Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation
Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inv...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/1/220 |
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| author | Yiwen Hu Zengliang Zang Dan Chen Xiaoyan Ma Yanfei Liang Wei You Xiaobin Pan Liqiong Wang Daichun Wang Zhendong Zhang |
| author_facet | Yiwen Hu Zengliang Zang Dan Chen Xiaoyan Ma Yanfei Liang Wei You Xiaobin Pan Liqiong Wang Daichun Wang Zhendong Zhang |
| author_sort | Yiwen Hu |
| collection | DOAJ |
| description | Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO<sub>2</sub>) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO<sub>2</sub> concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO<sub>2</sub> emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO<sub>2</sub> forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO<sub>2</sub> emission optimization methodology is computationally cost-effective. |
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| id | doaj.art-94b9d0a551ce435ca8e377969b671932 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:23:16Z |
| publishDate | 2022-01-01 |
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| spelling | doaj.art-94b9d0a551ce435ca8e377969b6719322023-11-23T12:15:02ZengMDPI AGRemote Sensing2072-42922022-01-0114122010.3390/rs14010220Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data AssimilationYiwen Hu0Zengliang Zang1Dan Chen2Xiaoyan Ma3Yanfei Liang4Wei You5Xiaobin Pan6Liqiong Wang7Daichun Wang8Zhendong Zhang9Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaNo. 32145 Unit of PLA, Xinxiang 453000, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaEmission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO<sub>2</sub>) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO<sub>2</sub> concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO<sub>2</sub> emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO<sub>2</sub> forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO<sub>2</sub> emission optimization methodology is computationally cost-effective.https://www.mdpi.com/2072-4292/14/1/2203DVARdata assimilationsulfur dioxideemission inventoryWRF-Chem |
| spellingShingle | Yiwen Hu Zengliang Zang Dan Chen Xiaoyan Ma Yanfei Liang Wei You Xiaobin Pan Liqiong Wang Daichun Wang Zhendong Zhang Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation Remote Sensing 3DVAR data assimilation sulfur dioxide emission inventory WRF-Chem |
| title | Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation |
| title_full | Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation |
| title_fullStr | Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation |
| title_full_unstemmed | Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation |
| title_short | Optimization and Evaluation of SO<sub>2</sub> Emissions Based on WRF-Chem and 3DVAR Data Assimilation |
| title_sort | optimization and evaluation of so sub 2 sub emissions based on wrf chem and 3dvar data assimilation |
| topic | 3DVAR data assimilation sulfur dioxide emission inventory WRF-Chem |
| url | https://www.mdpi.com/2072-4292/14/1/220 |
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