.png){kind=icon}
Return
Partial-convolution-implemented generative adversarial network for global oceanic data assimilation
.png){kind=icon}
OA
AI
DOI：10.1038/s42256-024-00867-x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
SEA-SURFACE TEMPERATURE
OBJECTIVE ANALYSES
KALMAN FILTER
SYSTEM
REANALYSIS
DESIGN
Journal
IF:
23.9
Papers:
1.3K
Citations:
1.5W
Organization
View more organizations
Cited Papers
In situ benthos and paleo-oxygenation in the Middle Cambrian Burgess Shale, British Columbia, Canada.png)
Geology
GeologyIF0
PREAI
ShareSaveShareSave
ShareSaveShareSaveShareSaveA Coupled Resonator Decoupling Network for Two-Element Compact Antenna Arrays in Mobile Terminals
PREAI
ShareSaveImplementing a variational data assimilation system in an operational 1/4 degree global ocean model
PREAI
ShareSaveShareSave View more