State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
Environ Pollut. 2022 Mar 15;297:118783. doi: 10.1016/j.envpol.2021.118783. Epub 2021 Dec 30.
The Coronavirus Disease 2019 (COVID-19) outbreak caused a suspension of almost all non-essential human activities, leading to a significant reduction of anthropogenic emissions. However, the emission inventory of the chemistry transport model cannot be updated in time, resulting in large uncertainty in PM predictions. This study adopted a three-dimensional variational approach to assimilate multi-source PM data from satellite and ground observations and jointly adjusted emissions to improve PM predictions of the WRF-Chem model. Experiments were conducted to verify the method over Hubei Province, China, during the COVID-19 epidemic from Jan 21st to Mar 20th, 2020. The results showed that PM predictions were improved at almost all the validation sites, and the benefit of data assimilation (DA) can last for 48 h. However, the benefits of DA diminished quickly with the increase of the forecast time. By adjusting emissions, the PM predictions showed a much slower error accumulation along forecast time. At 48Z, the RMSE still has an 8.85 μg/m (19.49%) improvement, suggesting the effectiveness of emissions adjustment based on the improved initial conditions via DA.
2019 年冠状病毒病（COVID-19）疫情导致几乎所有非必要的人类活动暂停，人为排放显著减少。然而，化学输送模式的排放清单无法及时更新，导致 PM 预测存在很大不确定性。本研究采用三维变分方法同化卫星和地面观测的多源 PM 数据，并联合调整排放以改进 WRF-Chem 模型的 PM 预测。实验在中国湖北省进行，验证了 2020 年 1 月 21 日至 3 月 20 日 COVID-19 疫情期间的方法。结果表明，PM 预测在几乎所有验证点都得到了改善，数据同化（DA）的益处可以持续 48 小时。然而，随着预测时间的增加，DA 的益处迅速减少。通过调整排放，PM 预测在预测时间内的误差积累速度明显较慢。在 48Z 时，RMSE 仍有 8.85μg/m（19.49%）的改善，表明通过 DA 基于改进的初始条件调整排放的有效性。
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