Principal Component Analysis Technique for Satellite Remote Sensing of Air Pollution

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Dr. Can Li

NASA GSFC/UMD ESSIC

Monday October 29, 2018, 12:00-1:00 PM

ESSIC Conference Room 4102, 5825 University Research Ct, College Park, MD 20740

Abstract:

Short-lived trace gas pollutants such as sulfur dioxide (SO2), despite their relatively low ambient concentrations, play an important role in air quality and climate. Routine, global monitoring of air pollution from space became possible with the launch of hyperspectral ultraviolet-visible (UV-Vis) instruments in the 1990s, and continues to be an active research area today. Retrieval techniques applied to these instruments have advanced over time, but substantial uncertainties still exist, as the relatively small signals from trace gases are often masked by various strong interferences. In this talk, I will introduce a recently developed trace gas retrieval approach based on principal component analysis (PCA) of satellite measured radiance data. I will describe the general framework of this data-driven approach and also demonstrate its applications to different species (e.g., SO2) and instruments such as the Dutch/Finnish Ozone Monitoring Instrument (OMI) aboard NASA Aura satellite. Finally, I will discuss the challenges in producing consistent, long-term data records of trace gases across multiple instruments and platforms, and how the PCA retrieval approach can help to mitigate them.

Bio-sketch:

Dr. Can Li is an Associate Research Scientist working at ESSIC and NASA GSFC Atmospheric Chemistry and Dynamics Laboratory. His research currently focuses on the development of trace gas retrieval algorithms using spaceborne UV-Vis spectrometers. His other research interests include field and airborne measurements of air pollutants and instrumentation. He joined ESSIC as a Postdoctoral Research Associate after earning his PhD in Atmospheric and Oceanic Science from the University of Maryland in 2008.

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Date

Oct 29 2018
Expired!

Time

8:00 am - 6:00 pm

Category