Member Since 2002
Jun Wang
Professor, University of Iowa
Member, Eos Science Advisers; Guest Associate Editor, Earth and Space Science; Guest Associate Editor, Geophysical Research Letters; Associate Editor, JGR Atmospheres Section
Jun Wang’s research focuses on the integration of satellite remote sensing and chemistry transport models to study air quality, wildfires, aerosol-cloud interaction, and land-air interaction. He has authored 210 articles (H-index 63), co-edited two books, served as a science team member for 10 satellite missions (including inaugural team member for TEMPO and MAIA) and the NASA’s senior review panel for Earth science (three times). He received 2022 AGU’s Atmospheric Science ASCENT award.
Professional Experience
University of Iowa
Professor
2016 - Present
Education
University of Alabama in Huntsville
Doctorate
2005
Honors & Awards
Joanne Simpson Medal
Received December 2024
Ascent Award
Received December 2022
Citation
Dr. Jun Wang is an internationally recognized leader in the field of remote sensing of aerosols and fires. He has been a science team member of 10 different satellite missions. His research efforts include many major advances in satellite algorithm physics for shortwave remote sensing. They have shed new light for gaining more insights on aerosol and fire properties from space. His research has spanned from being one of the first in satellite remote sensing of surface particulate matter air quality to the development of seminal algorithms for passive sensing of aerosol layer height and fire combustion efficiency (phase) to the most recent groundbreaking work of using satellite-measured backscattered moonlight to map the transport and optical depth of smoke particles at night and advanced aerosol retrievals from hyperspectral measurements. Equally illuminating is his team’s recent work of mapping aerosol plume height, for the first time, in each hour from the NASA DSCOVR (Deep Space Climate Observatory) satellite. On the modeling front, Dr. Wang has an evident track record of innovative application of satellite data to improve modeling and understanding of air quality and atmospheric composition (often via data assimilation). By assimilating the aerosol and fire products from geostationary satellites into a regional atmospheric model (RAMS), his early work attested to the importance of diurnal variations of dust and smoke aerosol emissions to the modeling of the surface energy budget and boundary layer process. Subsequent collaborative work led by him was 4D-VAR assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) radiance data and OMI sulfur dioxide (SO2) data to constrain the sources of dust and SO2 emissions, respectively; this was seminal at that time in the field of chemical data assimilation. Dr. Wang is known for his genuine generosity and his passion for collaborative research that have served our community well. For example, the numerical test bed that he and his team developed for satellite remote sensing of aerosols is accessible to the public and used by approximately 40 research groups around the world. In summary, Dr. Wang’s scholarly productivity and originality, along with his outstanding service to the AGU and atmospheric science communities, epitomize the qualities recognized by the Ascent Award. —Kelly Chance, Center for Astrophysics, Harvard & Smithsonian, Cambridge, Mass.
Response
I am delighted and honored to receive the 2022 AGU Ascent Award. I am grateful to my nominator, Kelly Chance, and supporters for their precious time spent in the nomination process. I thank the selection committee for this empowering and wonderful recognition. For the past 20 years, I have had the pleasure and privilege to learn from and work with many individuals in the field of atmospheric sciences, especially at the intersection of atmospheric composition and climate change. I thank my Ph.D. adviser, Sundar A. Christopher (University of Alabama in Huntsville), and my postdoctoral advisers, Scot T. Martin and Daniel J. Jacob (Harvard University). Their support and mentorship not only shaped my early research trajectory but also instilled in me the confidence and the desire to challenge myself often in pursuit of new research directions. I thank all the former and present members of my research group and would like to share this recognition with them. I also extend my appreciation to my research collaborators, including Peter R. Colarco, David J. Diner, Daven K. Henze, Edward J. Hyer, Charles M. Ichoku, G. Darrel Jenerette, Shobha Kondragunta, Robert C. Levy, Xiong Liu, Steven D. Miller, Jeffrey S. Reid, Arlindo M. da Silva, Eric M. Wilcox, John E. Yorks, and many more. Their generosity and wisdom have helped me in research areas from satellite remote sensing of atmospheric composition to chemical data assimilation and satellite mission development. My gratitude also goes to the faculty and staff at the University of Iowa for their tremendous collegiality. I am thankful to the research programs (and the program managers thereof) in NASA, NOAA, U.S. Department of Agriculture, National Science Foundation, and Office of Naval Research that have funded (and managed) my research projects. I thank my colleagues in NASA and NOAA for their team spirit to tackle day-to-day operational challenges (during the pandemic) in providing the satellite observations to users (like myself) in the research and application communities. I look forward to the successful launch of the TEMPO (Tropospheric Emissions: Monitoring of Pollution) satellite mission led by Dr. Kelly Chance, as it will provide the first-ever hourly mapping of atmospheric gas pollutants over North America. Finally, I am in debt to my wife, Jing Zeng, our parents, and our three children, Kerry, Cindy, and Justin, for their love and support. Without them, I would not have been able to make any of the achievements mentioned in the citation. —Jun Wang, University of Iowa, Iowa City
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Outstanding Reviewer Award - JGR-Atmospheres
Received December 2017
Publications
Effect of Dust Morphology on Aerosol Optics in the GEOS‐Chem Chemical Transport Model, on UV‐Vis Tra...

Many chemical transport models treat mineral dust as spherical. Solar backscatter retrievals of trace gases (e.g., OMI and TROPOMI) implicitly trea...

September 26, 2024
AGU Abstracts
Fire-exacerbated Surface Ozone in Contiguous US: A Deep Learning Study Integrating Models, Observations, and Emissions
AGU 2024
atmospheric sciences | 13 december 2024
Weizhi Deng, Jun Wang, Meng Zhou, Xi Chen, Huanxin...
More frequent and intensified fires in Western US and Canada under climate change have raised concerns in their induced air pollution. Of particular i...
View Abstract
Stratosphere Troposphere Response using Infrared Vertically-resolved light Explorer (STRIVE) Mission Concept – Taking the Pulse of the UTLS
AGU 2024
atmospheric sciences | 13 december 2024
Luke Oman, Lyatt Jaegle, Jun Wang, Thomas F. Hanis...
The Stratosphere Troposphere Response using Infrared Vertically-resolved light Explorer (STRIVE) is a new satellite mission concept selected for a com...
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Using Machine Learning to Retrieve Cloud Microphysical Parameters from VIIRS Observations
AGU 2024
atmospheric sciences | 13 december 2024
Ying-Chieh Chen, Jun Wang, Meng Zhou, Xi Chen
Clouds play an important role in regulating Earth's climate by influencing both shortwave and longwave radiation components. Accurate measurement of c...
View Abstract
Volunteer Experience
2024 - 2029
Member
Eos Science Advisers
2022 - 2028
Associate Editor
JGR Atmospheres Section
2024 - 2025
Guest Associate Editor
Earth and Space Science
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