A21A:
Advances in Data Assimilation, Inverse Modeling, and Forecasting of Trace Gases and Aerosols II


Submit an Abstract to this Session


Session ID#: 58284

Session Description:
This session aims to bring together recent studies in local, regional, and global data assimilation (DA) and inverse modeling of the sources and sinks of trace gases and aerosols. We invite contributions on topics, including but not limited to: 1) advances in data assimilation, including new methodologies (ensemble, hybrid, non-Gaussian) to estimate emissions as well as to integrate observations at diverse spatial and temporal scales; 2) applications of DA systems in studies related to air quality, biogeochemistry, and weather/chemistry interactions; 3) novel methods in assimilating ‘big’ data, complex, and/or new observations; 4) characterization of model and observation errors and uncertainties and reconciliation of top-down and bottom-up estimates; 5) transition to operational or near-real time applications of these tools; and 6) evaluation of aerosol/chemical models against observations, including reanalysis products.
Primary Convener:  Avelino F Arellano Jr, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States
Conveners:  Sebastian Milinski, University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Virginie Buchard, NASA Goddard Space Flight Center, Greenbelt, MD, United States and Juli Rubin, U.S. Naval Research Laboratory, Remote Sensing Division, Washington, DC, United States
Primary Liaison:  Daven K Henze, University of Colorado Boulder, Boulder, United States
Chairs:  Sebastian Milinski, University of California Los Angeles, Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Juli Rubin, US Naval Research Laboratory, Washington, DC, United States, Daven K Henze, University of Colorado Boulder, Boulder, United States and Virginie Buchard, NASA Goddard Space Flight Center, Greenbelt, MD, United States
OSPA Liaison:  Avelino F Arellano Jr, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States

Cross-Listed:
  • B - Biogeosciences
  • GC - Global Environmental Change
  • IN - Earth and Space Science Informatics
  • NG - Nonlinear Geophysics
Index Terms:

0322 Constituent sources and sinks [ATMOSPHERIC COMPOSITION AND STRUCTURE]
1910 Data assimilation, integration and fusion [INFORMATICS]
1986 Statistical methods: Inferential [INFORMATICS]
3260 Inverse theory [MATHEMATICAL GEOPHYSICS]

Abstracts Submitted to this Session:

Current State of the global operational aerosol multi-model ensemble: an update from the International Cooperative for Aerosol Prediction (ICAP) (369102)
Peng Xian1, Jeffrey S. Reid2, Edward J Hyer3, Charles R Sampson4, Juli Rubin5, Melanie Ades6, Nicole Asencio7, Sara Basart8, Angela Benedetti9, Partha Sarathi S Bhattacharjee10, Malcome Brooks11, Peter Richard Colarco12, Arlindo da SIlva12, Tom Eck13, Jonathan Guth14, Oriol Jorba15, Rostislav Kouznetsov16, Zak Kipling6, Mikhail Sofiev16, Carlos Pérez García-Pando17, Yaswant Pradhan11, Taichu Y Tanaka18, Jun Wang19, Douglas L Westphal20, Keiya Yumimoto21 and Jianglong Zhang22, (1)Naval Research Lab Monterey, Marine Meteorology, Monterey, CA, United States, (2)Naval Research Laboratory, Marine Meteorology Division, Monterey, United States, (3)Harvard-Smithsonian CFA, Cambridge, United States, (4)NRL Monterey, Monterey, CA, United States, (5)US Naval Research Laboratory, Washington, DC, United States, (6)European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, (7)dMétéo-France, Toulouse, France, (8)Barcelona SupercomputingCenter, Barcelona, Spain, (9)European Center for Medium-Range Weather Forecasts, Reading, United Kingdom, (10)IMSG at NOAA/NWS/NCEP/EMC, College Park, MD, United States, (11)UK Met Office, Reading, United Kingdom, (12)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (13)USRA, Greenbelt, United States, (14)Météo-France, Toulous, France, (15)Barcelona Supercomputing Center, Earth Sciences, Barcelona, Spain, (16)Finnish Meteorological Institute, Helsinki, Finland, (17)Barcelona Supercomputing Center (BSC), Barcelona, Spain, (18)Japan Meteorological Agency, Tokyo, Japan, (19)NOAA/NWS/NCEP/EMC, College Park, MD, United States, (20)Naval Research Lab, Monterey, CA, United States, (21)Kyushu University, Fukuoka, Japan, (22)U of N Dakota-Atmos Sciences, Grand Forks, ND, United States
Recent updates in the aerosol model of the Integrated Forecasting System (IFS) of the ECMWF and their impact on skill scores (377727)
Samuel Remy1, Olivier Boucher2, Zak Kipling3, Josué Bock4, Pierre Nabat5, Martine Michou4, Vincent Huijnen6, Melanie Ades3, Johannes Flemming7 and Richard J Engelen3, (1)CNRS, Paris Cedex 16, France, (2)Institut Pierre Simon Laplace, Paris, France, (3)European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, (4)Météo-France Toulouse, Toulouse Cedex 01, France, (5)METEO FRANCE/CNRM, Toulouse, France, (6)Royal Netherlands Meteorological Institute, De Bilt, Netherlands, (7)European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX UK, Reading, United Kingdom
Resolving the information in large-scale inversions: application to CMS-Flux (Invited) (419624)
Kevin W Bowman, NASA Jet Propulsion Laboratory, Pasadena, United States, Nicolas Bousserez, University of Colorado at Boulder, Boulder, CO, United States, Junjie Liu, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, Meemong Lee, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States and Daven K Henze, University of Colorado Boulder, Boulder, United States
Sensitivity of a Bayesian source-term estimation model to spatiotemporal sensor resolution (360656)
Derek D Jensen1, Donald D Lucas1, Katherine A Lundquist2 and Lee G Glascoe1, (1)Lawrence Livermore National Laboratory, Livermore, CA, United States, (2)Lawrence Livermore National Laboratory, Livermore, United States
Effect of including model uncertainties in HYSPLIT inverse modeling (356418)
Tianfeng Chai1, Ariel F Stein2 and Fong Ngan1, (1)University of Maryland College Park, College Park, MD, United States, (2)NOAA Air Resources Laboratory, College Park, MD, United States
Towards Refining U.S. Ammonia Emissions with CrIS Observations through Four-dimensional Variational Assimilation (363815)
Shannon Capps1, Mahmoudreza Momeni1, Matthew Lombardo2, Amir Hakami3, Daven K Henze4, Steven P. Thomas5, Peter J Rayner6, Jeremy Silver7 and CMAQ adjoint development team, (1)Drexel University, Philadelphia, PA, United States, (2)Johns Hopkins University, Baltimore, MD, United States, (3)Carleton University, Department of Civil and Environmental Engineering, Ottawa, ON, Canada, (4)University of Colorado Boulder, Boulder, United States, (5)University of Melbourne, Melbourne, VIC, Australia, (6)The University of Melbourne, Melbourne, Australia, (7)School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
Using machine learning to bias correct a tropospheric ozone simulation (412392)
Peter Ivatt, University of York, York, YO10, United Kingdom and Mathew J Evans, University of York, Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, York, United Kingdom
GEOS-Chem Adjoint Inversion of SO2 and NOx Emissions with Multi-sensor Data over China (386246)
Yi Wang, The University of Iowa, Center for Global & Regional Environmental Research, Iowa City, IA, United States, Jun Wang, University of Iowa, Iowa City, IA, United States, Xiaoguang Xu, University of Iowa, Chemical and Biochemical Engineering, Iowa City, United States, Daven K Henze, University of Colorado Boulder, Boulder, United States and Zhen Qu, University of Colorado at Boulder, Mechanical Engineering, Boulder, CO, United States

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