Urbanization has become a prominent anthropogenic activity globally, resulting in the substantial modification of temperature and rainfall in and around urban areas. Studies also indicate that rainfall exhibits an asymmetrical shift from light to extreme rainfall, causing both floods and droughts over different parts of the globe. However, to what extent urbanization influences the asymmetrical changes in rainfall and urban drought remains understudied. Accordingly, we provided an investigation of the urbanization effects on both rainfall and drought events from both statistical and model‐based perspectives. Results showed that urbanization generally increased heavy rainfall and decreased light rainfall in the rainy season over five urban agglomerations (i.e., Yangtze River Delta [YRD], Middle Region of Yangtze River [MRYR], Chengdu‐Chongqing, Guizhou, and Yunnan) in the Yangtze River Basin during 1981–2020. Specifically, urbanization contributed 42.7% and 30.8% of the increases of heavy rainfall in MRYR and YRD while 30.6% contribution to the decrease of light rainfall. Interestingly, results suggested that the urban regions were more prone to drought due to urbanization. Nevertheless, we also found that the urbanization effects showed some variabilities across different areas due to the interactions with complex terrains and climate patterns. Further model‐based simulations using the Weather Research and Forecasting model confirmed our findings and helped understand the potential underlying mechanism. The current research is expected to provide scientific knowledge for better urban planning and preparedness for urbanization‐induced hazards.
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
2.5
predictions. This study adopted a three-dimensional variational approach to assimilate multi-source PM
2.5
data from satellite and ground observations and jointly adjusted emissions to improve PM
2.5
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
2.5
predictions were improved at almost all the validation sites, and the benefit of data assimilation (DA) can last for 48 hours. However, the benefits of DA diminished quickly with the increase of the forecast time. By adjusting emissions, the PM
2.5
predictions showed a much slower error accumulation along forecast time. At 48Z, the RMSE still has an 8.85 μg/m
3
(19.49%) improvement, suggesting the effectiveness of emissions adjustment based on the improved initial conditions via DA.
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