Spatial distribution of atmospheric aerosol optical depth over Atlantic Ocean along the route of Russian Antarctic expeditions

2015 ◽  
Author(s):  
Dmitry M. Kabanov ◽  
Vladimir F. Radionov ◽  
Sergey M. Sakerin ◽  
Alexander Smirnov
Keyword(s):  
Spatial Distribution ◽  
Atlantic Ocean ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Aerosol

Specific features of spatial distribution of atmospheric aerosol optical depth along northern sea route

2014 ◽  
Author(s):  
Sergey M. Sakerin ◽  
Dmitry M. Kabanov ◽  
Vasily V. Polkin ◽  
Aleksander N. Prakhov ◽  
Konstantin A. Shmirko
Keyword(s):  
Spatial Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Aerosol ◽  
Northern Sea Route

Characteristics of atmospheric aerosol optical depth variation over China in recent 30 years

2000 ◽  
Vol 45 (14) ◽  
pp. 1328-1334 ◽  
Author(s):  
Yunfeng Luo ◽  
Daren Lü ◽  
Qing He ◽  
Weiliang Li ◽  
Xiuji Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Aerosol ◽  
Depth Variation

scholarly journals Effective aerosol optical depth from pyranometer measurements of surface solar radiation (global radiation) at Thessaloniki, Greece

2012 ◽  
Vol 12 (12) ◽  
pp. 33265-33289
Author(s):  
A. V. Lindfors ◽  
N. Kouremeti ◽  
A. Arola ◽  
S. Kazadzis ◽  
A. F. Bais ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Aerosol ◽  
Global Radiation ◽  
Single Scattering ◽  
Surface Radiation ◽  
Surface Solar Radiation ◽  
Long Time ◽  
Source Of Information

Abstract. Pyranometer measurements of the solar surface radiation (SSR) are available at many locations worldwide, often as long time series covering several decades into the past. These data constitute a potential source of information on the atmospheric aerosol load. Here, we present a method for estimating the aerosol optical depth (AOD) using pyranometer measurements of the SSR together with total water vapor column information. The method, which is based on radiative transfer simulations, was developed and tested using recent data from Thessaloniki, Greece. The effective AOD calculated using this method was found to agree well with co-located AERONET measurements, exhibiting a correlation coefficient of 0.9 with 2/3 of the data found within ±20% or ±0.05 of the AERONET AOD. This is similar to the performance of current satellite aerosol methods. Differences in the AOD as compared to AERONET can be explained by variations in the aerosol properties of the atmosphere that are not accounted for in the idealized settings used in the radiative transfer simulations, such as variations in the single scattering albedo and Ångström exponent. Furthermore, the method is sensitive to calibration offsets between the radiative transfer simulations and the pyranometer SSR. The method provides an opportunity of extending our knowledge of the atmospheric aerosol load to locations and times not covered by dedicated aerosol measurements.

scholarly journals Eurasia large-scale hazes in summer 2016

2019 ◽  
Vol 55 (3) ◽  
pp. 41-51
Author(s):  
G. I. Gorchakov ◽  
S. A. Sitnov ◽  
A. V. Karpov ◽  
I. A. Gorchakova ◽  
R. A. Gushchin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Large Scale ◽  
Empirical Distribution ◽  
Growth Period ◽  
Maximum Growth ◽  
Qualitative Change ◽  
Smoke Aerosol

Using maximum aerosol optical depth (MAOD) spatial distribution formation technique the optically dense haze expansion scales in period from 15 to 31 July 2016 over Eurasia are estimated in during great Siberian smoke haze (SSH) with the area 16 mln km2 about, smog over the Northern China Plain (2 mln km2), dust haze in Takla Makan desert (0.8 mln km2) and hazes in India and Pakistan (1 mln km2 approximately). Empirical distribution function (EDF) MAOD is received which is approximated by linear function of MAOD logarithm. Aerosol optical depth (AOD) spatial distribution at wavelength 550 nm in SSH is analyzed. Total smoke aerosol mass assessment in SSH (3.2 mln tons) is evaluated. Smoke aerosol (SA) mass during maximum growth period from 22 July to 26 July 2016 over Siberia (50°-70°, 60°-120 °E) was equal 2 mln tons approximately. Aerosol index (AI) temporal variability is illustrated visually SA composition qualitative change in SSH during long-range transport. It is shown that AI variations are correlated with AOD variations. Aerosol radiative forcing (ARF) at the top and the bottom of the atmosphere over Siberia from 22 July to 26 July 2016 is estimated (average ARF are equal –68 and –98 W/m2). EDF AOD and EDF ARF at the top of the atmosphere are approximated by exponential and power function of AOD correspondingly.

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