Yongwan Chun
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Dissertation
PHYSICAL STUDIES OF AIRBORNE POLLEN AND PARTICULATES UTILIZING MACHINE LEARNING by Xun Liu APPROVED BY SUPERVISORY COMMITTEE: David John Lary, Chair Roderick Heelis Robert Glosser Lunjin Chen Fabiano Rodrigues Copyright c 2019 Xun Liu All rights reserved PHYSICAL STUDIES OF AIRBORNE POLLEN AND PARTICULATES UTILIZING MACHINE LEARNING by XUN LIU, BS, MS DISSERTATION Presented to the Faculty of The University of Texas at Dallas in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY IN PHYSICS THE UNIVERSITY OF TEXAS AT DALLAS December 2019 ACKNOWLEDGMENTS I would like to thank all those who supported my research and dissertation. Without their help, I would never have completed this dissertation. I must express my deepest appreciation to my advisor Dr. David Lary for his guidance in the past three years. My heartfelt gratitude also goes to his patience and tolerance for all those mistakes I had once made. His warm support and insightful instructions helped me out of many hard times during the process of this research. I would also like to thank other members of my committee: Dr. Roderick Heelis, Dr. Robert Glosser, Dr. Lunjin Chen, and Dr. Fabiano Rodrigues for serving as my committee members and generously providing me with advice and comments. I am also grateful to all teammates in Dr. Lary's group: Daji Wu, Gebreab K. Zewdie and Lakitha Wijeratne, for their collaboration on my research. October 2019 iv PHYSICAL STUDIES OF AIRBORNE POLLEN AND PARTICULATES UTILIZING MACHINE LEARNING Xun Liu, PhD The University of Texas at Dallas, 2019 Supervising Professor: David John Lary, Chair This dissertation presents an approach for estimating the abundance of airborne pollen and particulates using a comprehensive description of the physical environment coupled with machine learning. -
Laboratory for Atmospheres PHILOSOPHY, ORGANIZATION, MAJOR ACTIVITIES, and 2001 HIGHLIGHTS
Laboratory for Atmospheres PHILOSOPHY, ORGANIZATION, MAJOR ACTIVITIES, AND 2001 HIGHLIGHTS January 2002 Impact of Simulated LIDAR Winds on Numerical Weather Prediction National Aeronautics and Space Administration Goddard Space Flight Center Greenbelt, MD 20771 NASA GODDARD SPACE FLIGHT CENTER Laboratory for Atmospheres PHILOSOPHY, ORGANIZATION, MAJOR ACTIVITIES, AND 2001 HIGHLIGHTS January 2002 Impact of Simulated LIDAR Winds on Numerical Weather Prediction The Laboratory for Atmospheres’ Data Assimilation Office (DAO) uses a modeling technique called OSSE (Observing System Simulation Experiment) to study atmospheric monitoring capabilities. In this unique approach, the OSSE synthesizes the observations of a proposed satellite instrument and uses them in a data assimilation to predict the instrument’s usefulness in forecasting. The cover shows simulations to evaluate various concepts for obtaining Doppler Wind Lidar (DWL) profiles from space. The drawing shows the cross-track coverage of a DWL in a 400 km orbit and the improved anomaly correlation for sea-level pressure in the southern hemisphere. The anomaly correlation shown on the ordinate in the chart indicates forecast accuracy. A perfect forecast has an anomaly correlation of 1.0, while the limit of useful forecast skill is about 0.6. Photo courtesy of R. Atlas, J. Ardizzone, J. Terry, and D. Bungato of the Data Assimilation Office; G.D. Emmitt of Simpson Weather Associates; and T. Carnahan and C. Congedo of the Mechanical Systems Analysis and Simulation Branch, NASA Goddard Space Flight Center. National Aeronautics and Space Administration Goddard Space Flight Center NASA Greenbelt, Maryland 20771 Laboratory Chief’s Summary Dear Reader: Welcome to the Laboratory for Atmospheres’ annual report for 2001. -
Curriculum Vitae Prof. David John Lary University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080 Telephone 972-489-2059
Curriculum Vitae Prof. David John Lary University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080 Telephone 972-489-2059. Email:[email protected]. Web: http://utdallas.edu/~david.lary/ OVERVIEW My work is in the area of Applied Physics for Societal Benefit with a focus on using remote sensing from robotic aerial vehicles and satellites coupled with machine learning to facilitate scientific discovery and decision support. All of the elements described in this CV have used or supported this goal. From my PhD at the University of Cambridge which described the world’s first chemical scheme within a weather forecasting model, to the autonomous earth observing system I have developed for NASA, to the use of artificial intelligence to deal with inter-instrument biases and produce climate data records, to the support of various decision support tools in areas such as smart agriculture, tornado prediction, disaster response, famine relief, health systems, to the detection of online fraud. All have used and furthered computational discovery. These contributions have been recognized by my peers through: Invited contributions to the Royal Society, National Academies, and CDC, two dedicated EGU symposia sessions, three prestigious fellowships, five editorial commendations, several million dollars in research funding, seven NASA awards, and more than sixty publications with over a thousand citations in the peer- reviewed literature with a Hirsch Index of seventeen. EDUCATION PhD In Photochemical Modeling of the Atmosphere 1987-1991 Department of Chemistry and Churchill College, University of Cambridge, England. First Class Double Honors BSc. In Physics & Chemistry, King's College, London, England.