A061
Applications of Machine Learning Algorithms in Modeling Atmospheric Aerosols, Clouds, and Radiation II Posters

Wednesday, 9 December 2020: 04:00-20:59
Poster
Primary Convener:  ManishKumar Shrivastava, Pacific Northwest National Labs, Richland, WA, United States
Conveners:  Ziheng Sun, George Mason University Fairfax, Center for Spatial Information Science and Systems, Fairfax, VA, United States, Daniel Tong, George Mason University Foundation Inc., Columbia, MD, United States and Anthony S Wexler, University of California Davis, Davis, CA, United States
Primary Liaison:  ManishKumar Shrivastava, Pacific Northwest National Labs, Richland, WA, United States
Chairs:  ManishKumar Shrivastava, Pacific Northwest National Labs, Richland, WA, United States and Ziheng Sun, George Mason University Fairfax, Center for Spatial Information Science and Systems, Fairfax, VA, United States
OSPA Liaison:  Daniel Tong, George Mason University Foundation Inc., Columbia, MD, United States
 
Determinants of fog and low stratus clouds in continental central Europe quantified using machine learning and satellite data (688998)
Eva Pauli, Jan Cermak and Hendrik Andersen, Karlsruhe Institute of Technology, Karlsruhe, Germany
 
Off the Grid: The use of Gaussian mixture models with Lagrangian transport and dispersion models for density estimation and feature identification. (744098)
Alice Crawford, NOAA Air Resources Laboratory, College Park, MD, United States
 
Operator Replacement Using Machine Learning with Conservation Laws (699766)
Obin Sturm and Anthony S Wexler, University of California Davis, Davis, CA, United States
 
Particulate Matter Forecasting in Los Angeles County with Sparse Ground-Based Sensor Data Analytics (669826)
Emmanuel Cocom1, Pratyush Muthukumar1, Jeanne Holm2, Dawn Comer2, Anthony Lyons2, Irene Burga2, Christa A Hasenkopf3, Chisato Calvert3 and Mohammad Pourhomayoun1, (1)California State University Los Angeles, Department of Computer Science, Los Angeles, CA, United States, (2)City of Los Angeles, Los Angeles, CA, United States, (3)OpenAQ, Washington, DC, United States
 
Quantification of Sensitivities for Aerosol-Cloud Interactions in Marine Boundary Layer Clouds with Machine Learning (726618)
Lukas Zipfel, Hendrik Andersen and Jan Cermak, Karlsruhe Institute of Technology, Karlsruhe, Germany
 
Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements (714536)
Arshad Arjunan Nair and Fangqun Yu, SUNY at Albany, Atmospheric Sciences Research Center, Albany, NY, United States
 
Using Machine Learning to Superob Observations for Use in Aerosol Data Assimilation (682625)
Finn Boire, University of California Berkeley, Physics, Berkeley, CA, United States, Catherine Thomas, IMSG at NOAA NWS NCEP EMC, College Park, MD, United States and Cory Martin, RedLine at NOAA NWS NCEP EMC, College Park, MD, United States
 
Using Neural Networks and Code Optimization to Accelerate Radiation Computations for Dynamical Models (725292)
Peter Ukkonen, University of Copenhagen, København K, Denmark, Kristian Pagh Nielsen, Danish Meteorological Institute, Copenhagen East, Denmark, Robert Pincus, University of Colorado at Boulder, Boulder, CO, United States, Eigil Kaas, University of Copenhagen, Nils Bohr Institute, København K, Denmark and Robin J. Hogan, ECMWF, Reading, United Kingdom
 
Using Statistical and Machine Learning Models with Remotely Sensed Data to Estimate PM2.5 in the San Francisco Bay Area (709885)
Anna Boser, University of California Santa Barbara, Bren School of Environmental Science and Management, Santa Barbara, CA, United States, Christian Ann White, Universities Space Research Association at NASA/MSFC, Huntsville, United States and Mohammad Z. Al-Hamdan, Universities Space Research Association at NASA/MSFC, Huntsville, AL, United States
 
VOODOO — A deep learning approach for reVealing supercOOled liquiD beyOnd lidar attenuatiOn (677683)
Willi Schimmel1, Heike Kalesse1 and Patric Seifert2, (1)University of Leipzig, Leipzig, Germany, (2)Leibniz Institute for Tropospheric Research - TROPOS, Leipzig, Germany
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