Potential of lidar backscatter data to estimate solar aerosol radiative forcing

Manfred Wendisch; Detlef Müller; Ina Mattis; Albert Ansmann

doi:10.1364/AO.45.000770

Applied Optics
Vol. 45,
Issue 4,
pp. 770-783
(2006)
•https://doi.org/10.1364/AO.45.000770

Potential of lidar backscatter data to estimate solar aerosol radiative forcing

Manfred Wendisch, Detlef Müller, Ina Mattis, and Albert Ansmann

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Manfred Wendisch, Detlef Müller, Ina Mattis, and Albert Ansmann, "Potential of lidar backscatter data to estimate solar aerosol radiative forcing," Appl. Opt. 45, 770-783 (2006)

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Abstract

The potential to estimate solar aerosol radiative forcing (SARF) in cloudless conditions from backscatter data measured by widespread standard lidar has been investigated. For this purpose 132 days of sophisticated ground-based Raman lidar observations (profiles of particle extinction and backscatter coefficients at $532 nm$ wavelength) collected during two campaigns [the European Aerosol Research Lidar Network (EARLINET) and the Indian Ocean Experiment (INDOEX)] were analyzed. Particle extinction profiles were used as input for radiative transfer simulations with which to calculate the SARF, which then was plotted as a function of the column (i.e., height-integrated) particle backscatter coefficient $(β_{c})$ . A close correlation between the SARF and $β_{c}$ was found. SARF– $β_{c}$ parameterizations in the form of polynomial fits were derived that exhibit an estimated uncertainty of $\pm (10 - 30) %$ . These parameterizations can be utilized to analyze data of upcoming lidar satellite missions and for other purposes. The EARLINET-based parameterizations can be applied to lidar measurements at mostly continental, highly industrialized sites with limited maritime influence (Europe, North America), whereas the INDOEX parameterizations rather can be employed in polluted maritime locations, e.g., coastal regions of south and east Asia.

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i	$a_{i, τ}$
i	EARLINET	INDOEX
0	0.0202674	−0.0980517
1	42.0411	75.3985
2	1264.39	−2880.41
$ϵ_{τ}$	0.073	0.078

EARLINET			INDOEX
θ_s (°)	ϵ_ΔA	ϵ_ΔF _(TOA) (W m⁻²)	ϵ_ΔF _(BOA) (W m⁻²)	ϵ_ΔA	ϵ_ΔF _(TOA) (W m⁻²)	ϵ_ΔF _(BOA) (W m⁻²)
10	0.0029	3.9	9.1	0.0021	2.8	8.0
30	0.0038	4.5	9.5	0.0026	3.1	8.1
50	0.0064	5.6	10.1	0.0041	3.6	8.1
65	0.0106	6.1	9.7	0.0065	3.8	7.3
70	0.0127	5.9	8.9	0.0078	3.6	6.6
75	0.0152	5.4	7.6	0.0094	3.3	5.6
80	0.0178	4.2	5.5	0.0112	2.6	4.0
85	0.0190	2.2	2.5	0.0121	1.4	1.8

$θ_{s}$	EARLINET
	$δ_{Δ A}$		$δ_{Δ F (TOA)} (W m^{- 2})$		$δ_{Δ F (BOA)} (W m^{- 2})$
	$Δ g = \pm 0.05$	$Δ w = \pm 0.05$	$Δ g = \pm 0.05$	$Δ w = \pm 0.05$	$Δ g = \pm 0.05$	$Δ w = \pm 0.05$
30°	±0.002	±0.004	±2.7	±4.7	±3.0	±8.6
	(±19)	(±32)	(±19)	(±32)	(±10)	(±29)
70°	±0.004	±0.008	±1.8	±3.8	±1.7	±4.9
	(±9)	(±17)	(±9)	(±17)	(±6)	(±15)

Parameterizations for the EARLINET data set
$Δ A$	$b_{0, Δ A}$	$b_{1, Δ A}$	$b_{2, Δ A}$	$b_{3, Δ A}$	$b_{4, Δ A}$	$b_{5, Δ A}$
$a_{0, Δ A}$	−0.0242266	0.00451036	−0.000262497	$6.70485 \times 10^{- 6}$	$- 7.83892 \times 10^{- 8}$	$3.45850 \times 10^{- 10}$
$a_{1, Δ A}$	16.1340	−2.64061	0.152705	−0.00380248	$4.32416 \times 10^{- 5}$	$- 1.79799 \times 10^{- 7}$
$a_{2, Δ A}$	−503.822	101.171	−5.73935	0.140258	−0.00153767	$6.01697 \times 10^{- 6}$
$Δ F (TOA)$	$b_{0, Δ F (TOA)}$	$b_{1, Δ F (TOA)}$	$b_{2, Δ F (TOA)}$	$b_{3, Δ F (TOA)}$	$b_{4, Δ F (TOA)}$	$b_{5, Δ F (TOA)}$
$a_{0, Δ F (TOA)}$	−3.92047	0.546980	−0.0319254	0.000802533	$- 9.21467 \times 10^{- 6}$	$3.90602 \times 10^{- 8}$
$a_{1, Δ F (TOA)}$	−3891.40	346.964	−21.8857	0.577431	−0.00739729	$3.64806 \times 10^{- 5}$
$a_{2, Δ F (TOA)}$	127338.	−38110.4	2275.58	−59.1141	0.716736	−0.00323376
$Δ F (BOA)$	$b_{0, Δ F (BOA)}$	$b_{1, Δ F (BOA)}$	$b_{2, Δ F (BOA)}$	$b_{3, Δ F (BOA)}$	$b_{4, Δ F (BOA)}$	$b_{5, Δ F (BOA)}$
$a_{0} Δ F (BOA)$	−7.08587	0.868169	−0.0500522	0.00125157	$- 1.42286 \times 10^{- 5}$	$5.99614 \times 10^{- 8}$
$a_{1, Δ F (BOA)}$	−6631.38	322.184	−20.6101	0.557597	−0.00738187	$3.80091 \times 10^{- 5}$
$a_{2, Δ F (BOA)}$	99665.3	−48246.4	2895.67	−75.4873	0.922491	−0.00419713
Parameterizations for the INDOEX data set
$Δ A$	$b_{0, Δ A}$	$b_{1, Δ A}$	$b_{2, Δ A}$	$b_{3, Δ A}$	$b_{4, Δ A}$	$b_{5, Δ A}$
$a_{0, Δ A}$	−0.0545145	0.00917684	−0.000535120	$1.36040 \times 10^{- 5}$	$- 1.58723 \times 10^{- 7}$	$6.94176 \times 10^{- 10}$
$a_{1, Δ A}$	21.8979	−3.32518	0.192997	−0.00482700	$5.53033 \times 10^{- 5}$	$- 2.33139 \times 10^{- 7}$
$a_{2, Δ A}$	−834.159	126.970	−7.36130	0.183806	−0.00210022	$8.81309 \times 10^{- 6}$
$Δ F (TOA)$	$b_{0, Δ F (TOA)}$	$b_{1, Δ F (TOA)}$	$b_{2, Δ F (TOA)}$	$b_{3, Δ F (TOA)}$	$b_{4, Δ F (TOA)}$	$b_{5, Δ F (TOA)}$
$a_{0, Δ F (TOA)}$	1.79998	0.735887	−0.0406182	0.000997268	$- 1.05549 \times 10^{- 5}$	$3.76067 \times 10^{- 8}$
$a_{1, Δ F (TOA)}$	−6342.82	264.156	−17.2693	0.465143	−0.00618695	$3.23002 \times 10^{- 5}$
$a_{2, Δ F (TOA)}$	253212.	−12641.6	810.740	−21.7033	0.283137	−0.00143807
$Δ F (BOA)$	$b_{0, Δ F (BOA)}$	$b_{1, Δ F (BOA)}$	$b_{2, Δ F (BOA)}$	$b_{3, Δ F (BOA)}$	$b_{4, Δ F (BOA)}$	$b_{5, Δ F (BOA)}$
$a_{0, Δ F (BOA)}$	7.87148	1.42642	−0.0808956	0.00201754	$- 2.21702 \times 10^{- 5}$	$8.40544 \times 10^{- 8}$
$a_{1, Δ F (BOA)}$	−14495.8	190.286	−12.7915	0.361999	−0.00518459	$3.04847 \times 10^{- 5}$
$a_{2, Δ F (BOA)}$	579992.	−16127.4	1003.04	−26.8006	0.348517	−0.00180948

i	$a_{i, τ}$
i	EARLINET	INDOEX
0	0.0202674	−0.0980517
1	42.0411	75.3985
2	1264.39	−2880.41
$ϵ_{τ}$	0.073	0.078

Abstract

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Equations (10)

Applied Optics