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In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar "scene", i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected "clear air" value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds. [ABSTRACT FROM AUTHOR]

Abstract: We advance a novel retrieval technique that combines a Raman and multi-wavelength elastic backscattered signals to retrieve multi-wavelength lidar ratio profiles of aerosol. With profile of backscatter coefficients at 355 nm retrieved from elastic backscatter signal at 355 nm and Raman scattering signal at 387 nm, lidar ratio profiles can be calculated at 532 nm and 1064 nm from the elastic backscatter signals at these wavelengths, taking advantage that the 532 nm/355 nm and 1064 nm/355 nm backscatter ratios are generally approximately equal for two neighboring range bins. This technique has been tested using numerical simulations and applied to lidar measurements at the Hampton University, Hampton, Virginia. [Copyright &y& Elsevier]

Abstract: Lidar ratio (i.e., extinction-to-backscatter ratio) is a key parameter required for retrieving extinction profiles and optical depths from elastic backscatter lidar measurements, and the quality of any extinction retrieval depends critically on the accuracy of the assumed or measured lidar ratio. In this study, we analyze the first two and a half years of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data acquired during nighttime. Distributions of the effective lidar ratio (ELR), which is the product of the lidar ratio and an instrument-dependent multiple scattering factor, are derived for opaque dust layers observed by CALIOP over the North Africa. The median and mean ELR values are, respectively, 36.4 and 38.5sr at 532nm and 47.7 and 50.3sr at 1064nm. For these opaque dust layers, the derived ELR decreases as the volume depolarization ratio (VDR) increases, reflecting the impact of multiple scattering within the dense layers. The particulate depolarization ratio is typically ∼0.3 at 532nm for African dust observed by CALIOP. This ratio can increase to ∼0.4 in the presence of significant multiple scattering. Correspondingly, the calculated ELR will decrease to ∼20sr at 532nm and to ∼30sr at 1064nm. The median and mean effective lidar ratio values approach, respectively, to 38 and 40sr at 532nm and 52 and 55sr at 1064nm for smaller VDR values measured in less dense layers where the multiple scattering is relatively insignificant. These values are very close to those derived in previous case studies for moderately dense dust. Case studies are also performed to examine the impacts of multiple scattering. The results obtained are generally consistent with Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Descriptions are provided of the aerosol classification algorithms and the extinction-to-backscatter ratio (lidar ratio) selection schemes for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products. One year of CALIPSO level 2 version 2 data are analyzed to assess the veracity of the CALIPSO aerosol-type identification algorithm and generate vertically resolved distributions of aerosol types and their respective optical characteristics. To assess the robustness of the algorithm, the interannual variability is analyzed by using a fixed season (June–August) and aerosol type (polluted dust) over two consecutive years (2006 and 2007). The CALIPSO models define six aerosol types: clean continental, clean marine, dust, polluted continental, polluted dust, and smoke, with 532-nm (1064 nm) extinction-to-backscatter ratios Sa of 35 (30), 20 (45), 40 (55), 70 (30), 65 (30), and 70 (40) sr, respectively. This paper presents the global distributions of the CALIPSO aerosol types, the complementary distributions of integrated attenuated backscatter, and the volume depolarization ratio for each type. The aerosol-type distributions are further partitioned according to surface type (land/ocean) and detection resolution (5, 20, and 80 km) for optical and spatial context, because the optically thick layers are found most often at the smallest spatial resolution. Except for clean marine and polluted continental, all the aerosol types are found preferentially at the 80-km resolution. Nearly 80% of the smoke cases and 60% of the polluted dust cases are found over water, whereas dust and polluted continental cases are found over both land and water at comparable frequencies. Because the CALIPSO observables do not sufficiently constrain the determination of the aerosol, the surface type is used to augment the selection criteria. Distributions of the total attenuated color ratios show that the use of surface type in the typing algorithm does not result in abrupt and artificial changes in aerosol type or extinction. [ABSTRACT FROM AUTHOR]

Abstract: The performance of a space-based 2.1-μm coherent Doppler wind lidar (CDWL) measurement at a single laser shot in clear-air conditions is computer simulated, based on the coherent Doppler lidar theory developed in the recent decades, and using the global aerosol distribution derived from one year (March 2007–February 2008) of the CALIPSO lidar measurements. The accuracy of radial wind velocity good estimates and the fraction of good estimates, depending on backscattered signals from aerosols, generally decrease with altitude. A critical altitude is defined as the altitude below which the good estimate fraction of velocity estimates is larger than 90.0%. With a laser pulse energy of 250mJ at an off-nadir pointing angle of 45°, a telescope of 1m in diameter and a vertical range resolution of ∼800m, this critical altitude can reach an altitude of 4.0–5.0km between 20°S and 40°N where dust and biomass burning aerosols are ubiquitous. The critical altitude gradually decreases as approaching the two poles and drops to 0.5–1.5km in the polar regions. When the laser pulse energy is reduced to 100mJ, the critical altitude is generally decreased by ∼0.5km and can still reach an altitude of 3.5–4.5km in the dust and smoke aerosol enriched tropical and subtropical regions. A laser pulse energy of only a few millijoules can still achieve velocity measurements with an RMS error smaller than 1ms−1 and a good estimate fraction better than 90% in the lowest kilometers of the troposphere. [Copyright &y& Elsevier]

Abstract: CALIPSO aerosol backscatter enhancement in the transition zone between clouds and clear sky areas is revisited with particular attention to effects of data selection based on the confidence level of cloud–aerosol discrimination (CAD). The results show that backscatter behavior in the transition zone strongly depends on the CAD confidence level. Higher confidence level data has a flatter backscatter far away from clouds and a much sharper increase near clouds (within 4km), thus a smaller transition zone. For high confidence level data it is shown that the overall backscatter enhancement is more pronounced for small clear-air segments and horizontally larger clouds. The results suggest that data selection based on CAD reduces the possible effects of cloud contamination when studying aerosol properties in the vicinity of clouds. [Copyright &y& Elsevier]

Abstract: Ice water content (IWC) is a standard product of cloud radar measurements. In this work, cloud radar cross-sections of various ice clouds are modeled to examine the relationship between the radar signal and the IWC. We report that using backscatter signal at cloud radar wavelength to retrieve IWC results in large uncertainties. Particle size distribution is the primary cause for the uncertainty in the retrieved IWC at radar wavelengths, though particle shape and orientation also play significant roles. Particularly in this study, we demonstrate that using both transmitted waves through the clouds (extinction) and backscattered waves from the clouds to retrieve the mean particle size and then using the mean particle size for IWC retrieval reduces the uncertainty. IWC retrieval can be improved with size distribution derived from dual wavelength cloud radar. [ABSTRACT FROM AUTHOR]