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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-253
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Aug 2018

Research article | 23 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

Kang Sun1, Lei Zhu2, Karen Cady-Pereira3, Christopher Chan Miller4, Kelly Chance4, Lieven Clarisse5, Pierre-François Coheur5, Gonzalo Gonzalez Abad4, Guanyu Huang6, Xiong Liu4, Martin Van Damme5, Kai Yang7, and Mark Zondlo8 Kang Sun et al.
  • 1Research and Education in eNergy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
  • 2School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
  • 3Atmospheric and Environmental Research, Lexington, MA, USA
  • 4Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA
  • 5Université libre de Bruxelles (ULB), Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Brussels, Belgium
  • 6Department of Environmental & Health Sciences, Spelman College, Atlanta, GA, USA
  • 7Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
  • 8Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (Level 2) to a regular grid (Level 3). When averaging Level 2 data over a long time period to a target Level 3 grid significantly finer than Level 2 pixels, this process is referred to as "oversampling'". An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous approaches. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOV) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid resolution, the ground size of FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for high resolution oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grid resolutions finer than ~16 km. The generalized 2-D super Gaussian function also enables smoothing of the Level 3 results by decreasing the shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO2 products and IASI NH3 products, showing substantially improved visualization of trace gas distribution and local gradients.

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Short summary
An agile, conceptually-simple, 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 approaches, the proposed method 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, conceptually-simple, physics-based approach is developed to oversample irregular...
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