Seminar: Dr. John Yorks | NASA Goddard Space Flight Center
In-Person PHYS 401
Location
Physics : 401
Date & Time
September 24, 2025, 11:00 am – 12:00 pm
Description
TITLE: “The Three-Way Intersection Between Airborne Field Campaigns,
Spaceborne Lidar Sensors, and Machine Learning”
ABSTRACT: Aerosols and clouds have spatiotemporal variability and diurnal processes that play critical roles in the Earth’s radiation budget, weather, and air quality. Backscatter lidar measurements provide cloud and aerosol vertical profiles during nighttime and in complex multi-layer scenes during day, when passive sensors are unable to reliably provide cloud and aerosol properties. This talk will discuss our group’s work at NASA GSFC, which is on the cutting edge of lidar instrumentation, algorithm development, and science analysis. Since 2000, we have worked with engineers to build new and compact lidar systems for both airplanes and spacecraft. These include the Cloud Physics Lidar (CPL) and Roscoe lidar for NASA aircraft, as well as Cloud-Aerosol Transport System (CATS) lidar that operated on the International Space Station from 2015 to 2017. Data from CPL and field campaigns like the Investigation of Microphysics and Precipitation in Atlantic Coast Threatening Snowstorms (IMPACTS) enable us to test new technologies for space and provide robust datasets to simulate spaceborne sensors. Using these lidar datasets, our group has developed machine learning and synergistic retrieval techniques to improve the quality of daytime spaceborne lidar data and advance our understanding of aerosol and cloud spatiotemporal variability. I will provide an overview of this work as well as some of the lidar data analysis our group has performed to better understand this spatiotemporal variability, as well as how some of these machine learning methods can be applied to future lidar datasets.
Spaceborne Lidar Sensors, and Machine Learning”
ABSTRACT: Aerosols and clouds have spatiotemporal variability and diurnal processes that play critical roles in the Earth’s radiation budget, weather, and air quality. Backscatter lidar measurements provide cloud and aerosol vertical profiles during nighttime and in complex multi-layer scenes during day, when passive sensors are unable to reliably provide cloud and aerosol properties. This talk will discuss our group’s work at NASA GSFC, which is on the cutting edge of lidar instrumentation, algorithm development, and science analysis. Since 2000, we have worked with engineers to build new and compact lidar systems for both airplanes and spacecraft. These include the Cloud Physics Lidar (CPL) and Roscoe lidar for NASA aircraft, as well as Cloud-Aerosol Transport System (CATS) lidar that operated on the International Space Station from 2015 to 2017. Data from CPL and field campaigns like the Investigation of Microphysics and Precipitation in Atlantic Coast Threatening Snowstorms (IMPACTS) enable us to test new technologies for space and provide robust datasets to simulate spaceborne sensors. Using these lidar datasets, our group has developed machine learning and synergistic retrieval techniques to improve the quality of daytime spaceborne lidar data and advance our understanding of aerosol and cloud spatiotemporal variability. I will provide an overview of this work as well as some of the lidar data analysis our group has performed to better understand this spatiotemporal variability, as well as how some of these machine learning methods can be applied to future lidar datasets.
