Reply on RC2

Abstract

Top-down atmospheric inversion infers surface-atmosphere fluxes from spatially distributed observations of atmospheric compositions, which is a vital means for quantifying large-scale anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting/Community Multiscale Air Quality Modeling System (WRF/CMAQ) model, the three-dimensional variational (3DVAR) algorithm and the ensemble square root filter (EnSRF) algorithm. It is capable to simultaneously assimilate spatially distributed hourly in-situ measurements of CO, SO2, NO2, PM2.5 and PM10 concentrations to quantitatively optimize gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5) and coarse PM10 (PMC) on regional scale. RAPAS includes two subsystems, initial field assimilation (IA) subsystem and emission inversion (EI) subsystem, which are used to generate a "perfect" chemical initial condition (IC), and conduct inversions of anthropogenic emissions, respectively. A "two-step" inversion scheme is adopted in the EI subsystem in its each data assimilation (DA) window, in which the emission is inferred in the first step, and then, it is input into the CMAQ model to simulate the initial field of the next window, meanwhile, it is also transferred to the next window as the prior emission. The chemical IC is optimized through the IA subsystem, and the original emission inventory is only used in the first DA window. Besides, a "super-observation" approach is implemented based on optimal estimation theory to decrease the computational costs and observation error correlations and reduce the influence of representativeness errors. With this system, we estimated the emissions of CO, SO2, NOx, PPM2.5 and PMC in December 2016 over China using the corresponding nationwide surface observations. The 2016 Multi-resolution Emission Inventory for China (MEIC 2016) was used as the prior emission. The system was run from 26 November to 31 December, in which the IA subsystem was run in the first 5 days, and the EI subsystem was run in the following days. The optimized ICs at the first 5 days and the posterior emissions in December were evaluated against the assimilated and independent observations. Results showed that the root mean squared error (RMSE) decreased by 50.0–73.2%, and the correlation coefficient (CORR) increased to 0.78–0.92 for the five species compared to the simulations without 3DVAR. Additionally, the RMSE decreased by 40.1–56.3 %, and the CORR increased to 0.69–0.87 compared to the simulations without optimized emissions. For the whole mainland China, the uncertainties were reduced by 44.4 %, 45.0 %, 34.3 %, 51.8 % and 56.1 % for CO, SO2, NOx, PPM2.5 and PMC, respectively. Overall, compared to the prior emission (MEIC 2016), the posterior emissions increased by 129 %, 20 %, 5 %, and 95 % for CO, SO2, NOx and PPM2.5, respectively, indicating that there was significant underestimation in the MEIC inventory. The posterior PMC emissions, including anthropogenic and natural dust contributions, increased by 1045 %. A series of sensitivity tests were conducted with different inversion processes, prior emissions, prior uncertainties, and observation errors. Results showed that the "two-step" scheme clearly outperformed the simultaneous assimilation of ICs and emissions ("one-step" scheme), and the system is rather robust in estimating the emissions using the nationwide surface observations over China. Our study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and near-real time.