Publications
- He, T.-L.#; G.-M. Oomen#; W. Tang; et al. Opportunities and challenges offered by geostationary space observations for air quality characterization and forecasts, BAMS, under review.
2024
[12] Dadheech, N.#, T.-L. He#, A. J. Turner. High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport, EGUsphere, under review.
[11] He, T.-L.#; N. Dadheech#; T. M. Thompson; A. J. Turner. FootNet v1.0: Development of a machine learning emulator of atmospheric transport. EGUsphere, under review.
Preprint@EarthArXiv.
[10] He, T.-L.; R. J. Boyd; D. J. Varon; A. J. Turner. Increased methane emissions from oil and gas following the Soviet Union’s collapse. Proc. Natl. Acad. Sci., 121 (12) e2314600121, 2024.
Press release: UW News, EurekAlert!, ScienceDaily, Phys
2023
[9] Han, W.#; He, T.-L.#; Zhu, R.; Jones, D. B. A.; Miyazaki, K.; Jiang, Z. The capability of deep learning model to predict ozone across continents in China, the United States and Europe, Geophysical Research Letters, 2023.
[8] Wang, M.; X. Chen; T.-L. He; D. B. A. Jones; J. Liu; Y. Shen. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015-2021. Science of the Total Environment, 2023.
[7] Chen, X.; M. Wang; T.-L. He; Z. Jiang; Y. Zhang; L. Zhou; J. Liu; H. Liao; H. Worden; D. B. A. Jones; D. Chen; Q. Tan; Y. Shen. Data- and model-based urban O3 responses to NOx changes in China and the United States. Journal of Geophysical Research: Atmospheres, 2023.
2022
[6] Zemskova, V. E.; T.-L. He; Z. Wan; N. Grisouard. A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage. Nature Communications 13, 4056 (2022).
Preprint@EarthArXiv, GitHub.
[5] He, T.-L.; D. B. A. Jones; K. Miyazaki; K. W. Bowman; Z. Jiang; X. Chen; R. Li; Y. Zhang. Inverse modeling of Chinese NOx emissions using deep learning: Integrating in situ observations with a satellite-based chemical reanalysis. Atmospheric Chemistry and Physics, 2022.
[4] Han, W.#; T.-L. He#; Z. Tang; M. Wang; D. B. A. Jones; Z. Jiang. A comparative analysis for deep learning and Kalman Filter to predict CO in China. Geoscientific Model Development, 2022.
[3] He, T.-L.; D. B. A. Jones; B. Huang; Y. Liu; K. Miyazaki; Z. Jiang; E. C. White; H. M. Worden; J. R. Worden. Deep learning to evaluate US NOx emissions using surface ozone predictions. Journal of Geophysical Research: Atmospheres, 2022.
Preprint@arXiv; Preprint@ESSOAr
Before 2022
[2] Khade, V.; S. M. Polavarapu; M. Neish; P. L. Houtekamer; D. B. A. Jones; S.-J. Baek; T.-L. He; S. Gravel. The Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0) : demonstration with simulated CO observations. Geoscientific Model Development, 2020.
[1] Hedelius, J. K.; T.-L. He; D. B. A. Jones; B. C. Baier; R. R. Buchholz; M. De Mazière; N. M. Deutscher; M. K. Dubey; D. G. Feist; D. W. T. Griffith; F. Hase; L. T. Iraci; P. Jeseck; M. Kiel; R. Kivi; C. Liu; I. Morino; J. Notholt; Y.-S. Oh; H. Ohyama; D. F. Pollard; M. Rettinger; S. Roche; C. M. Roehl; M. Schneider; K. Shiomi; K. Strong; R. Sussmann; C. Sweeney; Y. Té; O. Uchino; V. A. Velazco; W. Wang; T. Warneke; P. O. Wennberg; H. M. Worden; D. Wunch. Evaluation of MOPITT Version 7 joint TIR–NIR XCO retrievals with TCCON. Atmospheric Measurement Techniques, 12, 5547–5572, 2019.