Zhanqing Li

Zhanqing Li is a Chinese-American atmospheric and environmental scientist, serving as a Distinguished University Professor in the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park.

Li's work has been focused in atmospheric and environmental sciences, climate, remote sensing He has been ranked among the world's top 0.1% most cited scientists.

Li is a fellow of the American Association for the Advancement of Science, the American Geophysical Union, and the American Meteorological Society.

Education and career Born in Luoyang, Henan, China, Li received his B.Sc. and M.Sc. from Nanjing University of Information Science and Technology (NUIST) in 1983 and 1986, respectively, and earned his Ph.D. from McGill University, Canada, in 1991. His career began at the China Meteorological Administration, where he worked as a junior researcher for one year. After receiving his Ph.D., he conducted postdoctoral research at the Meteorological Service of Canada (1991–1992), and was then hired as a research scientist at the Canada Centre for Remote Sensing for 9 years. In 2001, he joined the University of Maryland, College Park, as a full professor in the Department of Atmospheric and Oceanic Sciences, and he has been a Distinguished University Professor since 2022.

He has also held editorial roles with journals including the Journal of Geophysical Research and Atmospheric Chemistry and Physics.

Research Li's research is concerned with earth's radiation budget, aerosol, cloud, aerosol-cloud-interactions, aerosol-radiation-interactions, air pollution and impact on severe weather and climate change, planetary boundary layers, biomass burning, and their impacts on public health. He has developed original remote sensing algorithms and products of atmospheric and environmental variables. He has published in journals including Nature, Science, and The Lancet. He received the Canadian Government's Head of Public Service Award in 1998, the 2014 Yoram J. Kaufman Research Award from the American Geophysical Union, and the Humboldt Research Award from the Alexander von Humboldt Foundation in 2015.

Li's work has encompassed research and discoveries across atmospheric, environmental, terrestrial sciences, and public health disciplines with main foci in the Earth's radiation budget, aerosol impacts on clouds and precipitation, satellite algorithm and product development, climate-environment interactions, fire monitoring systems, and extensive aerosol, climate, and environmental studies.

Li has fostered partnerships between the US and Canada, which supported the CloudSat mission, and between the US and China, leading the NASA's EAST-AIRE project and DOE's ARM/AMF field experiment in China. He has been engaged in US and international research programs like ARM (Atmospheric Radiation Measurement), ERBE (Earth Radiation Budget Experiment), SRB (Surface Radiation Budget), GACP (Global Aerosol Climatology Project), and LCLUC (Land Cover and Land Use Change). He led the project FIRE-M3 (Fire Monitoring, Mapping and Modeling) in Canada. He has also served on the science teams of France's ScaRaB and EU's Fire initiatives.

Solar radiation budget analysis through satellite algorithms Li has studied the Earth's solar radiation budget (SRB), the driving force of the climate system, and developed satellite-based algorithms to retrieve global SRB. His parameterized SRB algorithm has been employed in NASA's ERBE and CERES satellite programs to generate global SRB datasets.

Li has estimated the global solar energy distribution from satellite with 25–30% systematic changes in the solar radiation disposition relative to previous estimates, reducing surface absorption and increasing atmospheric absorption. He has helped identify and resolved model deficiencies concerning model treatments of water vapor absorption, surface albedo, addressed debates on cloud absorption anomalies and provided insights into solar radiative processes and their representation in climate models.

The cloud absorption anomaly Li's research challenged the once-growing acceptance of the cloud absorption anomaly (CAA), a historical paradox dated to 1957 through 1990s suggesting clouds absorb more solar radiation than theoretical calculations. Using global satellite and ground-based observations alongside model simulations, he found that the amount of solar radiation absorbed by clouds generally aligned with model estimates, with the exception in some tropical regions. This conclusion was further supported by a detailed study and the development of a new analysis method. The enhanced absorption observed in the tropics was later linked to smoke from tropical burning. Following this experiment, no significant CAA was found, although some model underestimation remained, which was attributed to surface albedo effects rather than clouds, prompting more efforts to document albedo changes.

Remote sensing of cloud layers, cloud droplet size, cloud condensation nuclei, cloud updraft speed and warm rainfall In collaboration with Fu-Lung Chang, he established retrieval algorithms to generate a global climatology of 3D cloud distributions from a passive satellite sensor MODIS, capturing both single-layer and dual-layer thin-over-thick clouds. This work corrected biases in earlier satellite products that misrepresented such dual-layer clouds as single mid-level clouds, aiding improvements in General Circulation Models (GCM). He also utilized MODIS's spectral channels to derive vertical variations in cloud effective radius (DER), enabling more accurate estimates of cloud liquid water path, identification of cloud development stages, and detection of warm rain from boundary-layer clouds using satellite data acquired by passive sensors.

Li's team devised original satellite-based approaches to retrieve cloud base updraft speed (Wb) and cloud condensation nuclei (CCN) concentrations, led by Y. Zheng in collaboration with D. Rosenfeld. These methods, designed for different cloud types, relied on relationships with meteorological parameters derived from satellite and reanalysis data, provided observational evidence for the role of cloud-base height in regulating updrafts and proposed a theory on the surface coupling of marine stratocumulus clouds, supported by ship-based and satellite measurements. followed by a wide range of investigations concerning aerosol-radiation-interaction (ARI) and aerosol-cloud-interaction (ACI) with a keystone finding that aerosols drastically alter cloud and precipitation by suppressing low cloud and light rain, and invigorating deep clouds and heavy rain, as reported in Nature Geoscience (2011), and Science in collaboration with J. Fan. Their findings were cited and reported by various media outlets. His team conducted studies concerning broad interactions between air pollution and climate changes, especially in Asian monsoon regions as reviewed in Review of Geophysics.

Li and his team revealed aerosol-induced changes in temperature, precipitation, storms, and lightning. He has also characterized biomass burning aerosols from tropical to boreal forests, devising methods to estimate their optical properties and radiative forcing using ground-based and aircraft measurements, as well as space-borne data. His research demonstrated the distinct characteristics of boreal forest smoke aerosols, including their ability to circulate globally and reach the stratosphere, led by M. Fromm, G. Kablick and K. Junghenn-Noyes. Additionally, he has delved into aerosol retrieval algorithms and products, identifying discrepancies in existing datasets, extending coverage to land surfaces, and providing critical reviews of inversion techniques.

East-Asian aerosols and climate Outside of the US, Li has focused on the climatic effects of aerosols in East Asia, particularly in China. In 2004–2010, he led the East Asian Study of Tropospheric Aerosols, a Regional International Experiment (EAST-AIRE). The project established a nationwide aerosol observation network with 25 stations measuring aerosol optical properties, single scattering albedo, and radiative effects, and three observation campaigns with a series of findings published in 18 peer-reviewed papers, such as the first observation-based estimates of aerosol loading, optical properties and cloud vertical structure across China. The experiment provided measurements that contributed to the understanding of aerosols' impact on radiation, clouds, and climate, assisted in validating satellite products, identified deficiencies in remote sensing products over the region, and suggested solutions. His work also characterized aerosol properties at key sites, identified pollutant transport mechanisms, and developed methods for analyzing sulfate aerosols and estimating aerosol scattering, resulting in scientific publications.

Air pollution monitoring by machine learning: particulate matter (PM) and trace gases In 2001, Li developed a method of tracking fire smoke using a neural network algorithm. Later, his team applied modern ML/AI models to monitor surface air pollutants on global and regional scales, including particulate matter (PM, PM, PM), black carbon (BC), and gaseous pollutants such as ozone, NO, SO, CO, and aerosol chemical species using data from various satellite sensors, ground observation networks and model simulations, led by J. Wei. The global 1 km PM product revealed the episodical trends and wave-like evolution of air pollution associated with COVID-19 and mortality rate. They also estimated daily 1 km PM and black carbon (BC) concentrations across the US over two decades and found that the increasing trends of fire activities in western US have reversed the trend of air quality improvement, published in Lancet Planetary Health in 2023.

Remote sensing of planetary boundary layer (PBL) and PBL-cloud-coupling, and understanding their interactions Li's team has conducted a series of planetary boundary layer (PBL)-related studies including PBL-pollutant-interactions, PBL-cloud-interaction, PBL-aerosol-cloud interactions, and remote sensing of PBL. By merging with meteorological data, the team developed novel remote sensing methods to determine PBL height and PBL-cloud-coupling. The PBL algorithm has been applied to CALIPSO and CATS satellites to generate global PBL products and ARM's ground measurements to develop a long-term PBL climatology. By applying remote sensing techniques, microphysical theory, and numerical modeling, they have addressed challenges to advance PBL observation, improving the understanding of fundamental PBL processes, and explore their interactions with aerosols and clouds, as well as entrainment rate, which are used for weather forecasting and future climate projections. The PBL related studies were led by T. Su, V. Sawyer and N. Roldán-Henao.

Development of the fire monitoring, mapping and modeling (FIRE/M3) system and satellite-based fire products Li led a team to guide the development of fire monitoring around the world, and a Canadian team for the development of the first satellite-based fire monitoring, mapping, and modeling system (FIRE/M3) across Canada, winning the Head of Public Service Award and the Alouette Award. His team advanced remote sensing technologies for detecting fire hotspots and mapping burned areas in boreal forests, introduced biomass estimation using passive optical sensors, in collaboration with R. Fraser. They validated these techniques using ground-truth data, created a modeling system for estimating trace gas emissions from fires, and generated the first 1-km daily fire data for North America.

Fundamental remote sensing methodology Li's contributions to fundamental remote sensing methodology include scene, cloud in particular, identification, bidirectional reflectance distribution function (BRDF), and narrow-to-broadband conversion. He presented a threshold algorithm to identify thin Arctic clouds over snow or ice-covered areas using AVHRR data and used these advancements to assess polar cloud radiative forcing, leading to corrections to earlier estimates. In addition, he led the application of a neural network approach to distinguish between smoke, clouds, and clear land. and surface UV-B radiation. In response to the challenges in determining APAR due to cloud effects, he developed a method leveraging minimal cloud absorption in PAR wavelengths to estimate the total PAR absorbed by ecosystems without the need of acquiring cloud parameters and thus circumventing a major source of error. In 2004, he appeared on the cover of Science as a representative figure of foreign-born scientists in the United States.

Awards and honors 1998 – Head of Public Service Award, Government of Canada 2000 - Alouette Award, Canadian Aeronautics and Space Institute 2014 – Yoram J. Kaufman Research Award, American Geophysical Union 2022 – Distinguished University Professor, University of Maryland, College Park 2026 – Verner E. Suomi Technology Medal, American Meteorological Society

Selected articles