Multiple scattering lidar

Bissonnette, Luc; Hutt, Daniel L.

doi:10.1364/ao.29.005045

Cited by 47 publications

(18 citation statements)

References 2 publications

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“…In addition to its effect on the width of the forward‐scattering peak, multiple scattering of light by spherical cloud droplets causes an observable modification of the polarization state of the backscattered light (Bissonnette, ). Single‐scattered light does not change its polarization state when scattered exactly into the backward direction of 180°.…”

Section: Ground‐based Remote Sensing Approachesmentioning

confidence: 99%

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives

Grosvenor

Sourdeval

Zuidema³

et al. 2018

Reviews of Geophysics

265

337

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The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.

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Section: Ground‐based Remote Sensing Approachesmentioning

confidence: 99%

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives

Grosvenor

Sourdeval

Zuidema³

et al. 2018

Reviews of Geophysics

265

337

View full text Add to dashboard Cite

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“…Although fairly straightforward, thresholds set too tolerant may result in strong ice precipitation exhibiting relatively large backscatter and occasionally low depolarization values (e.g., Platt, ; Sassen, , ) incorrectly being detected as a liquid layer. Conversely, too conservative thresholds might result in loss of liquid layer data (missed detections), mostly due to lower backscatter cross‐section values near the liquid layer edges (and possibly higher depolarization ratios, if the cloudy volume contains ice particles), and because of multiple scattering resulting in higher depolarization ratio values (Bissonnette, ; see Figure S1 in the supporting information). Moreover, this method is rather limited when the lidar does not possess inherent backscatter cross‐section extraction capabilities, for example, the MPL (Campbell et al, ).…”

Section: Introductionmentioning

confidence: 99%

Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data

et al. 2018

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Liquid layers in clouds affect their microphysical processes, as well as the surface energy budget. Studies focusing on these and other areas of research are often in need of skillful estimation of liquid‐bearing cloud layer boundaries. The bases of these layers are predominantly determined by ground‐based lidar instruments. Most studies requiring liquid cloud base height (LCBH) information use either fixed lidar parameter (depolarization and/or backscatter cross section) thresholds or cloud base height data products that do not distinguish between ice and liquid, all of which might introduce inconsistencies and errors in the resolved LCBH. In this paper, two explicit LCBH detection algorithms are presented. The first algorithm uses the high spectral resolution lidar (HSRL) data. Examination of this algorithm in multiple cases and scenarios during numerous days and first‐order comparison with microwave‐radiometer data show satisfactory results. The second algorithm incorporates widely available micropulse lidar (MPL) data for the LCBH detection. A 1‐year long comparison of data gathered at Barrow, Alaska, and McMurdo Station, Antarctica, which includes other cloud base detection methodologies (ceilometer, MPL value‐added product cloud base height, and LCBHs detected using a fixed MPL depolarization threshold), emphasizes the merits of the presented MPL algorithm. Examination of several unusual LCBH configurations suggests that the current practice of operating lidar at a tilting angle of 4° off zenith may not be sufficient to avoid specular reflection from oriented ice crystals. Data collected at Madison Wisconsin are used to show that specular reflection may impact measurements even at 4°.

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“…For clouds of Cumulus type I (Cl cloud) and Nimbostratus type (Ns cloud) values of 6 and 1 are valid for a and y respectively. The derivative form the equation (1) after substituting such constants is d -n( r) = ar5(6 -hr )e -hr (2) dr When the critical (or mode) radius rat which the number of cloud molecules is at a maximum is given, i.e., b=-r r=r, Once the size distribution has been decided, the volume differential scattering coefficient is decided, and then the excitation coefficient is decided. The calculation was repeated by changing the value of r until the calculated excitation coefficient was the same as the experimental value.…”

Section: Scattering Body Modelmentioning

confidence: 99%

Comparison of experiment and simulation of range (time)-resolved laser multiple scattering in cloud

et al. 2001

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Laser multiple scattering phenomena occurring inside a cloud were experimentally confirmed by showing the spread of the scattered laser light as a two-dimensional spatial distribution image. By changing the gate delay time of a CCD camera, range-resolved scattering images were obtained with a gate time width of 100 ns, which corresponds to a resolution of 15 m cloud depth. The size of the scattering area enlarged just after the laser pulse hit the cloud, and gradually decreased as the beam went deeper inside the cloud where extinction overcame the multiple scattering. An extinction coefficient of 3.62x102 [ m11 was calculated from the laser beam transmission. The particle size distribution of the cloud was derived to fit the obtained extinction coefficient. A Monte Carlo simulation using the new distribution function reproduced the experimental scattering image very well. This new experimental and simulation method to show the multiple scattering as a spread image will provide informative knowledge for cloud lidar observations.

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Multiple scattering lidar

Cited by 47 publications

References 2 publications

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives

Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data

Comparison of experiment and simulation of range (time)-resolved laser multiple scattering in cloud

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