Work

Improve Initial Field Estimation with Deep Learning in Data Assimilation for Climate Models

Year: 2025

Type: article

Abstract: In marine and coastal climate prediction, the integration of multiple imperfect models can improve accuracy by leveraging their complementary strengths. This study investigates this potential by developing a hybrid data assimilation framework that couples a biased physical model with a deep learning model. A neural network learns an optimal fitting coefficient to weight the contributions of both models throughout the assimilation process. We evaluated the framework in twin experiments based on a five-variable coupled climate model and a trained LSTM. Evaluations using root-mean-square error, frequency histograms, and probability density functions consistently demonstrated that the multi-model synthesis achieves superior assimilation performance compared to the single-model approach. Furthermore, when employing different analysis values for prediction, the overall prediction error of the multi-model coupled scheme is reduced to approximately 50% of that from single-model predictions. The promising results from this conceptual model study preliminarily validate the potential of the multi-model coupling approach, offering valuable insights into its potential application to more realistic oceanographic models.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13122406

Authors: Jiakuan Wang, Liang Zhang, Yi Lin, Xuefeng Zhang

Institutions: Tianjin University, Ministry of Natural Resources, Tianjin Meteorological Bureau, Hainan Tropical Ocean University

Language: English

Topic: Oceanographic and Atmospheric Processes

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137