Tai-Long He

Tai-Long He

University of Toronto

H-index: 4

North America-Canada

About Tai-Long He

Tai-Long He, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at University of Toronto, specializes in the field of atmospheric chemistry, carbon cycle, deep learning, remote sensing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Increased methane emissions from oil and gas following the Soviet Union’s collapse

The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains

Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021

The capability of deep learning model to predict ozone across continents in China, the United States and Europe

FootNet: Development of a machine learning emulator of atmospheric transport

Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States

Spaceborne assessment of the Soviet Union's role in the 1990s methane slowdown

Development of a deep learning emulator of source-receptor footprints for computationally efficient flux inversions

Tai-Long He Information

University

Position

___

Citations(all)

68

Citations(since 2020)

67

Cited By

11

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Tai-Long He Skills & Research Interests

atmospheric chemistry

carbon cycle

deep learning

remote sensing

Top articles of Tai-Long He

Increased methane emissions from oil and gas following the Soviet Union’s collapse

Proceedings of the National Academy of Sciences

2024/3/19

Tai-Long He
Tai-Long He

H-Index: 2

The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains

2024/3/7

Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021

Science of the Total Environment

2024/1/1

The capability of deep learning model to predict ozone across continents in China, the United States and Europe

Geophysical Research Letters

2023/12/28

FootNet: Development of a machine learning emulator of atmospheric transport

2023/12/9

Tai-Long He
Tai-Long He

H-Index: 2

Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States

Journal of Geophysical Research: Atmospheres

2023/10/27

Spaceborne assessment of the Soviet Union's role in the 1990s methane slowdown

2023/10/3

Tai-Long He
Tai-Long He

H-Index: 2

Development of a deep learning emulator of source-receptor footprints for computationally efficient flux inversions

AGU Fall Meeting Abstracts

2022/12

Tai-Long He
Tai-Long He

H-Index: 2

Can the data assimilation of CO from MOPITT or IASI constrain high-latitude wildfire emissions? A Case Study of the 2017 Canadian Wildfires

Authorea Preprints

2022/11/21

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Atmospheric Chemistry and Physics

2022/11/3

Large discrepancy between observations and simulations: Implications for urban air quality in China

arXiv preprint arXiv:2208.11831

2022/8/25

A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage

Nature communications

2022/7/13

Tai-Long He
Tai-Long He

H-Index: 2

Nicolas Grisouard
Nicolas Grisouard

H-Index: 9

A comparative analysis for a deep learning model (hyDL-CO v1. 0) and Kalman filter to predict CO concentrations in China

Geoscientific Model Development

2022/6/1

Deep Learning to Evaluate US NOx Emissions Using Surface Ozone Predictions

Journal of Geophysical Research: Atmospheres

2022/2/27

Mitigating Model Errors in Chemical Data Assimilation: Application of New Data Assimilation and Machine Learning Approaches

2022

Tai-Long He
Tai-Long He

H-Index: 2

Deep learning for Chinese NOx emission inversion and the integration of in situ observations: a case study on the COVID-19 pandemic

2021/6/18

The environment and climate change Canada carbon assimilation system (EC-CAS v1. 0): Demonstration with simulated CO observations

Geoscientific Model Development

2021/5/6

Dylan Jones
Dylan Jones

H-Index: 5

Tai-Long He
Tai-Long He

H-Index: 2

Quantifying Changes in NOx Emissions in China during the COVID-19 Pandemic Using a Neural Network Approach

AGU Fall Meeting Abstracts

2020/12

Tai-Long He
Tai-Long He

H-Index: 2

See List of Professors in Tai-Long He University(University of Toronto)

Co-Authors

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