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Stuart A. Young and Mark A. Vaughan

Abstract

This work describes the algorithms used for the fully automated retrieval of profiles of particulate extinction coefficients from the attenuated backscatter data acquired by the lidar on board the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. The close interaction of the Hybrid Extinction Retrieval Algorithms (HERA) with the preceding processes that detect and classify atmospheric features (i.e., cloud and aerosol layers) is described within the context of the analysis of measurements from scenes of varying complexity. Two main components compose HERA: a top-level algorithm that selects the analysis pathway, the order of processing, and the analysis parameters, depending on the nature and spatial extent of the atmospheric features to be processed; and a profile solver or “extinction engine,” whose task it is to retrieve profiles of particulate extinction and backscatter coefficients from specified sections of an atmospheric scene defined by the top-level algorithm. The operation of these components is described using synthetic data derived from Lidar In Space Technology Experiment (LITE) measurements. The performance of the algorithms is illustrated using CALIPSO measurements acquired during the mission on 1 January 2007.

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Stuart A. Young, Mark A. Vaughan, Ralph E. Kuehn, and David M. Winker

Abstract

Profiles of atmospheric cloud and aerosol extinction coefficients are retrieved on a global scale from measurements made by the lidar on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission since mid-June 2006. This paper presents an analysis of how the uncertainties in the inputs to the extinction retrieval algorithm propagate as the retrieval proceeds downward to lower levels of the atmosphere. The mathematical analyses, which are being used to calculate the uncertainties reported in the current (version 3) data release, are supported by figures illustrating the retrieval uncertainties in both simulated and actual data. Equations are also derived that describe the sensitivity of the extinction retrieval algorithm to errors in profile calibration and in the lidar ratios used in the retrievals. Biases that could potentially result from low signal-to-noise ratios in the data are also examined. Using simulated data, the propagation of bias errors resulting from errors in profile calibration and lidar ratios is illustrated.

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Stuart A. Young, Mark A. Vaughan, Ralph E. Kuehn, and David M. Winker

Abstract

An error in a recent analysis of the sensitivity of retrievals of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) particulate optical properties to errors in various input parameters is described. This error was in the specification of an intermediate variable that was used to write a general equation for the sensitivities to errors in either the renormalization (calibration) factor or in the lidar ratio used in the retrieval, or both. The result of this incorrect substitution (an additional multiplicative factor to the exponent of the particulate transmittance) was then copied to some intermediate equations; the corrected versions of which are presented here. Fortunately, however, all of the final equations for the specific cases of renormalization and lidar ratio errors are correct, as are all of the figures and approximations, because these were derived directly from equations for the specific errors and not from the equation for the general case. All of the other sections, including the uncertainty analyses and the analyses of sensitivities to low signal-to-noise ratios and errors in constrained retrievals, and the presentations of errors and uncertainties in simulated and actual data are unaffected.

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James A. Carton, Stuart A. Cunningham, Eleanor Frajka-Williams, Young-Oh Kwon, David P. Marshall, and Rym Msadek
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David M. Winker, Mark A. Vaughan, Ali Omar, Yongxiang Hu, Kathleen A. Powell, Zhaoyan Liu, William H. Hunt, and Stuart A. Young

Abstract

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is a two-wavelength polarization lidar that performs global profiling of aerosols and clouds in the troposphere and lower stratosphere. CALIOP is the primary instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, which has flown in formation with the NASA A-train constellation of satellites since May 2006. The global, multiyear dataset obtained from CALIOP provides a new view of the earth’s atmosphere and will lead to an improved understanding of the role of aerosols and clouds in the climate system. A suite of algorithms has been developed to identify aerosol and cloud layers and to retrieve a variety of optical and microphysical properties. CALIOP represents a significant advance over previous space lidars, and the algorithms that have been developed have many innovative aspects to take advantage of its capabilities. This paper provides a brief overview of the CALIPSO mission, the CALIOP instrument and data products, and an overview of the algorithms used to produce these data products.

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Kathleen A. Powell, Chris A. Hostetler, Mark A. Vaughan, Kam-Pui Lee, Charles R. Trepte, Raymond R. Rogers, David M. Winker, Zhaoyan Liu, Ralph E. Kuehn, William H. Hunt, and Stuart A. Young

Abstract

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission was launched in April 2006 and has continuously acquired collocated multisensor observations of the spatial and optical properties of clouds and aerosols in the earth’s atmosphere. The primary payload aboard CALIPSO is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which makes range-resolved measurements of elastic backscatter at 532 and 1064 nm and linear depolarization ratios at 532 nm. CALIOP measurements are important in reducing uncertainties that currently limit understanding of the global climate system, and it is essential that these measurements be accurately calibrated. This work describes the procedures used to calibrate the 532-nm measurements acquired during the nighttime portions of the CALIPSO orbits. Accurate nighttime calibration of the 532-nm parallel-channel data is fundamental to the success of the CALIOP measurement scheme, because the nighttime calibration is used to infer calibration across the day side of the orbits and all other channels are calibrated relative to the 532-nm parallel channel. The theoretical basis of the molecular normalization technique as applied to space-based lidar measurements is reviewed, and a comprehensive overview of the calibration algorithm implementation is provided. Also included is a description of a data filtering procedure that detects and removes spurious high-energy events that would otherwise introduce large errors into the calibration. Error estimates are derived and comparisons are made to validation data acquired by the NASA airborne high–spectral resolution lidar. Similar analyses are also presented for the 532-nm perpendicular-channel calibration technique.

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Yongxiang Hu, David Winker, Mark Vaughan, Bing Lin, Ali Omar, Charles Trepte, David Flittner, Ping Yang, Shaima L. Nasiri, Bryan Baum, Robert Holz, Wenbo Sun, Zhaoyan Liu, Zhien Wang, Stuart Young, Knut Stamnes, Jianping Huang, and Ralph Kuehn

Abstract

The current cloud thermodynamic phase discrimination by Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) is based on the depolarization of backscattered light measured by its lidar [Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)]. It assumes that backscattered light from ice crystals is depolarizing, whereas water clouds, being spherical, result in minimal depolarization. However, because of the relationship between the CALIOP field of view (FOV) and the large distance between the satellite and clouds and because of the frequent presence of oriented ice crystals, there is often a weak correlation between measured depolarization and phase, which thereby creates significant uncertainties in the current CALIOP phase retrieval. For water clouds, the CALIOP-measured depolarization can be large because of multiple scattering, whereas horizontally oriented ice particles depolarize only weakly and behave similarly to water clouds. Because of the nonunique depolarization–cloud phase relationship, more constraints are necessary to uniquely determine cloud phase. Based on theoretical and modeling studies, an improved cloud phase determination algorithm has been developed. Instead of depending primarily on layer-integrated depolarization ratios, this algorithm differentiates cloud phases by using the spatial correlation of layer-integrated attenuated backscatter and layer-integrated particulate depolarization ratio. This approach includes a two-step process: 1) use of a simple two-dimensional threshold method to provide a preliminary identification of ice clouds containing randomly oriented particles, ice clouds with horizontally oriented particles, and possible water clouds and 2) application of a spatial coherence analysis technique to separate water clouds from ice clouds containing horizontally oriented ice particles. Other information, such as temperature, color ratio, and vertical variation of depolarization ratio, is also considered. The algorithm works well for both the 0.3° and 3° off-nadir lidar pointing geometry. When the lidar is pointed at 0.3° off nadir, half of the opaque ice clouds and about one-third of all ice clouds have a significant lidar backscatter contribution from specular reflections from horizontally oriented particles. At 3° off nadir, the lidar backscatter signals for roughly 30% of opaque ice clouds and 20% of all observed ice clouds are contaminated by horizontally oriented crystals.

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Mark A. Vaughan, Kathleen A. Powell, David M. Winker, Chris A. Hostetler, Ralph E. Kuehn, William H. Hunt, Brian J. Getzewich, Stuart A. Young, Zhaoyan Liu, and Matthew J. McGill

Abstract

Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth’s atmosphere is critical in assessing the planet’s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars.

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Diana Greenslade, Mark Hemer, Alex Babanin, Ryan Lowe, Ian Turner, Hannah Power, Ian Young, Daniel Ierodiaconou, Greg Hibbert, Greg Williams, Saima Aijaz, João Albuquerque, Stewart Allen, Michael Banner, Paul Branson, Steve Buchan, Andrew Burton, John Bye, Nick Cartwright, Amin Chabchoub, Frank Colberg, Stephanie Contardo, Francois Dufois, Craig Earl-Spurr, David Farr, Ian Goodwin, Jim Gunson, Jeff Hansen, David Hanslow, Mitchell Harley, Yasha Hetzel, Ron Hoeke, Nicole Jones, Michael Kinsela, Qingxiang Liu, Oleg Makarynskyy, Hayden Marcollo, Said Mazaheri, Jason McConochie, Grant Millar, Tim Moltmann, Neal Moodie, Joao Morim, Russel Morison, Jana Orszaghova, Charitha Pattiaratchi, Andrew Pomeroy, Roger Proctor, David Provis, Ruth Reef, Dirk Rijnsdorp, Martin Rutherford, Eric Schulz, Jake Shayer, Kristen Splinter, Craig Steinberg, Darrell Strauss, Greg Stuart, Graham Symonds, Karina Tarbath, Daniel Taylor, James Taylor, Darshani Thotagamuwage, Alessandro Toffoli, Alireza Valizadeh, Jonathan van Hazel, Guilherme Vieira da Silva, Moritz Wandres, Colin Whittaker, David Williams, Gundula Winter, Jiangtao Xu, Aihong Zhong, and Stefan Zieger

Abstract

The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.

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Diana Greenslade, Mark Hemer, Alex Babanin, Ryan Lowe, Ian Turner, Hannah Power, Ian Young, Daniel Ierodiaconou, Greg Hibbert, Greg Williams, Saima Aijaz, João Albuquerque, Stewart Allen, Michael Banner, Paul Branson, Steve Buchan, Andrew Burton, John Bye, Nick Cartwright, Amin Chabchoub, Frank Colberg, Stephanie Contardo, Francois Dufois, Craig Earl-Spurr, David Farr, Ian Goodwin, Jim Gunson, Jeff Hansen, David Hanslow, Mitchell Harley, Yasha Hetzel, Ron Hoeke, Nicole Jones, Michael Kinsela, Qingxiang Liu, Oleg Makarynskyy, Hayden Marcollo, Said Mazaheri, Jason McConochie, Grant Millar, Tim Moltmann, Neal Moodie, Joao Morim, Russel Morison, Jana Orszaghova, Charitha Pattiaratchi, Andrew Pomeroy, Roger Proctor, David Provis, Ruth Reef, Dirk Rijnsdorp, Martin Rutherford, Eric Schulz, Jake Shayer, Kristen Splinter, Craig Steinberg, Darrell Strauss, Greg Stuart, Graham Symonds, Karina Tarbath, Daniel Taylor, James Taylor, Darshani Thotagamuwage, Alessandro Toffoli, Alireza Valizadeh, Jonathan van Hazel, Guilherme Vieira da Silva, Moritz Wandres, Colin Whittaker, David Williams, Gundula Winter, Jiangtao Xu, Aihong Zhong, and Stefan Zieger
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