Global Land-Atmospheric Data Assimilation Based on Koopman Operator Error Covariance in a Deep Neural Network Latent Space for GHG Emission Verification

JPL Data 2026 Dataset Open access

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Authors: S. V. Nghiem, Hironori Arai, Yoshinobu Kawahara, Thuy Le Toan, Kei Oyoshi, Wataru Takeuchi, Mehrez Zribi, Tamon Fumoto, Kazuyuki Inubushi, Lam Đao Nguyen, Shinichi Sobue, Toshimasa Ohara
DOI: 10.48577/jpl.rwq3bb
Cited by: 0

Abstract

Global Land-Atmospheric Data Assimilation Based on Koopman Operator Error Covariance in a Deep Neural Network Latent Space for GHG Emission Verification

Fields of Study & Subjects

Meteorological Phenomena and Simulations Atmospheric and Environmental Gas Dynamics Model Reduction and Neural Networks

Publication details

Journal / source
JPL Data
Publication date
1 January 2026
Language
EN
Document type
Dataset