Articles | Volume 11, issue 12
https://doi.org/10.5194/amt-11-6679-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-6679-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2018
Research article |
| 18 Dec 2018
| 18 Dec 2018
A physics-based approach to oversample multi-satellite, multispecies observations to a common grid
Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Karen Cady-Pereira
Atmospheric and Environmental Research, Lexington, MA, USA
Christopher Chan Miller
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Kelly Chance
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Lieven Clarisse
Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Université libre de Bruxelles (ULB), Brussels, Belgium
Pierre-François Coheur
Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Université libre de Bruxelles (ULB), Brussels, Belgium
Gonzalo González Abad
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Guanyu Huang
Department of Environmental and Health Sciences, Spelman College, Atlanta, GA, USA
Xiong Liu
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
Martin Van Damme
Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Université libre de Bruxelles (ULB), Brussels, Belgium
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Mark Zondlo
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
Model code and software
Matlab code for OMI oversampling K. Sun https://github.com/Kang-Sun-CfA/Oversampling_matlab/
Short summary
An agile, physics-based approach is developed to oversample irregular satellite observations to a high-resolution common grid. Instead of assuming each sounding as a point or a polygon as in previous methods, the proposed physical oversampling represents soundings as distributions of sensitivity on the ground. This sensitivity distribution can be determined by the spatial response function of each satellite sensor, parameterized as generalized 2-D super Gaussian functions.
An agile, physics-based approach is developed to oversample irregular satellite observations to...
