 5.9439% (5.6298%) and 6.3600 (5.8400) percentage points in the loss value and Mean Intersection over Union (MIoU), demonstrating the better feature extraction performance of the model. Furthermore, the False Alarm Ratio (FAR) of precipitation estimation with respect to Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (GPM-IMERG), for SW-Net was lower than that of PERSIANN-CNN (U-Net) by 49.2132% (49.4302%), showing the higher accuracy of proposed model.

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (41875027) and Postgraduate Research Practice Innovation Program of Jiangsu Province (1534052001064).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_0967]; National Natural Science Foundation of China [41875027]; National Natural Science Foundation of China [42075138].