Identifying and accounting for the Coriolis Effect in satellite NO₂ observations and emission estimates

Abstract

Abstract. Recent developments in atmospheric remote sensing from satellites have made it possible to resolve daily emission plumes from industrial point sources, around the globe. Wind rotation aggregation coupled with statistical fitting is commonly used to extract emission estimates from these observations. These methods are used here to investigate how the Coriolis Effect influences the trajectory of observed emission plumes, and to assess the impact of this influence on satellite derived emission estimates. Of the 17 industrial sites investigated, nine showed the expected curvature for the hemisphere they reside in. Five showed no or negligible curvature, and two showed opposing or unusual curvature. The sites which showed conflicting curvature all reside in topographically diverse regions, where strong meso-gamma scale (2–20 km) turbulence dominates over larger synoptic circulation patterns. For high curvature cases the assumption that the wind-rotated plume aggregate is symmetrically distributed across the downwind axis breaks down, which impairs the quality of statistical fitting procedures. Using NOx emissions from Matimba power station as a test case, not compensating for Coriolis curvature resulted in an10 underestimation of ∼ 9 % on average for years 2018 to 2021. This study is the first formal observation of the Coriolis Effect and its influence on satellite observed emission plumes, and highlight both the variability of emission calculation methods and the need for a standardised scheme for this data to act as evidence for regulators.

On the Theory of the Divergence Method for Quantifying Source Emissions From Satellite Observations

“…A potential problem is that this procedure of computing second‐order derivatives of real satellite images will be very sensitive to noise. Alternatively, if one manages to fit a local coordinate system to individual point sources prior to taking the divergence, one can follow the Gaussian plume example given in this paper and include diffusion by adopting a cross‐wind speed V = Uy /(2 x ) together with along‐wind speed U .An additional factor that must be considered for very large plumes are Coriolis effects that add an additional (virtual) transport velocity (see, e.g., Potts et al., 2023, for more details). The normal fluxes at the bottom and top of the atmosphere vanish . The assumption regarding a vanishing flux at the top of the atmosphere is certainly valid for space‐based remote sensing if one considers the total columns.…”

“…An additional factor that must be considered for very large plumes are Coriolis effects that add an additional (virtual) transport velocity (see, e.g., Potts et al., 2023, for more details).…”

On the theory of the divergence method for quantifying source emissions from satellite observations

“…An additional factor that must be considered for very large plumes are Coriolis effects that add an additional (virtual) transport velocity (see, e.g., Potts et al, 2023, for more details).…”