Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data

Eduard Chemyakin; Detlef Müller; Sharon Burton; Alexei Kolgotin; Chris Hostetler; Richard Ferrare

doi:10.1364/AO.53.007252

Applied Optics
Vol. 53,
Issue 31,
pp. 7252-7266
(2014)
•https://doi.org/10.1364/AO.53.007252

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data

Eduard Chemyakin, Detlef Müller, Sharon Burton, Alexei Kolgotin, Chris Hostetler, and Richard Ferrare

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Eduard Chemyakin, Detlef Müller, Sharon Burton, Alexei Kolgotin, Chris Hostetler, and Richard Ferrare, "Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data," Appl. Opt. 53, 7252-7266 (2014)

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Abstract

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the “3 backscatter $(β) + 2$ extinction ( $α$ )” configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested “ $3 β + 1 α$ ,” “ $2 β + 1 α$ ,” and “ $3 β$ ” lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center’s airborne “ $3 β + 2 α$ ” High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov’s inversion with regularization. This advanced algorithm has recently undergone development to allow automated and unsupervised processing; the arrange and average algorithm can be used as a preclassifier to further improve its speed and precision. First tests of the performance of arrange and average algorithm are encouraging. We used a set of 48 different monomodal particle size distributions, 4 real parts and 15 imaginary parts of the complex refractive index. All in all we tested 2880 different optical data sets for 0%, 10%, and 20% Gaussian measurement noise (one-standard deviation). In the case of the “ $3 β + 2 α$ ” configuration with 10% measurement noise, we retrieve the particle effective radius to within 27% for 1964 (68.2%) of the test optical data sets. The number concentration is obtained to 76%, the surface area concentration to 16%, and the volume concentration to 30% precision. The “ $3 β$ ” configuration performs significantly poorer. The performance of the “ $3 β + 1 α$ ” and “ $2 β + 1 α$ ” configurations is intermediate between the “ $3 β + 2 α$ ” and the “ $3 β$ .”

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$r_{med}$ (nm)	15	25	35	45	55	65	75
	85	95	105	115	125	135	145
	155	165	175	185	195	205	215
	225	235	245	255	265	275	285
	295	305
$σ$	1.475	1.525	1.575	1.625	1.675	1.725	1.775
	1.825	1.875	1.925	1.975	2.025	2.075	2.125
	2.175	2.225	2.275	2.325	2.375	2.425	2.475
	2.525
$m_{R}$	1.29	1.31	1.33	1.35	1.37	1.39	1.41
	1.43	1.45	1.47	1.49	1.51	1.53	1.55
	1.57	1.59	1.61	1.63	1.65	1.67	1.69
	1.71
$m_{I} \cdot 10^{- 3}$	0	0.25	1.25	2.25	3.25	4.25	5.25
	6.25	7.25	8.25	9.25	10.25	11.25	12.25
	13.25	14.25	15.25	16.25	17.25	18.25	19.25
	20.25	21.25	22.25	23.25	24.25	25.25	26.25
	27.25	28.25	29.25	30.25	31.25	32.25	33.25
	34.25	35.25	36.25	37.25	38.25	39.25	40.25
	41.25	42.25	43.25	44.25	45.25	46.25	47.25
	48.25	49.25	50.25

$r_{med}$ (nm)	20	60	100	140	180	220	260	300
$σ$	1.5	1.7	1.9	2.1	2.3	2.5
$m_{R}$	1.4	1.5	1.6	1.7
$m_{I} \cdot 10^{- 3}$	0	0.1	1.0	2.5	5.0	7.5	10.0	15.0
	20.0	25.0	30.0	35.0	40.0	45.0	50.0

	Noiseless			Gaussian Noise. 10%			Gaussian Noise. 20%
Lidar Configuration	68.2%	95.4%	100%	68.2%	95.4%	100%	68.2%	95.4%	100%
	Retrieval error for real part of CRI
“ $3 β + 2 α$ ”	0.054	0.116	0.256	0.08	0.187	0.363	0.09	0.201	0.378
“ $3 β + 1 α$ ”	0.062	0.131	0.303	0.085	0.194	0.382	0.096	0.215	0.402
“ $2 β + 1 α$ ”	0.083	0.177	0.318	0.09	0.195	0.398	0.097	0.217	0.408
“ $3 β$ ”	0.103	0.257	0.4	0.137	0.29	0.41	0.163	0.303	0.41
	Retrieval error for imaginary part of CRI
“ $3 β + 2 α$ ”	0.0067	0.0182	0.0313	0.0119	0.0291	0.0455	0.0144	0.0318	0.0482
“ $3 β + 1 α$ ”	0.0087	0.0197	0.0401	0.0131	0.0294	0.0491	0.0154	0.0346	0.0496
“ $2 β + 1 α$ ”	0.0137	0.0278	0.0455	0.0148	0.032	0.0494	0.0158	0.0373	0.0496
“ $3 β$ ”	0.0179	0.0323	0.0489	0.0204	0.0351	0.05	0.0228	0.0409	0.05
	Relative retrieval error for effective radius, %
“ $3 β + 2 α$ ”	10	25	107	27	62	218	39	99	388
“ $3 β + 1 α$ ”	15	40	163	32	111	281	45	170	536
“ $2 β + 1 α$ ”	28	92	291	38	129	421	56	205	695
“ $3 β$ ”	40	161	535	50	188	554	56	239	1137
	Relative retrieval error for total number concentration, %
“ $3 β + 2 α$ ”	22	94	2218	93	678	10871	111	1890	$3.7 \cdot 10^{5}$
“ $3 β + 1 α$ ”	45	298	5974	76	765	$1.3 \cdot 10^{5}$	95	2948	$4.8 \cdot 10^{5}$
“ $2 β + 1 α$ ”	90	1812	50919	101	3189	$7.7 \cdot 10^{5}$	166	10715	$9.2 \cdot 10^{6}$
“ $3 β$ ”	381	21113	$5.2 \cdot 10^{5}$	393	26111	$1.2 \cdot 10^{6}$	480	51970	$1.9 \cdot 10^{7}$
	Relative retrieval error for total surface area concentration, %
“ $3 β + 2 α$ ”	2	21	253	16	75	596	27	123	2007
“ $3 β + 1 α$ ”	4	42	430	17	91	628	32	180	2787
“ $2 β + 1 α$ ”	16	97	1491	23	195	2300	37	525	15412
“ $3 β$ ”	92	1307	7367	106	1390	11668	114	1603	24536
	Relative retrieval error for total volume concentration, %
“ $3 β + 2 α$ ”	12	38	173	30	78	447	41	100	648
“ $3 β + 1 α$ ”	16	57	349	35	120	485	50	163	1023
“ $2 β + 1 α$ ”	25	76	537	37	128	1025	54	235	4115
“ $3 β$ ”	62	550	1506	76	670	4085	78	683	5258

$r_{med}$ (nm)	15	25	35	45	55	65	75
	85	95	105	115	125	135	145
	155	165	175	185	195	205	215
	225	235	245	255	265	275	285
	295	305
$σ$	1.475	1.525	1.575	1.625	1.675	1.725	1.775
	1.825	1.875	1.925	1.975	2.025	2.075	2.125
	2.175	2.225	2.275	2.325	2.375	2.425	2.475
	2.525
$m_{R}$	1.29	1.31	1.33	1.35	1.37	1.39	1.41
	1.43	1.45	1.47	1.49	1.51	1.53	1.55
	1.57	1.59	1.61	1.63	1.65	1.67	1.69
	1.71
$m_{I} \cdot 10^{- 3}$	0	0.25	1.25	2.25	3.25	4.25	5.25
	6.25	7.25	8.25	9.25	10.25	11.25	12.25
	13.25	14.25	15.25	16.25	17.25	18.25	19.25
	20.25	21.25	22.25	23.25	24.25	25.25	26.25
	27.25	28.25	29.25	30.25	31.25	32.25	33.25
	34.25	35.25	36.25	37.25	38.25	39.25	40.25
	41.25	42.25	43.25	44.25	45.25	46.25	47.25
	48.25	49.25	50.25

$r_{med}$ (nm)	20	60	100	140	180	220	260	300
$σ$	1.5	1.7	1.9	2.1	2.3	2.5
$m_{R}$	1.4	1.5	1.6	1.7
$m_{I} \cdot 10^{- 3}$	0	0.1	1.0	2.5	5.0	7.5	10.0	15.0
	20.0	25.0	30.0	35.0	40.0	45.0	50.0

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Applied Optics