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

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 period to a target level 3 grid that is significantly finer than the sizes of 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 methods. 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 (FOVs) 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 size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids 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, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.