Peiyang Cheng

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Atmospheric and Earth Science

Committee Chair

John R. Mecikalski

Committee Member

Arastoo Pour-Biazar

Committee Member

Lawrence D. Carey

Committee Member

Udaysankar S. Nair

Committee Member

Richard T. McNider

Committee Member

Jonathan E. Pleim


Air quality management, Air quality--Remote sensing


The air quality community uses numerical models for air quality forecasting and establishing regulatory policies. However, the prediction of clouds and the estimation of emissions remain highly uncertain, limiting the credibility and usefulness of model results. This study explored how Geostationary Operational Environmental Satellite (GOES) observations can be used to reduce the uncertainty in air quality simulations. The major findings are summarized as follows. First, assimilating the GOES-13 cloud products into a numerical weather prediction model improved the agreement between model-predicted and satellite-observed clouds by 3.5%. Model surface insolation bias and error were reduced by 7.1 W m–2 (13.1%) and 7.0 W m–2 (3.8%), respectively. Improvements in the meteorological fields subsequently affected biogenic emissions and photochemical reaction rates, leading to a 1.0 ppb (29%) reduction in the positive ground-level ozone bias. Second, the new GOES-16 surface insolation product was compared to ground-based pyranometer observations and showed a negative bias of –12.6 W m–2 (or −2.8% of the mean observed value) in the summer of 2019. Several factors may have contributed to the uncertainty in the current retrieval system, including using original narrow-to-broadband conversion parameters, misclassification of sky conditions, the effect of topography on surface insolation, and large satellite viewing angles for the western U.S. Overall, error statistics are comparable to the previous GOES-13 retrieval, suggesting that similar improvements to air quality simulations may be achieved by assimilating the GOES-16 products. Third, Geostationary Lightning Mapper (GLM) observations were used to estimate lightning-induced nitrogen oxides (LNOx) emissions. Assuming each lightning flash would produce 250 moles of NOx, approximately 0.174 Tg N (11.4% of total NOx emissions) was emitted due to lightning activity over the continental U.S. from June through September 2019. In air quality model simulations, the GLM-derived LNOx emission estimates increased tropospheric ozone by 1.37% (0.50 Dobson Units). The estimates are comparable to previous studies but fall at the lower end of the uncertainty range, indicating that the average 250 moles per flash production rate used in this study needs to be revised.



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