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.

The Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP, pronounced the same as “calliope”) is a spaceborne two‐wavelength polarization lidar that has been acquiring global data since June 2006. CALIOP provides high resolution vertical profiles of clouds and aerosols, and has been designed with a very large linear dynamic range to encompass the full range of signal returns from aerosols and clouds. CALIOP is the primary instrument carried by the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, which was launched on April 28, 2006. CALIPSO was developed within the framework of a collaboration between NASA and the French space agency, CNES. Initial data analysis and validation intercomparisons indicate the quality of data from CALIOP meets or exceeds expectations. This paper presents a description of the CALIPSO mission, the CALIOP instrument, and an initial assessment of on‐orbit measurement performance.

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.

Current uncertainties in the effects of aerosols and clouds on the Earth radiation budget limit our understanding of the climate system and the potential for global climate change. The CALIPSO satellite will use an active lidar together with passive instruments to provide vertical profiles of aerosols and clouds and their properties which will help address these uncertainties. CALIPSO will fly in formation with the EOS Aqua and CloudSat satellites and the other satellites of the Aqua constellation. The acquisition of simultaneous and coincident observations will allow numerous synergies to be realized by combining CALIPSO observations with complementary observations from other platforms. In particular, cloud observations from the CALIPSO lidar and the CloudSat radar will be complementary, together encompassing the variety of clouds found in the atmosphere, from thin cirrus to deep convective clouds. CALIPSO is being developed within the framework of a collaboration between NASA and CNES and is scheduled for launch in 2004.

Atmospheric brown clouds are mostly the result of biomass burning and fossil fuel consumption. They consist of a mixture of light-absorbing and light-scattering aerosols and therefore contribute to atmospheric solar heating and surface cooling. The sum of the two climate forcing terms-the net aerosol forcing effect-is thought to be negative and may have masked as much as half of the global warming attributed to the recent rapid rise in greenhouse gases. There is, however, at least a fourfold uncertainty in the aerosol forcing effect. Atmospheric solar heating is a significant source of the uncertainty, because current estimates are largely derived from model studies. Here we use three lightweight unmanned aerial vehicles that were vertically stacked between 0.5 and 3 km over the polluted Indian Ocean. These unmanned aerial vehicles deployed miniaturized instruments measuring aerosol concentrations, soot amount and solar fluxes. During 18 flight missions the three unmanned aerial vehicles were flown with a horizontal separation of tens of metres or less and a temporal separation of less than ten seconds, which made it possible to measure the atmospheric solar heating rates directly. We found that atmospheric brown clouds enhanced lower atmospheric solar heating by about 50 per cent. Our general circulation model simulations, which take into account the recently observed widespread occurrence of vertically extended atmospheric brown clouds over the Indian Ocean and Asia, suggest that atmospheric brown clouds contribute as much as the recent increase in anthropogenic greenhouse gases to regional lower atmospheric warming trends. We propose that the combined warming trend of 0.25 K per decade may be sufficient to account for the observed retreat of the Himalayan glaciers.

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.

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.

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.