Aerosols and clouds play important roles in Earth's climate system but uncertainties over their interactions and their
effects on the Earth energy budget limit our understanding of the climate system and our ability to model it. The
CALIPSO satellite was developed to provide new capabilities to observe aerosol and cloud from space and to reduce
these uncertainties. CALIPSO carries the first polarization-sensitive lidar to fly in space, which has now provided a
four-year record of global aerosol and cloud profiles. This paper briefly summarizes the status of the CALIPSO mission,
describes some of the results from CALIPSO, and presents highlights of recent improvements in data products.
The CALIPSO Level II data are analyzed to assess the veracity of the CALIPSO aerosol type identification algorithm and generate distributions of aerosol types and their respective optical
characteristics. The distributions show that the classification algorithm has no surface type or diurnal dependencies. For this initial assessment of algorithm performance, we analyze global distributions of the CALIPSO aerosol types, along with distributions of integrated attenuated backscatter, backscatter color ratio, and volume depolarization ratio for each type. The aerosol type distributions are further partitioned according to various geophysical discriminators (e.g., geographic region, land
vs. ocean, and day vs. night). The algorithm generates the expected results in most scenes. The total color ratio distributions show significant overlap between the aerosol types. Since the aerosol typing algorithm uses a logical decision tree based on fixed thresholds, we test the sensitivity of the typing algorithm to perturbations in these threshold values. To test the CALIPSO extinction to backscatter ratio estimates, we compare
extinction-to-backscatter ratios derived using the transmittance method
to the values in the look up tables.
Satellite lidars are now beginning to provide new capabilities for global atmospheric sensing from space.
Following the Lidar In-space Technology Experiment (LITE), which flew on the Space Shuttle in 1994,
and the Geoscience Laser Altimeter System (GLAS), which launched in 2003, the CALIPSO satellite was
launched on April 28, 2006. Carrying a two-wavelength polarization lidar along with two passive imagers,
CALIPSO is now providing unique measurements to improve our understanding of the role of aerosols and
clouds in the Earth's climate system. The primary instrument on CALIPSO is CALIOP (Cloud-Aerosol
LIdar with Orthogonal Polarization), a two-wavelength polarization lidar. Using a linearly polarized laser
and a polarization-sensitive receiver, the instrument allows the discrimination of cloud ice/water phase and
the identification of non-spherical aerosols. First light was achieved in June, 2006 and five months of
nearly continuous observations have now been acquired. Initial performance assessments and calibration
activities have been performed and instrument performance appears to be excellent. CALIPSO was
developed within the framework of a collaboration between NASA and CNES.
The extinction-to-backscatter ratio (S<sub>a</sub>) is an important parameter used in the determination of the aerosol extinction and subsequently the optical depth from lidar backscatter measurements. We outline the algorithm used to determine S<sub>a</sub> for the Cloud and Aerosol Lidar and Infrared Pathfinder Spaceborne Observations (CALIPSO) lidar. S<sub>a</sub> for the CALIPSO lidar will either be selected from a look-up table or calculated using the lidar measurements depending on the characteristics of aerosol layer. Whenever suitable lofted layers are encountered, S<sub>a</sub> is computed directly from the integrated backscatter and transmittance. In all other cases, the CALIPSO observables: the depolarization ratio, δ, the layer integrated attenuated backscatter, β', and the mean layer total attenuated color ratio, γ, together with the surface type, are used to aid in aerosol typing. Once the type is identified, a look-up-table developed primarily from worldwide observations, is used to determine the S<sub>a</sub> value. The CALIPSO aerosol models include desert dust, biomass burning, background, polluted continental, polluted dust, and marine aerosols.
Current uncertainties in the role of aerosols and clouds in the Earth's climate system limit our abilities to model the climate system and predict climate change. These limitations are due primarily to difficulties of adequately measuring aerosols and clouds on a global scale. The A-train satellites (Aqua, CALIPSO, CloudSat, PARASOL, and Aura) will provide an unprecedented opportunity to address these uncertainties. The various active and passive sensors of the A-train will use a variety of measurement techniques to provide comprehensive observations of the multi-dimensional properties of clouds and aerosols. However, to fully achieve the potential of this ensemble requires a robust data analysis framework to optimally and efficiently map these individual measurements into a comprehensive set of cloud and aerosol physical properties. In this work we introduce the Multi-Instrument Data Analysis and Synthesis (MIDAS) project, whose goal is to develop a suite of physically sound and computationally efficient algorithms that will combine active and passive remote sensing data in order to produce improved assessments of aerosol and cloud radiative and microphysical properties. These algorithms include (a) the development of an intelligent feature detection algorithm that combines inputs from both active and passive sensors, and (b) identifying recognizable multi-instrument signatures related to aerosol and cloud type derived from clusters of image pixels and the associated vertical profile information. Classification of these signatures will lead to the automated identification of aerosol and cloud types. Testing of these new algorithms is done using currently existing and readily available active and passive measurements from the Cloud Physics Lidar and the MODIS Airborne Simulator, which simulate, respectively, the CALIPSO and MODIS A-train instruments.
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite will be launched in April of 2005, and will make continuous measurements of the Earth's atmosphere for the following three years. Retrieving the spatial and optical properties of clouds and aerosols from the CALIPSO lidar backscatter data will be confronted by a number of difficulties that are not faced in the analysis of ground-based data. Among these are the very large distance from the target, the high speed at which the satellite traverses the ground track, and the ensuing low signal-to-noise ratios that result from the mass and power restrictions imposed on space-based platforms. In this work we describe an integrated analysis scheme that employs a nested, multi-grid averaging technique designed to optimize tradeoffs between spatial resolution and signal-to-noise ratio. We present an overview of the three fundamental retrieval algorithms (boundary location, feature classification, and optical properties analysis), and illustrate their interconnections using data product examples that include feature top and base altitudes, feature type (i.e., cloud or aerosol), and layer optical depths.
Accurate estimation of cloud and aerosol optical depths using backscatter lidar data requires knowledge of the particulate <i>lidar ratio</i> (i.e., the extinction-to-backscatter ratio). In those cases for which a measurement of molecular backscatter can be made on the far side of a layer, knowledge of the lidar ratio can be derived directly from the data. However, obtaining a reliable clear air constraint is a function of layer optical depth, system sensitivity and overall signal-to-noise ratio (SNR). To date, the design constraints imposed on space-based lidars such as LITE and CALIPSO have rendered the use of this retrieval technique virtually impossible for measurements made at 1064 nm. Layers to which the constraint method can be successfully applied are assumed to be homogeneous with respect to particle composition and size distribution, and therefore are characterized by lidar ratios that are range-invariant throughout the layer. By extending this assumption of homogeneity to include the layer backscatter color ratio, this work derives a new technique that simultaneously retrieves both the color ratio and the 1064 nm lidar ratio from two wavelength elastic backscatter lidar measurements of transmissive clouds and/or lofted aerosol layers. Retrieval examples are illustrated using data obtained from LITE. Initial error estimates derived from numerical experiments using simulated data show the retrieval of the backscatter color ratio to be stable, even in the presence of considerable noise in the data.
A method of obtaining the reflected solar radiance from clouds with space lidar is described. The lidar telescope and detector are used effectively together as a visible radiometer at the lidar wavelength, the background signal on a lidar backscatter profile being proportional to the observed radiance in the lidar field of view. A DC-coupled output from the telescope detector is required for this method. The RMS background noise signal is also proportional to the observed radiance, where a DC-coupled detector is still effective. However, an AC-coupled detector could also be used. Both the methods are used here to measure the relative radiance along a part of one orbit of the Lidar In-Space Technology Experiment (LITE) on Space Shuttle Discovery. This orbit crossed over Typhoon Melissa where the cloud was optically thick and the reflectance at 532 nm was estimated by normalization at maximum values to previously observed GMS satellite values over similar clouds. Retrieval of the radiance from the internal lidar parameters is also being investigated. Some profiles of extinction coefficient below cloud top near the center of Melissa were also retrieved, showing an increase in extinction below cloud top to at least a depth of 1 km.
A review of the language and parameters that define subvisual cirrus is provided, including a literature review and classic examples of observations. Complementary measurement techniques are discussed, including lidar, solar aureole measurements, photographic techniques, and the use of radiometric observations. We address the microphysical and optical properties of these high, thin clouds, along with suggested dynamic sources. Five classes of subvisual cirrus clouds are suggested. Complementary subvisual cirrus observations recorded during the 1986 and 1991 FIRE (First ISSCP Regional Experiment) intensive field operations and other field programs support these classes. Detection of subvisual cirrus via satellite imagery is vital and a limiting case for discrimination or threshold methods. The lidar, satellite, rawinsonde, and meteorological conditions are used to derive a likely picture of the atmospheric conditions required to produce the subvisual cirrus class observed.