Wang, R.; Shen, Z. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation. J. Mar. Sci. Eng.2024, 12, 108.
Wang, R.; Shen, Z. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation. J. Mar. Sci. Eng. 2024, 12, 108.
Wang, R.; Shen, Z. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation. J. Mar. Sci. Eng.2024, 12, 108.
Wang, R.; Shen, Z. A Deep Neural Network-Ensemble Adjustment Kalman Filter and Its Application on Strongly Coupled Data Assimilation. J. Mar. Sci. Eng. 2024, 12, 108.
Abstract
This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF. This process nonlinearly projects observation increments into the state variable space. The newly developed DNN-EAKF algorithm can be applied to coupled data assimilation using coupled ocean-atmosphere models. It enhances cross-component updates in strongly coupled data assimilation (SCDA) by diminishing errors in estimating cross-component error covariance arising from insufficient ensemble members, thereby improving the SCDA analysis. This paper employs a conceptual model to conduct twin experiments, validating the DNN-EAKF’s capability to utilize cross-component observation information in SCDA effectively. The approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models.
Keywords
Data assimilation; machine learning; deep neural network; ensemble Kalman filter; strongly coupled data assimilation
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
