Development and Evaluation of a Long‐Term Data Record of Planetary Boundary Layer Profiles From Aircraft Meteorological Reports

2019 ◽  
Vol 124 (4) ◽  
pp. 2008-2030 ◽  
Author(s):  
Yuanjie Zhang ◽  
Dan Li ◽  
Zekun Lin ◽  
Joseph A. Santanello ◽  
Zhiqiu Gao
2018 ◽  
Vol 10 (6) ◽  
pp. 940 ◽  
Author(s):  
José García-Lázaro ◽  
José Moreno-Ruiz ◽  
David Riaño ◽  
Manuel Arbelo

2019 ◽  
Vol 5 (4) ◽  
pp. 261-271 ◽  
Author(s):  
Yucong Miao ◽  
Jing Li ◽  
Shiguang Miao ◽  
Huizheng Che ◽  
Yaqiang Wang ◽  
...  

Abstract Purpose of Review During the past decades, the number and size of megacities have been growing dramatically in China. Most of Chinese megacities are suffering from heavy PM2.5 pollution. In the pollution formation, the planetary boundary layer (PBL) plays an important role. This review is aimed at presenting the current state of understanding of the PBL-PM2.5 interaction in megacities, as well as to identify the main gaps in current knowledge and further research needs. Recent Findings The PBL is critical to the formation of urban PM2.5 pollution at multiple temporal scales, ranging from diurnal change to seasonal variation. For the essential PBL structure/process in pollution, the coastal megacities have different concerns from the mountainous or land-locked megacities. In the coastal cities, the recirculation induced by sea-land breeze can accumulate pollutants, whereas in the valley/basin, the blocking effects of terrains can lead to stagnant conditions and thermal inversion. Within a megacity, although the urbanization-induced land use change can cause thermodynamic perturbations and facilitate the development of PBL, the increases in emissions outweigh this impact, resulting in a net increase of aerosol concentration. Moreover, the aerosol radiative effects can modify the PBL by heating the upper layers and reducing the surface heat flux, suppressing the PBL and exacerbating the pollution. Summary This review presented the PBL-PM2.5 interaction in 13 Chinese megacities with various geographic conditions and elucidated the critical influencing processes. To further understand the complicated interactions, long-term observations of meteorology and aerosol properties with multi-layers in the PBL need to be implemented.


2021 ◽  
pp. 1-64
Author(s):  
Man Yue ◽  
Minghuai Wang ◽  
Jianping Guo ◽  
Haipeng Zhang ◽  
Xinyi Dong ◽  
...  

AbstractThe planetary boundary layer (PBL) plays an essential role in climate and air quality simulations. Nevertheless, large uncertainties remain in understanding the drivers for long-term trend of PBL height (PBLH) and its simulation. Here we combinate the radiosonde data and reanalysis datasets to analyze PBLH long-term trends over China, and to further explore the performance of CMIP6 climate models in simulating these trends. Results show that the observed long-term “positive to negative” trend shift of PBLH is related to the variation in the surface upward sensible heat flux (SHFLX), and the SHFLX is further controlled by the synergistic effect of low cloud cover (LCC) and soil moisture (SM) changes. Variabilities in LCC and SM directly influence the energy balance via surface net downward shortwave flux (SWF) and the latent heat flux (LHFLX), respectively. The CMIP6 climate models, however, cannot reproduce the observed PBLH long-term trend shift over China. The CMIP6 results illustrate an overwhelming continuous downward PBLH trend during the 1979–2014 period, which is largely caused by the poor capability in simulating long-term variations of cloud radiative effect. Our results reveal that the long-term cloud radiative effect simulation is critical for CMIP6 models in reproducing the long-term trend of PBLH. This study highlights the importance of processes associated with LCC and SM in modulating PBLH long-term variations and calls attentions to improve these processes in climate models in order to improve the PBLH long-term trend simulations.


2019 ◽  
Vol 10 (3) ◽  
pp. 989-996 ◽  
Author(s):  
Liang Pan ◽  
Jianming Xu ◽  
Xuexi Tie ◽  
Xiaoqing Mao ◽  
Wei Gao ◽  
...  

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