A113
Remote sensing of atmospheric dust and ash: techniques and applications
Session ID#: 75251
Session Description:
Desert dust and volcanic ash degrade air quality, modulate the radiation budget, influence the formation and properties of clouds, and affect the biogeochemical cycles. Remote sensing is one of indispensable tools for assessing these influences. This session invites latest advances in remote sensing techniques and the applications of remote sensing products for characterizing dust/ash distribution and understanding their influences. The topics of interest include: (1) advances in dust/ash remote sensing techniques (passive and active) and theories (inversion algorithm, machine learning), (2) innovative instruments for dust/ash remote sensing, (3) real-time detection of dust storms and volcanic eruptions, (4) characterizations of sources, transport, and deposition of dust/ash particles, (5) observations of dust/ash optical and radiative properties from UV to thermal infrared, (6) studies of dust influence on air quality, weather, climate, and biogeochemical cycles, and (7) novel applications of remote sensing products to improve the model simulation of dust/ash.
Index Terms:
0305 Aerosols and particles [ATMOSPHERIC COMPOSITION AND STRUCTURE]
0370 Volcanic effects [ATMOSPHERIC COMPOSITION AND STRUCTURE]
Primary Convener: Zhibo Zhang, University of Maryland Baltimore County, Baltimore, MD, United States
Conveners: Hongbin Yu, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Bing Pu, University of Kansas, Department of Geography and Atmospheric Science, Lawrence, KS, United States and Gregory L Schuster, NASA Langley Research Center, Hampton, VA, United States
Primary Liaison: Hongbin Yu, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Combining Satellite Remote Sensing and Numerical Modelling to Improve the Representation of Atmospheric Dust in Aerosol Models (Invited) (494686)
Kerstin Schepanski1, Barry Baker2, Ralph A Kahn3, Daniel Tong4 and Stefanie Feuerstein1, (1)Leibniz Institute for Tropospheric Research, Leipzig, Germany, (2)NOAA ARL, College Park, United States, (3)NASA/Goddard Space Flight Ctr, Greenbelt, MD, United States, (4)NOAA Air Resources Laboratory, College Park, MD, United States
Evaluating NASA GEOS Simulation of Trans-Atlantic Dust Transport and Deposition with Satellite Remote Sensing Products (494698)
Hongbin Yu, NASA Goddard Space Flight Center, Earth Sciences Division, Greenbelt, MD, United States, Huisheng Bian, NASA Goddard Space Flight Ctr, Greenbelt, MD, United States, Qian Tan, Bay Area Environmental Research Institute, San Jose, CA, United States, Mian Chin, NASA Goddard SFC, Greenbelt, MD, United States, Dongchul Kim, Universities Space Research Association, Greenbelt, MD, United States and Paul A Ginoux, NOAA GFDL, Princeton, NJ, United States
Evolution of Volcanic Emissions Over Time: A Case Study of Icelandic Eruptions. (Invited) (496586)
Verity J. B. Flower, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Universities Space Research Association Greenbelt, Greenbelt, MD, United States and Ralph A Kahn, NASA/Goddard Space Flight Ctr, Greenbelt, MD, United States
A New Retrieval Algorithm of the Thermal Infrared Optical Depth of Dust based on the Combined CALIOP and IIR Observations (505989)
Jianyu Zheng, University of Maryland Baltimore County, Department of Physics, Baltimore, MD, United States, Anne Garnier, Science Systems and Applications, Inc. Hampton, Hampton, VA, United States, Zhibo Zhang, University of Maryland Baltimore County, Baltimore, MD, United States, Hongbin Yu, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Philippe Dubuisson, Lab. d'Optique Atmosphérique, Villeneuve D'Ascq, France and Jacques Pelon, CNRS, Paris Cedex 16, France
Estimate of dust poleward transport efficiency using two spaceborne lidars. (519300)
Qian Tan, Bay Area Environmental Research Institute Moffett Field, Moffett Field, CA, United States, Hongbin Yu, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Huisheng Bian, NASA Goddard Space Flight Ctr, Greenbelt, MD, United States, John E Yorks, SSAI/NASA GSFC, Greenbelt, MD, United States and Mian Chin, NASA Goddard SFC, Greenbelt, MD, United States
Seasonal prediction potential for springtime dustiness in the U.S. (527593)
Bing Pu, University of Kansas, Department of Geography and Atmospheric Science, Lawrence, KS, United States, Paul A Ginoux, NOAA GFDL, Princeton, NJ, United States, Sarah B. Kapnick, NOAA Geophysical Fluid Dynamics Laboratory, Princeton, United States and Xiaosong Yang, UCAR, Princeton, NJ, United States; NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States
A High Spatiotemporal Resolution Dust Tracking Framework based on MSG‐SEVIRI (586802)
Manzhu Yu, Pennsylvania State University Main Campus, University Park, PA, United States, Hui Xu, University of Maryland, Earth System Science Interdisciplinary Center, College Park, MD, United States and Qian Liu, George Mason University Fairfax, Fairfax, VA, United States
Evaluation of dust emission and transport simulated by global climate models with satellite observations from CALIOP, MODIS, and MISR (592513)
Mingxuan Wu1, Xiaohong Liu1, Kang Yang2, Yang Shi1, Anton Darmenov3, Hongbin Yu4, Zhien Wang5, Tao Luo6, Hailong Wang7, Yan Feng8, Chenglai Wu9 and Ziming Ke10, (1)University of Wyoming, Department of Atmospheric Science, Laramie, WY, United States, (2)University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States, (3)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)University of Colorado Boulder, Boulder, CO, United States, (6)Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,CAS Chinese Academy of Sciences, Hefei, China, (7)Pacific Northwest National Laboratory, Richland, WA, United States, (8)Argonne National Laboratory, Environmental Science Division, Argonne, IL, United States, (9)Insititute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, (10)University of Wyoming, Laramie, United States
Satellite Detection of Dust With Deep Convolutional Neural Networks (595636)
Changjie Cai1, Jangho Lee2, Yingxi Rona Shi3,4, Camille Zerfas5, Jianwu Wang6, Zhibo Zhang7, Aryya Gangopadhyay7, Matthias K Gobbert7 and Carlos Barajas7, (1)University of Oklahoma Health Sciences Center, University of Oklahoma, Oklahoma City, United States, (2)Texas A&M University, College Station, TX, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)Joint Center for Earth Systems Technology UMBC, Baltimore, ND, United States, (5)Clemson University, Clemson, United States, (6)University of Maryland Baltimore County, Information Systems, Baltimore, MD, United States, (7)University of Maryland Baltimore County, Baltimore, MD, United States
