Dependence of Aerosol Extinction Measurements Using a Camera Based Lidar on Various Aerosol Phase Functions

2021 ◽  
Author(s):  
Amin Kabir ◽  
Nimmi Sharma ◽  
John E Barnes ◽  
Alicja Urbanczyk ◽  
Justin Fagnoni ◽  
...  
Keyword(s):  
Aerosol Extinction ◽  
Phase Functions

scholarly journals Scattered moonlight observations with X-shooter

2019 ◽  
Vol 624 ◽  
pp. A39
Author(s):  
A. Jones ◽  
S. Noll ◽  
W. Kausch ◽  
S. Unterguggenberger ◽  
C. Szyszka ◽  
...  
Keyword(s):  
Near Infrared ◽  
Current Model ◽  
Background Model ◽  
Lunar Phase ◽  
Aerosol Extinction ◽  
Coarse Particles ◽  
The Moon ◽  
Aerosol Scattering ◽  
Phase Functions ◽  
Background Models

Estimating the sky background is critical for ground-based astronomical research. In the optical, scattered moonlight dominates the sky background, when the moon is above the horizon. The most uncertain component of a scattered moonlight model is the aerosol scattering. The current, official sky background model for Cerro Paranal uses an extrapolated aerosol extinction curve. With a set of X-shooter sky observations, we have tested the current sky background model as well as determined the aerosol extinction from the ultra-violet (UV) to near-infrared (NIR). To our knowledge, this is the first time that a scattered moonlight model has been used for this purpose. These observations were taken of blank sky, during three different lunar phases, and at six different angular distances from the moon for each lunar phase. Overall, the current model does reproduce the observations for average conditions quite well. Using a set of sky background models with varying aerosol distributions to compare with the observations, we found the most likely aerosol extinction curves, phase functions, and volume densities for the three nights of observations and compare them with the current model. While there are some degeneracies in the aerosol scattering properties, the extinction curves tend to flatten towards redder wavelengths and are overall less steep compared to the extrapolated curve used in the current model. Also, the current model had significantly less coarse particles compared to the favored volume densities from the X-shooter data. Having more coarse particles affects the phase function by being more peaked at small angular distances. For the three nights of sky observations, the aerosol size distributions differed, most likely reflecting the changes in atmospheric conditions and aerosol content, which is expected. In short, the current sky background model is in fair agreement with the observations, and we have determined better aerosol extinction curves and phase functions for Cerro Paranal. Using nighttime sky observations of scattered moonlight and a set of sky background models is a new method to probe the aerosol content of the atmosphere.

SAGE I and SAM II Measurements of 1 μm Aerosol Extinction in the Free Troposphere

1988 ◽  
Vol 27 (3) ◽  
pp. 269-279 ◽  
Author(s):  
G. S. Kent ◽  
U. O. Farrukh ◽  
P. H. Wang ◽  
A. Deepak
Keyword(s):  
Aerosol Extinction ◽  
Free Troposphere

scholarly journals MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

2015 ◽  
Vol 8 (2) ◽  
pp. 941-963 ◽  
Author(s):  
T. Vlemmix ◽  
F. Hendrick ◽  
G. Pinardi ◽  
I. De Smedt ◽  
C. Fayt ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Systematic Bias ◽  
Aerosol Extinction ◽  
Data Set ◽  
Near Surface ◽  
Beijing Area ◽  
Least Squares Fit ◽  
Relative Differences ◽  
Good Agreement

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

scholarly journals In situ measurement of the aerosol extinction-to-backscatter ratio at a polluted continental site

2000 ◽  
Vol 105 (D22) ◽  
pp. 26907-26915 ◽  
Author(s):  
Theodore L. Anderson ◽  
Sarah J. Masonis ◽  
David S. Covert ◽  
Robert J. Charlson ◽  
Mark J. Rood
Keyword(s):  
In Situ Measurement ◽  
Aerosol Extinction

Optimal Signal Extraction with Correlated Components

2014 ◽  
Vol 6 (2) ◽  
pp. 237-273 ◽  
Author(s):  
Tucker S. McElroy ◽  
Agustin Maravall
Keyword(s):  
Signal Extraction ◽  
Economic Time Series ◽  
Systematic Treatment ◽  
Finite Samples ◽  
Economic Time ◽  
The Mean ◽  
Phase Functions ◽  
Component Models ◽  
Correlated Components ◽  
Optimal Signal

AbstractWhile it is typical in the econometric signal extraction literature to assume that the unobserved signal and noise components are uncorrelated, there is nevertheless an interest among econometricians in the hypothesis of hysteresis, i.e. that major movements in the economy are fundamentally linked. While specific models involving correlated signal and noise innovation sequences have been developed and applied using state space methods, there is no systematic treatment of optimal signal extraction with correlated components. This paper provides the mean square error optimal formulas for both finite samples and bi-infinite samples and furthermore relates these filters to the more well-known Wiener–Kolmogorov (WK) and Beveridge–Nelson (BN) signal extraction formulas in the case of ARIMA component models. Then we obtain the result that the optimal filter for correlated components can be viewed as a weighted linear combination of the WK and BN filters. The gain and phase functions of the resulting filters are plotted for some standard cases. Some discussion of estimation of hysteretic models is presented, along with empirical results on an economic time series. Comparisons are made between signal extractions from traditional WK filters and those arising from the hysteretic models.

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