Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation
Abstract
:1. Introduction
2. Data and Methodology
2.1. Observational Data and a Priori Emission Data
2.2. WRF-Chem Forecast Model and the 3DVAR DA System
2.3. The Methodology Used for Optimizing the SO2 Emission Inventory
2.3.1. The Assumptions and Procedure to Optimize the Emissions
2.3.2. Conversion from Forecast Error to Emission Error
2.3.3. The Operational Consideration for Emission Optimization
2.4. Observing Systems Simulation Experiment
2.5. Experimental Design
3. Results
3.1. Increments of SO2 Concentration and Optimized Emissions
3.2. Forecast Performance
3.2.1. Improvement in Hourly SO2 Simulations
3.2.2. Spatial Distribution of SO2 Concentrations
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Physical or Chemical Process | Option |
---|---|
Microphysics | Lin microphysics scheme [54] |
Long-wave radiation | Rapid Radiative Transfer Model [55] |
Shortwave radiation | Goddard Space Flight Center shortwave radiation scheme [56] |
Boundary layer scheme | Yonsei University [57] |
Land surface model | Noah land surface model [58] |
Cumulus parameterization | Grell 3-D scheme [59] |
Aerosol scheme | Model for Simulating Aerosol Interactions and Chemistry (MOSAIC-4 bin) [60] |
Gas scheme | Carbon Bond Mechanism-Z [61] |
Initial condition for chemical species | 10 d spin-up |
Experiment Name | Emissions | Data for DA | Cycle DA |
---|---|---|---|
Control (Ctrl) | MEIC_2010 | / | / |
Update Cycle DA (UC_DA) | MEIC_2010 | Surface observations of SO2 concentration | hourly |
New Emission (N_EM) | 2015 optimized emissions | / | / |
N Data | Mean Concentration | Bias | RMSE | CORR | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Obs | Ctrl | UC_DA | N_EM | Ctrl | UC_DA | N_EM | Ctrl | UC_DA | N_EM | Ctrl | UC_DA | N_EM | ||
China | 241,023 | 23.41 | 28.95 | 22.96 | 23.16 | 5.54 | −0.45 | −0.25 | 33.81 | 19.56 | 25.04 | 0.18 | 0.51 | 0.27 |
NCP | 36,866 | 33.81 | 38.42 | 32.19 | 36.78 | 4.61 | −1.62 | 2.97 | 39.10 | 23.91 | 32.96 | 0.24 | 0.70 | 0.40 |
NEC | 29,939 | 24.77 | 18.12 | 24.24 | 19.91 | −6.65 | −0.53 | −4.86 | 28.67 | 22.11 | 25.51 | 0.17 | 0.55 | 0.31 |
EGT | 43,683 | 30.84 | 30.09 | 25.83 | 27.34 | −0.74 | −5.01 | −3.49 | 36.42 | 24.42 | 30.64 | 0.14 | 0.56 | 0.28 |
XJ | 16,819 | 11.59 | 6.99 | 13.93 | 8.21 | −4.60 | 2.34 | −3.37 | 17.09 | 13.25 | 14.87 | 0.16 | 0.37 | 0.21 |
SB | 58,012 | 21.79 | 38.21 | 21.56 | 23.64 | 16.42 | −0.23 | 1.85 | 41.80 | 18.49 | 25.10 | 0.16 | 0.42 | 0.22 |
YRD | 42,964 | 25.03 | 35.60 | 23.82 | 25.64 | 10.56 | −1.21 | 0.61 | 35.22 | 19.24 | 25.01 | 0.25 | 0.52 | 0.31 |
PRD | 12,740 | 16.05 | 35.23 | 19.14 | 20.56 | 19.17 | 3.08 | 4.51 | 38.39 | 15.52 | 21.23 | 0.13 | 0.43 | 0.20 |
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Hu, Y.; Zang, Z.; Chen, D.; Ma, X.; Liang, Y.; You, W.; Pan, X.; Wang, L.; Wang, D.; Zhang, Z. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation. Remote Sens. 2022, 14, 220. https://doi.org/10.3390/rs14010220
Hu Y, Zang Z, Chen D, Ma X, Liang Y, You W, Pan X, Wang L, Wang D, Zhang Z. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation. Remote Sensing. 2022; 14(1):220. https://doi.org/10.3390/rs14010220
Chicago/Turabian StyleHu, Yiwen, Zengliang Zang, Dan Chen, Xiaoyan Ma, Yanfei Liang, Wei You, Xiaobin Pan, Liqiong Wang, Daichun Wang, and Zhendong Zhang. 2022. "Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation" Remote Sensing 14, no. 1: 220. https://doi.org/10.3390/rs14010220