A060
Application of Machine Learning and Artificial Intelligence in Observing and Modeling, and Analyzing Our Atmosphere III Posters

Wednesday, 9 December 2020: 04:00-20:59
Poster
Primary Convener:  Tianle Yuan, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Joint Center for Earth Systems Technology, Baltimore, MD, United States
Convener:  Chenxi Wang, University of Maryland College Park, College Park, MD, United States
Primary Liaison:  Tianle Yuan, NASA GSFC, Greenbelt, MD, United States
Chairs:  Tianle Yuan, NASA GSFC, Greenbelt, MD, United States and Chenxi Wang, University of Maryland College Park, College Park, MD, United States
OSPA Liaison:  Tianle Yuan, NASA GSFC, Greenbelt, MD, United States
 
A land use model for high-resolution black carbon estimation in Oakland, CA: A comparison of different machine learning models’ performance in spatial prediction (775137)
Minmeng Tang, Davis, California, United States; University of California Davis, Department of Land, Air and Water Resources, Davis, CA, United States and Deb Niemeier, University of Maryland College Park, Civil and Environmental Engineering, College Park, MD, United States
 
Absorbing aerosols optical depth from satellite ultra-violet aerosol index: a deep learning approach (704149)
Jiyunting Sun1, Joris P Veefkind1, Peter F. J. van Velthoven2 and Pieternel Levelt2, (1)Royal Netherlands Meteorological Institute, De Bilt, 3730, Netherlands, (2)Royal Netherlands Meteorological Institute, De Bilt, Netherlands
 
Air Quality Data Time Series Modeling Using Deep Recurrent Neural Networks on Sentinel-5p Products (690259)
Maria Kaselimi1, Athanasios Voulodimos2, Nikolaos Doulamis1, Anastasios Doulamis1 and Demitris Delikaraoglou1, (1)National Technical University of Athens (NTUA), Athens, Greece, (2)University of West Attica (UNIWA), Athens, Greece
 
Applying Machine Learning Technique for Winter Ozone Forecasting in the Uintah Basin (759395)
Huy Tran and Marc L Mansfield, Utah State University, Bingham Research Center, Logan, UT, United States
 
Deep Multi-Sensor Domain Adaptation on Active and Passive Satellite Remote Sensing Data (701402)
Jianwu Wang1, Xin Huang1, Sahara Ali2, Sanjay Purushotham2, Chenxi Wang3 and Zhibo Zhang2, (1)University of Maryland Baltimore County, Information Systems, Baltimore, MD, United States, (2)University of Maryland Baltimore County, Baltimore, MD, United States, (3)University of Maryland Baltimore County, Joint Center for Earth Systems Technology, Baltimore, MD, United States
 
Determination of Wind from Radar Wind Profilers in the Presence of Bird Clutter Using Machine Learning (703218)
Vasura Jayaweera, Western University Canada, London, ON, Canada, Robert J Sica, Univ Western Ontario, London, ON, Canada, Maxime Hervo, MeteoSwiss Federal Office of Meteorology and Climatology, Zurich, Switzerland, Maëlle Romero Grass, EPFL Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland, Alexis Berne, EPFL Swiss Federal Institute of Technology Lausanne, LTE, ENAC, Lausanne, Switzerland and Alexander Haefele, Federal Office of Meteorology and Climatology MeteoSwiss, Remote Sensing Group, Payerne, Switzerland
 
Estimate of PM2.5 concentration in New York State during fire seasons of 2016 – 2019 using machine learning (754655)
Wei-Ting Hung1, Sarah Lu2, Stefano Alessandrini3, Rajesh Kumar3, Chin-An Lin4, Ravan Ahmadov5 and Eric James6, (1)Atmospheric Science Research Center, Albany, NY, United States, (2)University at Albany State University of New York, Albany, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States, (4)University at Albany State University of New York, Albany, NY, United States, (5)NOAA ESRL/CSL, Boulder, CO, United States, (6)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
 
Evaluating Image Derived Estimates of Pavement State, Visibility, and Precipitation (760273)
Brittany Welch, University of Utah, Salt Lake City, UT, United States and John Horel, University of Utah, Atmospheric Sciences, Salt Lake City, UT, United States
 
Predicting atmospheric particle number concentration from roadway surveillance video (702498)
Christopher Tessum, University of Illinois at Urbana Champaign, Civil and Environmental Engineering, Urbana, IL, United States, Mei W Tessum, University of Illinois at Urbana Champaign, Agricultural and Biological Engineering, Urbana, IL, United States and Manav Mehra, University of Illinois at Urbana Champaign, Urbana, United States
 
The ChinaHighPM2.5 data set: generation, validation, and spatiotemporal variations from 2000 to 2018 in China (687760)
Jing Wei, Beijing Normal University, Beijing, China and Zhanqing Li, Univ of Maryland College Park, College Park, MD, United States
 
Using Machine Learning to Identify Planetary Boundary Layer Heights for Ceilometer-Based Aerosol Backscatter Retrievals (770520)
Jennifer Sleeman1, Vanessa Caicedo2, Dorsa Ziaei1, Milton Halem1, Belay Demoz3 and Ruben Delgado4, (1)University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States, (2)UMBC/GSFC, JCET, Savage, MD, United States, (3)University of Maryland Baltimore County, Department of Physics & JCET, Baltimore, United States, (4)Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, United States
 
Using Surface Observations to Select Features and to Predict Extreme Ozone in Texas via Generalized Additive Modeling (GAM), the Synthetic Minority Oversampling Technique (SMOTE), and a Tail Dependence Optimization Method (695361)
Benjamin Brown-Steiner1, Xiong Zhou1 and Matthew James Alvarado2, (1)Atmospheric and Environmental Research, Lexington, MA, United States, (2)AER, Inc., Lexington, MA, United States
 
Volcanic SO2 Effective Layer Height Retrieval with OMI Using a Machine Learning Driven Approach (735968)
Nikita Markovich Fedkin, University of Maryland College Park, College Park, MD, United States, Can Li, Earth System Science Interdisciplinary Center, College PARK, MD, United States, Nickolay Anatoly Krotkov, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Diego G Loyola, German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Oberpfaffenhofen, Germany and Pascal Hedelt, Institut für Methodik der Fernerkundung, Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Weßling, Germany
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