Piece-wise variance method for signal-to-noise ratio estimation in elastic/Raman lidar signals

Reba, Mohd Nadzri Md; Rocadenbosch, Francesc; Sicard, Michaël; Muñoz, C.; Tomás, S.A.

doi:10.1109/igarss.2007.4423515

Cited by 8 publications

(14 citation statements)

References 3 publications

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“…We constructed a lidar signal from the model aerosol and molecular distributions, which were contaminated by stochastic noise generated from normal Gaussian white noise. The Signal-to-Noise Ratio (SNR) estimation method used in the present study is described by Reba et al (2007). The lidar ratios at 532 nm and 1,064 nm were then calculated in a similar manner for 1,000 cases using the performance function to minimize the differences between CALIOP and Aksu-lidar backscattering coefficients.…”

Section: Caliop Analysismentioning

confidence: 99%

Validation of the Dust Layer Structure over the Taklimakan Desert, China by the CALIOP Space-Borne Lidar Using Ground-Based Lidar

Jin

Kai

Shibata

et al. 2010

SOLA

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A ground-based lidar observation was carried out in the northwest of China to validate the space-borne lidar CALIOP on 23 March 2009. Combining backscatter profiles of the groundbased lidar and CALIOP, lidar ratio (extinction to backscattering ratio) was retrieved for 532 nm and 1064 nm wavelengths by using performance function that minimizing the difference between the ground-based lidar and CALIOP for backscattering coefficient. The correlation coefficients between them were 0.98 for 532 nm and 0.95 1064 nm, respectively. Using the retrieved lidar ratio, the color ratio and aerosol optical depth (AOD) were calculated. The observed aerosols and clouds were classified into three groups (boundary layer dust, free tropospheric aerosol and cirrus cloud) according to a relationship between color ratio and 532 nm-backscattering coefficient.

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Section: Caliop Analysismentioning

confidence: 99%

Validation of the Dust Layer Structure over the Taklimakan Desert, China by the CALIOP Space-Borne Lidar Using Ground-Based Lidar

Jin

Kai

Shibata

et al. 2010

SOLA

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“…The SNR levels given in Section II-B ensure a virtually noiseless log-range-corrected signal, G(h), over the whole processing range [44]. The noise variance has been estimated according to the procedure described in [45]. In Section III, the temporal resolution of the retrieval products (rainfall rate, RR, and rain extinction coefficient, α) is one time bin (80 s).…”

Section: B Event Overview and Preprocessing Methodsmentioning

confidence: 99%

Ceilometer-Based Rain-Rate Estimation: A Case-Study Comparison With S-Band Radar and Disdrometer Retrievals in the Context of VORTEX-SE

Rocadenbosch

Barragán

Frasier

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Attenuated backscatter measurements from a Vaisala CL31 ceilometer and a modified form of the wellknown slope method are used to derive the ceilometer extinction profiles during rain events, restricted to rainfall rates (RRs) below approximately 10 mm/h. RR estimates from collocated S-band radar and portable disdrometer are used to derive the RR-to-extinction correlation models for the ceilometer-radar and ceilometer-disdrometer combinations. Data were collected during an intensive observation period of the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) conducted in northern Alabama. These models are used to estimate the RR from the ceilometer observations in similar situations that do not have collocated radar or the disdrometer. Such correlation models are, however, limited by the different temporal and spatial resolutions of the measured variables, measurement capabilities of the instruments, and the inherent assumption of a homogeneous atmosphere. An empirical method based on extinction and RR uncertainty scoring and covariance fitting are proposed to solve, in part, these limitations.

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“…Noise variance estimation can be carried out using, for example, piecewise or parametric SNR estimators [27], [28]. The noise model variance follows the well-known model described in [23].…”

Section: Filter Modelsmentioning

confidence: 99%

Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

Lange

Tiana-Alsina

Saeed

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios.Index Terms-Adaptive kalman filtering, laser radar, remote sensing, signal processing.

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Piece-wise variance method for signal-to-noise ratio estimation in elastic/Raman lidar signals

Cited by 8 publications

References 3 publications

Validation of the Dust Layer Structure over the Taklimakan Desert, China by the CALIOP Space-Borne Lidar Using Ground-Based Lidar

Validation of the Dust Layer Structure over the Taklimakan Desert, China by the CALIOP Space-Borne Lidar Using Ground-Based Lidar

Ceilometer-Based Rain-Rate Estimation: A Case-Study Comparison With S-Band Radar and Disdrometer Retrievals in the Context of VORTEX-SE

Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

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