Inverse modeling of NH3 sources using CrIS remote sensing measurements

Abstract

Spatiotemporal uncertainty in $\mathrm{NH_{3}}$ emissions in the US hinders prediction of environmental effects of atmospheric $\mathrm{NH_3}$ . We conducted 4D-Var inversions using CrIS remote-sensing observations and GEOS-Chem to estimate monthly $\mathrm{NH_{3}}$ emissions over the contiguous US at the 0.25 ^° × 0.3125 ^° resolution in 2014, finding they are 33% higher than the prior emissions which likely underestimated most agricultural emissions, especially intense springtime fertilizer and livestock sources over the Central US. However, decreases were found in the Central Valley, southern Minnesota, northern Iowa and southeastern North Carolina during warm months. These updates increased the correlation coefficient between modeled monthly mean $\mathrm{NH_3}$ and surface observations from 0.53 to 0.84, and reduced the normalized mean bias of annual mean simulated $\mathrm{NH_3}$ and wet $\mathrm{NH_{4}^{+}}$ by a factor of 1.3 to 12.7. Our satellite-based inversion approach thus holds promise for improving estimates of $\mathrm{PM_{2.5}}$ and reactive nitrogen deposition throughout the world where $\mathrm{NH_3}$ measurements are scarce.

Keywords

• CrIS NH3
• 4D-Var inversion NH3 emissions