scholarly journals Remote Sensing of Seasonal Distribution of Precipitable Water Vapor over the Oceans and the Inference of Boundary-Layer Structure

1979 ◽  
Vol 107 (10) ◽  
pp. 1388-1401 ◽  
Author(s):  
C. Prabhakara ◽  
G. Dalu ◽  
R. C. Lo ◽  
N. R. Nath
2020 ◽  
Vol 12 (21) ◽  
pp. 3469
Author(s):  
Bilawal Abbasi ◽  
Zhihao Qin ◽  
Wenhui Du ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
...  

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.


2020 ◽  
Author(s):  
Jun Zou ◽  
Jianning Sun ◽  
Zixuan Xiang ◽  
Xiaomen Han ◽  
Qiuji Ding

<p>At the end of November 2018, a heavy air pollution event was recorded by many meteorological stations in the Yangtze River Delta (YRD), China. The local PM2.5 concentration exceeding to 200 µg m<sup>-3</sup>. This is the heaviest, longest and most widespread heavy-polluted weather in Jiangsu Province since 2018. Meanwhile, there has been severe foggy weather in Jiangsu Province, with visibility less than 200 meters in most parts of the province. In order to study the interaction between PM2.5 concentration and boundary layer height in the haze event, and the effect of fog on pollutant aggregation, the boundary layer structure of the continuous haze process was analyzed by using the SORPES Observation of Nanjing University's Xianlin Campus. The results of the analysis show that:<br>1, The PM2.5 concentration in the boundary layer is inversely correlated with the boundary layer height, the higher the PM2.5 concentration, the lower the boundary layer height during the day. By absorbing and scattering solar radiation, atmospheric aerosols affect the balance of surface energy and reduce the sensitive heat flux, thereby inhibiting the development of the boundary layer. While inhibited development of the boundary layer will limit the diffusion of atmospheric aerosols, thereby increasing the concentration of atmospheric aerosols in the boundary layer. In addition, nocturnal atmospheric aerosols absorb heat, leading to strong grounding inversion temperature the next day, further inhibiting the development of the daytime boundary layer. <br>2, The fog-top inversion is very strong, far stronger than the inversion caused by atmospheric aerosols. Therefore, the heights of the boundary layer of fog days are much lower than that of non-fog days under the same pollution conditions.<br>3, During the fog, the PM2.5 concentration significantly reduced. And after the fog dissipated, due to the sun, the air moisture evaporation, PM2.5 concentration quickly reverted to the pre-fog state. Fog has limited wet removal of PM2.5.<br>4, Fog can inhibit the development of the boundary layer, with the continuation of the fog process, the pollution in the boundary layer continues to increase. At the same time, due to the inhibition of the development of the boundary layer, the diffusion of water vapor in the air is also affected, resulting in the boundary layer water vapor content is always in a high state, thus promoting the production of fog.</p>


2006 ◽  
Author(s):  
Mikhail D. Alexandrov ◽  
Brian Cairns ◽  
Andrew A. Lacis ◽  
Barbara E. Carlson

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