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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]

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. [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]

The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite was launched in April 2006 to provide global vertically resolved measurements of clouds and aerosols. Correct discrimination between clouds and aerosols observed by the lidar aboard the CALIPSO satellite is critical for accurate retrievals of cloud and aerosol optical properties and the correct interpretation of measurements. This paper reviews the theoretical basis of the CALIPSO lidar cloud and aerosol discrimination (CAD) algorithm, and describes the enhancements made to the version 2 algorithm that is used in the current data release (release 2). The paper also presents a preliminary assessment of the CAD performance based on one full day (12 August 2006) of expert manual classification and on one full month (July 2006) of the CALIOP 5-km cloud and aerosol layer products. Overall, the CAD algorithm works well in most cases. The 1-day manual verification suggests that the success rate is in the neighborhood of 90% or better. Nevertheless, several specific layer types are still misclassified with some frequency. Among these, the most prevalent are dense dust and smoke close to the source regions. The analysis of the July 2006 data showed that the misclassification of dust as cloud occurs for <1% of the total tropospheric cloud and aerosol features found. Smoke layers are misclassified less frequently than are dust layers. Optically thin clouds in the polar regions can be misclassified as aerosols. While the fraction of such misclassifications is small compared with the number of aerosol features found globally, caution should be taken when studies are performed on the aerosol in the polar regions. Modifications will be made to the CAD algorithm in future data releases, and the misclassifications encountered in the current data release are expected to be reduced greatly. [ABSTRACT FROM AUTHOR]

Abstract: Quantifying above-cloud aerosol can help improve the assessment of aerosol intercontinental transport and climate impacts. In this study we conduct an integrated analysis of aerosols above clouds by using multi-sensor A-Train measurements, including above-cloud aerosol optical depth at 532nm (AOD532) from CALIPSO lidar, the UV aerosol index (AI) from OMI, and cloud fraction and cloud optical depth (COD) from MODIS. The analysis of Saharan dust outflow and Southwest African smoke outflow regions shows that the above-cloud AOD correlates positively with AI in an approximately linear manner, and that the AOD532/AI ratio decreases with increasing COD. The dependence of AOD532/AI ratio on COD doesn''t depend on aerosol type when potential biases in the CALIOP AOD measurements are empirically accounted for. Our results may suggest the potential of combining OMI AI and MODIS cloud measurements to empirically derive above-cloud AOD with a spatial coverage much more extensive than CALIPSO measurements, which needs to be further explored in the future. [Copyright &y& Elsevier]