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1. Introduction The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite, was launched in April 2006 ( Winker et al. 2007 ), in formation with the CloudSat satellite, as part of the A-Train constellation of satellites ( Stephens et al. 2002 ). The main objectives of the CALIPSO mission are to provide global measurements of cloud and aerosol spatial distributions and optical properties
1. Introduction The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite, was launched in April 2006 ( Winker et al. 2007 ), in formation with the CloudSat satellite, as part of the A-Train constellation of satellites ( Stephens et al. 2002 ). The main objectives of the CALIPSO mission are to provide global measurements of cloud and aerosol spatial distributions and optical properties
boundary layer cloud is usually much larger than the signal from the nearby aerosol, these clouds can be separated from aerosol based on backscatter intensity alone. To enhance the cloud–aerosol contrast, the cloud detection is done using 1064-nm profiles. The classification of all other layers is performed by the cloud–aerosol discrimination (CAD) algorithm ( Liu et al. 2009 ). CAD classifications are based on statistical differences in the various optical and physical properties of cloud and aerosol
boundary layer cloud is usually much larger than the signal from the nearby aerosol, these clouds can be separated from aerosol based on backscatter intensity alone. To enhance the cloud–aerosol contrast, the cloud detection is done using 1064-nm profiles. The classification of all other layers is performed by the cloud–aerosol discrimination (CAD) algorithm ( Liu et al. 2009 ). CAD classifications are based on statistical differences in the various optical and physical properties of cloud and aerosol
polarization plane of the outgoing beam. Each component is measured separately using photomultiplier tubes (PMTs). Additional information about the CALIOP transmitter and receiver design and operation can be found in Hunt et al. (2009) . Accurate calibration of all three lidar signals is essential for layer detection and the subsequent retrieval of layer optical properties. Complete details of all CALIOP calibration algorithms are presented in Hostetler et al. (2006) . Essential aspects of the CALIOP
polarization plane of the outgoing beam. Each component is measured separately using photomultiplier tubes (PMTs). Additional information about the CALIOP transmitter and receiver design and operation can be found in Hunt et al. (2009) . Accurate calibration of all three lidar signals is essential for layer detection and the subsequent retrieval of layer optical properties. Complete details of all CALIOP calibration algorithms are presented in Hostetler et al. (2006) . Essential aspects of the CALIOP
)] have afforded global daily measurements of optical properties that are useful for aerosol classification. One of the challenges of satellite-based passive remote sensing of aerosol properties is the separation of the path radiance from the top-of-atmosphere (TOA) radiance. The path radiance is due to atmospheric reflection, whereas TOA radiance includes reflection by the surface. For aerosol retrieval from passive instruments, once the path radiance is isolated, some methods use simulations of the
)] have afforded global daily measurements of optical properties that are useful for aerosol classification. One of the challenges of satellite-based passive remote sensing of aerosol properties is the separation of the path radiance from the top-of-atmosphere (TOA) radiance. The path radiance is due to atmospheric reflection, whereas TOA radiance includes reflection by the surface. For aerosol retrieval from passive instruments, once the path radiance is isolated, some methods use simulations of the
atmospheric region. As the lidar equation is nonlinear, any attempt to retrieve profiles of particulate extinction from the averages of profiles containing features of grossly different intrinsic or extrinsic optical properties (and hence lidar ratios—ratios of the particulate volume extinction and backscatter coefficients) could produce results that would be unrepresentative of the real atmosphere. A unique feature of the CALIPSO lidar analysis is that it attempts to average signals in atmospheric
atmospheric region. As the lidar equation is nonlinear, any attempt to retrieve profiles of particulate extinction from the averages of profiles containing features of grossly different intrinsic or extrinsic optical properties (and hence lidar ratios—ratios of the particulate volume extinction and backscatter coefficients) could produce results that would be unrepresentative of the real atmosphere. A unique feature of the CALIPSO lidar analysis is that it attempts to average signals in atmospheric
.1175/1520-0450(1978)017<1220:SMPOAI>2.0.CO;2 Platt, C. M. R. , Scott J. C. , and Dilley A. C. , 1987 : Remote sounding of high clouds. Part VI: Optical properties of midlatitude and tropical cirrus. J. Atmos. Sci. , 44 , 729 – 747 . 10.1175/1520-0469(1987)044<0729:RSOHCP>2.0.CO;2 Platt, C. M. R. , Young S. A. , Manson P. J. , Patterson G. R. , Marsden S. C. , Austin R. T. , and Churnside J. , 1998 : The optical properties of equatorial cirrus from observations in the ARM Pilot Radiation Observation
.1175/1520-0450(1978)017<1220:SMPOAI>2.0.CO;2 Platt, C. M. R. , Scott J. C. , and Dilley A. C. , 1987 : Remote sounding of high clouds. Part VI: Optical properties of midlatitude and tropical cirrus. J. Atmos. Sci. , 44 , 729 – 747 . 10.1175/1520-0469(1987)044<0729:RSOHCP>2.0.CO;2 Platt, C. M. R. , Young S. A. , Manson P. J. , Patterson G. R. , Marsden S. C. , Austin R. T. , and Churnside J. , 1998 : The optical properties of equatorial cirrus from observations in the ARM Pilot Radiation Observation
reflections from the ice crystals, making identification of HOICs easy but making the measurement of cloud optical properties such as depolarization and optical depth difficult, if not impossible. If the lidar pointing direction is several degrees off nadir, then the specular reflections are avoided and cloud optical properties can be measured. A small off-nadir angle of 0.3° was chosen at the beginning of the mission, allowing statistics on HOIC clouds to be obtained. After acquiring more than a year of
reflections from the ice crystals, making identification of HOICs easy but making the measurement of cloud optical properties such as depolarization and optical depth difficult, if not impossible. If the lidar pointing direction is several degrees off nadir, then the specular reflections are avoided and cloud optical properties can be measured. A small off-nadir angle of 0.3° was chosen at the beginning of the mission, allowing statistics on HOIC clouds to be obtained. After acquiring more than a year of
-km resolution. The spatial and optical properties for features detected at 1 km are recorded in the data products. However, the feature-removal and attenuation-correction steps so critical to the performance of the MRLD scheme are not implemented for the 1-km search, as they are not required within the context of the HRCCL analyses. If layers with tops below ∼8.3 km are detected at 1 km, a final scan is conducted at the highest horizontal resolution (⅓ km) of the downlinked data. Once again, the
-km resolution. The spatial and optical properties for features detected at 1 km are recorded in the data products. However, the feature-removal and attenuation-correction steps so critical to the performance of the MRLD scheme are not implemented for the 1-km search, as they are not required within the context of the HRCCL analyses. If layers with tops below ∼8.3 km are detected at 1 km, a final scan is conducted at the highest horizontal resolution (⅓ km) of the downlinked data. Once again, the
