Spatial and temporal variation of Visible Infrared Imaging Radiometer Suite (VIIRS)-derived aerosol optical thickness over Shandong, China

2014 ◽  
Vol 35 (16) ◽  
pp. 6023-6034 ◽  
Author(s):  
F. Meng ◽  
C.Y. Cao ◽  
X. Shao ◽  
Y.G. Shi
Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 242
Author(s):  
Muhammad Umar ◽  
Salman Atif ◽  
Mark L. Hildebrandt ◽  
Ali Tahir ◽  
Muhammad Azmat ◽  
...  

Aerosol Optical Thickness (AOT) is one of the important parameters for assessing regional and global level of climate change. Fog episodes have considerably increased in south Asia because of environmental factors, and the burning of agricultural residue leads to major social and economic problems. In present study, Mann-Kendall trend analysis of AOT and active fire events was done, and their significance were assessed using long-term (October 2012–February 2020) remote sensing data derived smog maps. Visible Infrared Imaging Radiometer Suite National Polar Partnership (VIIRS N-PP) was used to map AOT episodes over the northern region of Pakistan and India. Results reveal that AOT displays a significantly decreasing trend over the northern and eastern region of Pakistan and a similar decreasing trend from the Western to Eastern region of India. Furthermore, active fire events have a significantly increasing trend at the Northern region of Pakistan. However, fire events have a significantly decreasing trend over the southern and southeastern region of India. Additionally, statistically significant decreasing trends were observed for AOT over Chakwal (p-value = 0.2, Z_MK = −2.3) and Patiala (p-value = 0.15, Z_MK = −3.2). Fire events have a significantly increasing trend for Dera Ismail Khan (p-value = 0.01, Z_MK = 1.9), Jhang (p-value = 0.01, Z_MK = 1.9), and Chakwal (p-value = 0.01, Z_MK = 1.8), while they are significantly decreasing trend near New Delhi (p-value = 0.2, Z_MK = −0.9), Aligarh (p-value = 0.15, Z_MK = −0.9) and Patiala (p-value = 0.2, Z_MK = −0.8).


Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1798
Author(s):  
Xu Wu ◽  
Su Li ◽  
Bin Liu ◽  
Dan Xu

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.


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