4.Generative network:NowcastNet的两部分之二,深入技术细节。 5.如何训练:从损失函数入手,深入NowcastNet的训练过程。 6.结语:保留曲目,一点愚见。 短临降水预报一直是大气科学领域内深度学习方法发挥的首选舞台。继DeepMind DGMR后,清华龙明盛老师团队联合中央气象台,在Nature正刊发表了短临降水预报模型的重磅论文“N...
分别为:NowcastNet,Evolution network,Evolution operator。 (1)NowcastNet NowcastNet的输入为原始的雷达数据,通过Evolution network得到经过物理规律计算之后的预测雷达数据。将原始的雷达数据和预测之后得到的雷达数据结合输入NowcastNet encoder得到3小时的预测结果。 (2)Evolution network 该部分就是论文中介绍的引入...
We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model ...
对比起常用的端到端的GAN网络(比如Cycle-GAN)来说,Nowcastnet的主体结构多出来一个Evolution network模块。 该模块本质上仍然是一个生成网络,图片Evolution Encoder后经过Motion decoder和Intensity decoder可以捕捉输入数据中的额外物理信息,经过进化算子后生成x''_{1:T}返回给原始的生成网络。 本质上讲,可以理解为Now...
Fig. 1: NowcastNet for extreme-precipitation nowcasting. a, Architecture of NowcastNet, a physics-conditional deep generative model. The nowcast encoder learns contextual representations. The nowcast decoder conditions on the physics-informed evolutions\({{\bf{x}}}_{1:T}^{{\prime\prime} }\...
We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model ...
极端降水临近预报的NowcastNet 面对AI闯进天气预报领域,科罗拉多州立大学大气合作研究所研究员Kyle Hilburn给出了中肯的评价,他认为,“人工智能在天气预测任务中具有巨大潜力”。 但是机遇与风险并存,这种大语言模型仍“需气象学家学会设计、评估和解读”。
We use recently proposed NowcastNet, a physics-conditioned deep generative network, to forecast precipitation for different regions of Europe using satellite images. Both spatial and temporal transfer learning is done by forecasting for the unseen regions and year. Model makes realistic predictions and...