Estimation of precipitation intensity based on small wisely network (SW-Net)
Estimation of precipitation intensity based on small wisely network (SW-Net)
复制标题
基于小明智网络（SW-Net）的降水强度估计
DOI：
10.1080/01431161.2021.1913297
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发表时间：
2021-04
影响因子：
3.4
通讯作者：
Jiangeng Wang
中科院分区：
文献类型：
--
作者：
Yonghong Zhang;Hao Liu;Wei Tian;Jiangeng Wang
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.
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影响因子：
8
作者：
Ebert, Elizabeth E.;Janowiak, John E.;Kidd, Chris
通讯作者：
Kidd, Chris
DOI：
10.1175/jam2173.1
发表时间：
2004-12-01
期刊：
JOURNAL OF APPLIED METEOROLOGY
影响因子：
--
作者：
Hong, Y;Hsu, KL;Gao, XG
通讯作者：
Gao, XG
影响因子：
3.8
作者：
Daqing Yang;B. Ye;A. Shiklomanov
通讯作者：
Daqing Yang;B. Ye;A. Shiklomanov
影响因子：
3.2
作者：
E. Kalnay;A. Dalcher
通讯作者：
E. Kalnay;A. Dalcher
DOI：
10.1175/1520-0450(2001)040
发表时间：
2001-08
期刊：
Journal of Applied Meteorology
影响因子：
--
作者：
E. Valor;V. Meneu;V. Caselles
通讯作者：
E. Valor;V. Meneu;V. Caselles