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.
- Yoo-Geun Ham
- Yong-Sik Joo
- Jeong-Gil Lee
