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

Abstract

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 obser-5 vation 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 10 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 15 shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO 2 products and IASI NH 3 products, showing substantially improved visualization of trace gas distribution and local gradients.1 Atmos. Meas. Tech. Discuss., https://doi

A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements

“…For these reasons, whenever possible, and especially for validation, it is always better to use raw L2 data. We refer the reader to Levy et al (), Schutgens et al (), and Sun et al () for a discussion of these issues and for an overview of different averaging approaches.…”

High-resolution (0.05° × 0.05°) NOx emissions in the Yangtze River Delta inferred from OMI

“…Thus, sampling from multiple days increases the horizontal resolution of data. Our oversampling approach is different from Fioletov et al, who filled a grid cell with data from pixels within a certain distance (e.g., 30 km), which implied a spatial smoothing (Fioletov et al, 2011;Krotkov et al, 2016;Sun et al, 2018).…”

A Global Spatial-Temporal Land Use Regression Model for Nitrogen Dioxide Air Pollution

“…While OMI reports data at a coarser resolution (24 × 13 km 2 ) than TROPOMI, the measurements are over a multi-decadal timeframe, which makes it advantageous for performing retrospective long-term trend studies [9][10][11], such as this one. Satellite NO2 measurements can be reported at finer spatial resolution (~1 x 1 km 2 ) when aggregated to monthly, seasonal or annual timescales using a process called oversampling [12,13]. Global LUR models for annual NO2 are available at high spatial resolutions (100m) for single snapshots in time [14], daytime and nighttime 2017 average global LUR models are available [15], and deterministic global models adjusting OMI and TROPOMI measurements with the Geos-chem chemical transport model exist at moderate spatial resolutions (~2.8km 2 ) [16].…”