Research

The research conducted by Dr. Christopher's group is dedicated to utilizing satellite data for studying the earth-atmosphere system. Our investigations span various aspects, including:

 

Pixel-Level Classification: Employing pixel-level multi-spectral, multi-angle, polar orbiting, and geostationary datasets to categorize pixels into distinct classes, such as clouds, dust, smoke, vegetation, and others.

 

Radiative Transfer Algorithms: Utilizing radiative transfer algorithms alongside satellite data to retrieve crucial properties like aerosol optical depth, cloud optical depth, and particle sizes.

 

Integration of Datasets: Combining narrow and broadband datasets, such as MODIS and CERES, to investigate aerosol radiative forcing, gaining insights into the impact of aerosols on the Earth's energy balance.

 

Air Quality Assessment: Utilizing satellite data in conjunction with ground monitors to assess particulate matter air quality, providing valuable information for understanding and addressing air pollution issues.

 

Numerical Modeling: Applying numerical models to study and predict air quality, enhancing our ability to anticipate and mitigate potential environmental challenges.

 

Algorithm Validation: Leveraging ground datasets to validate and refine satellite algorithms, ensuring accuracy and reliability in the interpretation of satellite observations.

 

Airborne Data Integration: Integrating airborne datasets to enhance and fine-tune satellite algorithms, further improving the precision of satellite-based measurements.

 

Machine Learning Applications: Harnessing machine learning techniques to detect various features in satellite imagery, enhancing our ability to automatically identify and analyze key patterns or phenomena.

 

Through these diverse approaches, Dr. Christopher's group contributes significantly to advancing our understanding of the intricate dynamics within the earth-atmosphere system, with a specific focus on leveraging satellite technology for environmental research.