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- Author or Editor: Mark A. Vaughan x
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Abstract
This work describes the algorithms used for the fully automated retrieval of profiles of particulate extinction coefficients from the attenuated backscatter data acquired by the lidar on board the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. The close interaction of the Hybrid Extinction Retrieval Algorithms (HERA) with the preceding processes that detect and classify atmospheric features (i.e., cloud and aerosol layers) is described within the context of the analysis of measurements from scenes of varying complexity. Two main components compose HERA: a top-level algorithm that selects the analysis pathway, the order of processing, and the analysis parameters, depending on the nature and spatial extent of the atmospheric features to be processed; and a profile solver or “extinction engine,” whose task it is to retrieve profiles of particulate extinction and backscatter coefficients from specified sections of an atmospheric scene defined by the top-level algorithm. The operation of these components is described using synthetic data derived from Lidar In Space Technology Experiment (LITE) measurements. The performance of the algorithms is illustrated using CALIPSO measurements acquired during the mission on 1 January 2007.
Abstract
This work describes the algorithms used for the fully automated retrieval of profiles of particulate extinction coefficients from the attenuated backscatter data acquired by the lidar on board the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. The close interaction of the Hybrid Extinction Retrieval Algorithms (HERA) with the preceding processes that detect and classify atmospheric features (i.e., cloud and aerosol layers) is described within the context of the analysis of measurements from scenes of varying complexity. Two main components compose HERA: a top-level algorithm that selects the analysis pathway, the order of processing, and the analysis parameters, depending on the nature and spatial extent of the atmospheric features to be processed; and a profile solver or “extinction engine,” whose task it is to retrieve profiles of particulate extinction and backscatter coefficients from specified sections of an atmospheric scene defined by the top-level algorithm. The operation of these components is described using synthetic data derived from Lidar In Space Technology Experiment (LITE) measurements. The performance of the algorithms is illustrated using CALIPSO measurements acquired during the mission on 1 January 2007.
Abstract
Profiles of atmospheric cloud and aerosol extinction coefficients are retrieved on a global scale from measurements made by the lidar on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission since mid-June 2006. This paper presents an analysis of how the uncertainties in the inputs to the extinction retrieval algorithm propagate as the retrieval proceeds downward to lower levels of the atmosphere. The mathematical analyses, which are being used to calculate the uncertainties reported in the current (version 3) data release, are supported by figures illustrating the retrieval uncertainties in both simulated and actual data. Equations are also derived that describe the sensitivity of the extinction retrieval algorithm to errors in profile calibration and in the lidar ratios used in the retrievals. Biases that could potentially result from low signal-to-noise ratios in the data are also examined. Using simulated data, the propagation of bias errors resulting from errors in profile calibration and lidar ratios is illustrated.
Abstract
Profiles of atmospheric cloud and aerosol extinction coefficients are retrieved on a global scale from measurements made by the lidar on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission since mid-June 2006. This paper presents an analysis of how the uncertainties in the inputs to the extinction retrieval algorithm propagate as the retrieval proceeds downward to lower levels of the atmosphere. The mathematical analyses, which are being used to calculate the uncertainties reported in the current (version 3) data release, are supported by figures illustrating the retrieval uncertainties in both simulated and actual data. Equations are also derived that describe the sensitivity of the extinction retrieval algorithm to errors in profile calibration and in the lidar ratios used in the retrievals. Biases that could potentially result from low signal-to-noise ratios in the data are also examined. Using simulated data, the propagation of bias errors resulting from errors in profile calibration and lidar ratios is illustrated.
CorrigendumAbstract
An error in a recent analysis of the sensitivity of retrievals of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) particulate optical properties to errors in various input parameters is described. This error was in the specification of an intermediate variable that was used to write a general equation for the sensitivities to errors in either the renormalization (calibration) factor or in the lidar ratio used in the retrieval, or both. The result of this incorrect substitution (an additional multiplicative factor to the exponent of the particulate transmittance) was then copied to some intermediate equations; the corrected versions of which are presented here. Fortunately, however, all of the final equations for the specific cases of renormalization and lidar ratio errors are correct, as are all of the figures and approximations, because these were derived directly from equations for the specific errors and not from the equation for the general case. All of the other sections, including the uncertainty analyses and the analyses of sensitivities to low signal-to-noise ratios and errors in constrained retrievals, and the presentations of errors and uncertainties in simulated and actual data are unaffected.
Abstract
An error in a recent analysis of the sensitivity of retrievals of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) particulate optical properties to errors in various input parameters is described. This error was in the specification of an intermediate variable that was used to write a general equation for the sensitivities to errors in either the renormalization (calibration) factor or in the lidar ratio used in the retrieval, or both. The result of this incorrect substitution (an additional multiplicative factor to the exponent of the particulate transmittance) was then copied to some intermediate equations; the corrected versions of which are presented here. Fortunately, however, all of the final equations for the specific cases of renormalization and lidar ratio errors are correct, as are all of the figures and approximations, because these were derived directly from equations for the specific errors and not from the equation for the general case. All of the other sections, including the uncertainty analyses and the analyses of sensitivities to low signal-to-noise ratios and errors in constrained retrievals, and the presentations of errors and uncertainties in simulated and actual data are unaffected.
Abstract
This paper provides background material for a collection of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) algorithm papers that are to be published in the Journal of Atmospheric and Oceanic Technology. It provides a brief description of the design and performance of CALIOP, a three-channel elastic backscatter lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After more than 2 yr of on-orbit operation, CALIOP performance continues to be excellent in the key areas of laser energy, signal-to-noise ratio, polarization sensitivity, and overall long-term stability, and the instrument continues to produce high-quality data products. There are, however, some areas where performance has been less than ideal. These include short-term changes in the calibration coefficients at both wavelengths as the satellite passes between dark and sunlight, some radiation-induced effects on both the detectors and the laser when passing through the South Atlantic Anomaly, and slow transient recovery on the 532-nm channels. Although these issues require some special treatment in data analysis, they do not seriously detract from the overall quality of the level 2 data products.
Abstract
This paper provides background material for a collection of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) algorithm papers that are to be published in the Journal of Atmospheric and Oceanic Technology. It provides a brief description of the design and performance of CALIOP, a three-channel elastic backscatter lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After more than 2 yr of on-orbit operation, CALIOP performance continues to be excellent in the key areas of laser energy, signal-to-noise ratio, polarization sensitivity, and overall long-term stability, and the instrument continues to produce high-quality data products. There are, however, some areas where performance has been less than ideal. These include short-term changes in the calibration coefficients at both wavelengths as the satellite passes between dark and sunlight, some radiation-induced effects on both the detectors and the laser when passing through the South Atlantic Anomaly, and slow transient recovery on the 532-nm channels. Although these issues require some special treatment in data analysis, they do not seriously detract from the overall quality of the level 2 data products.
Abstract
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
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
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
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
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.
Abstract
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.
Abstract
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.
Abstract
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.
This paper examines the potential of regional environmental prediction by focusing on the local forecasting effort in the Pacific Northwest. A consortium of federal, state, and local agencies have funded the development and operation of a multifaceted numerical prediction system centered at the University of Washington that includes atmospheric, hydrologic, and air quality models, the collection of real-time regional weather data sources, and a number of real-time applications using both observations and model output. The manuscript reviews northwest modeling and data collection systems, describes the funding and management system established to support and guide the effort, provides some examples of regional real-time applications, and examines the national implications of regional environmental prediction.
This paper examines the potential of regional environmental prediction by focusing on the local forecasting effort in the Pacific Northwest. A consortium of federal, state, and local agencies have funded the development and operation of a multifaceted numerical prediction system centered at the University of Washington that includes atmospheric, hydrologic, and air quality models, the collection of real-time regional weather data sources, and a number of real-time applications using both observations and model output. The manuscript reviews northwest modeling and data collection systems, describes the funding and management system established to support and guide the effort, provides some examples of regional real-time applications, and examines the national implications of regional environmental prediction.
