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Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning

机译：基于远程感测风场和深度学习的全局旋风和反气旋检测模型

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Ming Xie; Ying Li; Kai Cao;
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1. Mapping the Wind Hazard of Global Tropical Cyclones with Parametric Wind Field Models by Considering the Effects of Local Factors [J] . Chenyan Tan, Weihua Fang International Journal of Disaster Risk Science . 2018,第1期

机译：考虑局部因素影响的参数化风场模型绘制全球热带气旋的风灾
2. Mapping the Wind Hazard of Global Tropical Cyclones with Parametric Wind Field Models by Considering the Effects of Local Factors [J] . Chenyan Tan, Weihua Fang 国际灾害风险科学学报：英文版 . 2018,第001期

机译：考虑局部因素影响的参数化风场模型绘制全球热带气旋的风灾
3. Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth [J] . Arteaga Lionel, Pahlow Markus, Oschlies Andreas Geophysical Research Letters . 2015,第4期

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4. Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection [C] . Papandreou George, Kokkinos Iasonas, Savalle Pierre-Andre IEEE Conference on Computer Vision and Pattern Recognition . 2015

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5. Validation of remotely-sensed soil moisture observations for bare soil at 1.4 GHz: A quantitative approach through radiative transfer models to characterize abrupt transitions caused by a ponding event in an agricultural field, modifications to the radiative transfer models, and a mobile ground-based system [D] . Erbas, Cihan 2009

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6. How will southern hemisphere subtropical anticyclones respond to global warming? Mechanisms and seasonality in CMIP5 and CMIP6 model projections [O] . Abdullah al Fahad, Natalie J. Burls, Zachary Strasberg -1

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7. Surface wind field analyses of tropical cyclones during TCS-08 relative impacts of aircraft and remotely-sensed observations [O] . Havel Patrick J. 2009

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Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning

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