Review of profiling oceanographic lidar

Churnside, James H.

doi:10.1117/1.oe.53.5.051405

Cited by 150 publications

(100 citation statements)

References 142 publications

(142 reference statements)

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“…30 The most successful is probably the quasisingle-scattering approximation, 2,31-33 which has been shown to provide good agreement with measured values. 34 This model has recently been used to obtain an expression for the lidar extinction-to-backscatter ratio for lidar inversions 35 that can provide the depth profile of phytoplankton directly.…”

Section: Introductionmentioning

confidence: 89%

Bio-optical model to describe remote sensing signals from a stratified ocean

Churnside

2015

J. Appl. Remote Sens

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Abstract. We use a bio-optical model of the optical properties of natural seawater to investigate the effects of subsurface chlorophyll layers on passive and active remote sensors. A thin layer of enhanced chlorophyll concentration reduces the remote sensing reflectance in the blue, while having little effect in the green. As a result, the chlorophyll concentration inferred from ocean color instruments will fall between the background concentration and the concentration in the layer, depending on the concentrations and the depth of the layer. For lidar, an iterative inversion algorithm is described that can reproduce the chlorophyll profile within the limits of the model. The model is extended to estimate column-integrated primary productivity, demonstrating that layers can contribute significantly to overall productivity. This contribution also depends on the chlorophyll concentrations and the depth of the layer. Using passive remote sensing alone to estimate primary productivity can lead to significant underestimation in the presence of subsurface plankton layers. Active remote sensing is not affected by this bias. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 89%

Bio-optical model to describe remote sensing signals from a stratified ocean

Churnside

2015

J. Appl. Remote Sens

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“…Both the parallel and perpendicular components of 532nm Figure 5 shows beam c (red line) derived from CALIOP depolarization measurements (blue line) together with collocated MODIS diffuse attenuation coefficient estimates scaled to 532 nm (black line). Difference between CALIOP's c estimates and c based on MODIS chlorophyll measurements (green line) [7] are mostly within 30%. …”

Section: Introduction Of the Conceptmentioning

confidence: 93%

“…Multiple scattering can cause depolarization [7]. Thus for non-absorbing media with spherical particles,  can be estimated accurately from lidar depolarization measurements [8][9] [10].…”

Section: Introduction Of the Conceptmentioning

confidence: 99%

Ocean Lidar Measurements of Beam Attenuation and a Roadmap to Accurate Phytoplankton Biomass Estimates

Behrenfeld

Hostetler

et al. 2016

EPJ Web of Conferences

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Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor).On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraftbased high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

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“…Beyond this, the cloud feedback decomposition into K c and C allows us to examine cloud-circulation coupling using longer data records. Furthermore, with the technological advances of the last decade [67][68][69][70][71][72][73] and our increasing understanding of how to exploit multi-instrument, multi-frequency active and passive synergy that began with the A-Train, the observational community is well positioned to address the aerosol-cloud-precipitation processes that presently drive the large uncertainty in ECS.…”

Section: Discussionmentioning

confidence: 99%

Using Active Remote Sensing to Evaluate Cloud-Climate Feedbacks: a Review and a Look to the Future

Mace

Berry

2017

Curr Clim Change Rep

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Uncertainty in the equilibrium climate sensitivity (ECS) of the Earth continues to be large. Aspects of the cloud feedback problem have been identified as fundamental to the uncertainty in ECS. Recent analyses have shown that changes to cloud forcing with climate change can be decomposed into contributions from changes in cloud occurrence that are proportional to globally averaged temperature change and changes associated with rapid adjustments in the system that are independent of changes to globally averaged surface temperature. Together these responses enhance warming due to (1) cloud feedback from increasing cloud altitude by upper tropospheric clouds and (2) decreases in cloud coverage by marine boundary layer clouds. We argue that active remote sensing from space can play a unique and crucial role in constraining our understanding of these separate phenomena. For 1, the feedback associated with changing tropical cirrus is predicted to emerge from the statistical noise of the climate system within the next one to two decades. However, active remote sensing will need to continue for that signal to be observed since accurate placement of these clouds in the vertical dimension is necessary. For 2, the processes associated with changes to marine boundary layer clouds have been linked to the coupling between cloud and precipitation microphysics and air motions over remote ocean basins where precipitation formation in shallow convection is modulated by changes to aerosols and thermodynamics. Exploiting the synergy in combined active and passive remote sensing is likely one of the only ways of constraining our evolving theoretical understanding of low-level cloud processes as represented in cloudresolving models and for validating global-scale models.

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Review of profiling oceanographic lidar

Cited by 150 publications

References 142 publications

Bio-optical model to describe remote sensing signals from a stratified ocean

Bio-optical model to describe remote sensing signals from a stratified ocean

Ocean Lidar Measurements of Beam Attenuation and a Roadmap to Accurate Phytoplankton Biomass Estimates

Using Active Remote Sensing to Evaluate Cloud-Climate Feedbacks: a Review and a Look to the Future

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