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Atmospheric lidar observations provide a unique capability to directly observe the vertical column of cloud and aerosol scattering properties. Detector and solar background noise, however, hinder the ability of lidar systems to provide reliable backscatter and extinction cross-section estimates. Standard methods for solving this inverse problem are most effective with high signal-to-noise ratio observations that are only available at low-resolution in uniform scenes. This paper describes a novel method for solving the inverse problem with high-resolution, lower signal-to-noise ratio observations that are effective in non-uniform scenes. The novelty is twofold. First, the inference of the backscatter and extinction are done on images, whereas current lidar algorithms only use the information content of single profiles. Hence, the latent spatial and temporal information in the noisy images are utilized to infer the cross-sections. Second, the noise associated with photon-counting lidar observations can be modeled using a Poisson distribution, and state-of-the-art tools for solving Poisson inverse problems are adapted to the atmospheric lidar problem. It is demonstrated through photon-counting High Spectral Resolution Lidar (HSRL) simulations that the proposed algorithm yield inverted backscatter and extinction cross-sections (per unit volume) with smaller Mean Squared Error (MSE) values at higher spatial and temporal resolutions, compared to the standard approach. Two case studies of real experimental data are also provided where the proposed algorithm is applied on HSRL observations and the inverted backscatter and extinction cross-sections are compared against the standard approach., Comment: 19 pages, 14 figures

After launch we discovered that the CALIOP daytime measurements were subject to thermally induced beamdrift,and this caused the calibration to vary by as much as 30% during the course of a single daytime orbit segment. Using an algorithm developed by Powell et al.(2010), empirically derived correction factors are now computed in near realtime as a function of orbit elapsed time, and these are used to compensate for the beam wandering effects.

In the spring of 2010, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project will be releasing version 3 of its level 2 data products. In this paper we describe several changes to the algorithms and code that yield substantial improvements in CALIPSO's retrieval of aerosol optical depths (AOD). Among these are a retooled cloud-clearing procedure and a new approach to determining the base altitudes of aerosol layers in the planetary boundary layer (PBL). The results derived from these modifications are illustrated using case studies prepared using a late beta version of the level 2 version 3 processing code.

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 CALIPSO lidar (CALIOP) makes backscatter measurements at 532 nm and 1064 nm and linear depolarization ratios at 532 nm. Accurate calibration of the backscatter measurements is essential in the retrieval of optical properties. An assessment of the nighttime 532 nm parallel channel calibration showed that the calibration strategy used for the initial release (Release 1) of the CALIOP lidar level 1B data was acceptable. In general, the nighttime calibration coefficients are relatively constant over the darkest segment of the orbit, but then change rapidly over a short period as the satellite enters sunlight. The daytime 532 nm parallel channel calibration scheme implemented in Release 1 derived the daytime calibration coefficients from the previous nighttime coefficients. A subsequent review of the daytime 532 nm parallel channel calibration revealed that the daytime calibration coefficients do not remain constant, but vary considerably over the course of the orbit, due to thermally-induced misalignment of the transmitter and receiver. A correction to the daytime calibration scheme is applied in Release 2 of the data. Results of both nighttime and daytime calibration performance are presented in this paper.