A generative adversarial network-based unified model integrating bias correction and downscaling for global SSTOA
本文提出了一种基于生成对抗网络的全球海表面温度(sea surface temperature,SST)偏差订正及降尺度整合模型.该模型的生成器使用偏差订正模块将数值模式预测结果进行校正,再用可复用的共享降尺度模块将订正后的数据分辨率逐次提高.该模型的判别器可鉴别偏差订正及降尺度结果的质量,以此为标准进行对抗训练。同时,在对抗损失函数中含有物理引导的动力学惩罚项以提高模型的性能.本研究基于分辨率为1°的GFDL SPEAR模式的SST预测结果,选…查看全部>>
Shijin Yuan;Xin Feng;Bin Mu;Bo Qin;Xin Wang;Yuxuan Chen
School of Software Engineering,Tongji University,Shanghai,ChinaSchool of Software Engineering,Tongji University,Shanghai,ChinaSchool of Software Engineering,Tongji University,Shanghai,ChinaSchool of Software Engineering,Tongji University,Shanghai,ChinaSchool of Software Engineering,Tongji University,Shanghai,ChinaSchool of Software Engineering,Tongji University,Shanghai,China
海洋学
偏差订正降尺度海表面温度生成对抗网络物理引导的神经网络
《Atmospheric and Oceanic Science Letters》 2024 (1)
P.45-52,8
supported by the National Key Research and Development Program of China[grant number 2020YFA0608000]the National Natural Science Foundation of China[grant number 42075141]the Meteorological Joint Funds of the National Natural Science Foundation of China[grant number U2142211]the Key Project Fund of the Shanghai 2020“Science and Technology Innovation Action Plan”for Social Development[grant number 20dz1200702]the first batch of Model Interdisciplinary Joint Research Projects of Tongji University in 2021[grant number YB-21-202110].
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