Ham, Yoo-Geun Joo, Yong-Sik Kim, Jeong-Hwan Lee, Jeong-Gil 2024-08-08T01:00:27Z 2024-08-08T01:00:27Z 2024-08-08 2024-07 2522-5839 https://pubs.kist.re.kr/handle/201004/150375 The oceanic data assimilation (DA) system has been developed to optimally combine numerical-model predictions with actual measurements from the ocean to create the best estimates of current ocean conditions and their uncertainties, improving our ability to forecast and understand the global climate variations. We developed DeepDA, a global oceanic DA system using deep learning, by integrating a partial convolutional neural network and a generative adversarial network. Partial convolution serves as an observation operator, mapping irregular observational data onto gridded fields, while generative adversarial network incorporates observational information from previous time frames. Our observing system simulation experiments, using simulated observations for the DA, revealed that DeepDA markedly reduces analysis error of the oceanic temperature, outperforming both background and observed values. DeepDA's real-case global temperature reanalysis spanning from 1981 to 2020 accurately reconstructs observed global climatological temperature fields, along with their seasonal cycles, major oceanic temperature variabilities and global warming trend. Developed solely with a long-term control simulation, DeepDA lowers technical hurdles in creating global ocean reanalysis datasets using multiple numerical models' physical constraints, thereby diminishing systematic uncertainties in estimating global oceanic states over decades with these models. Data assimilation (DA) techniques are commonly used to assess global Earth system variability but require considerable computational resources and struggle to handle sparse observational data. Ham and colleagues introduce a partial convolution and generative adversarial network-based global oceanic DA system and successfully reconstruct the observed global temperature in a real case study with smaller computational costs than traditional DA systems. English SPRINGERNATURE Partial-convolution-implemented generative adversarial network for global oceanic data assimilation Article 10.1038/s42256-024-00867-x 1 Nature Machine Intelligence, v.6, no.7, pp.834 - 843 Nature Machine Intelligence 6 7 834 843 N scie scopus 001274938000002 2-s2.0-85199247160 Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science Article KALMAN FILTER SYSTEM REANALYSIS DESIGN SEA-SURFACE TEMPERATURE OBJECTIVE ANALYSES