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Abstract
A lower bound on the uncertainty in observational estimates of the aerosol direct radiative effect (DRE; the direct interaction with solar radiation by all aerosols) and the aerosol direct radiative forcing [DRF; the radiative effect of just anthropogenic aerosols (RFari)] is quantified by making the optimistic assumption that global aerosol observations can be made with the accuracy found in the Aerosol Robotic Network (AERONET) sun photometer retrievals. The global-mean all-sky aerosol DRE uncertainty was found to be 1.1 W m−2 (one standard deviation). The global-mean all-sky aerosol DRF (RFari) uncertainty was determined to be 0.31 W m−2. The total uncertainty in both quantities is dominated by contributions from the aerosol single scattering albedo uncertainty. These uncertainty estimates were compared to a literature survey of mostly satellite-based aerosol DRE/DRF values. Comparisons to previous studies reveal that most have significantly underestimated the aerosol DRE uncertainty. Past estimates of the aerosol DRF uncertainty are smaller (on average) than our optimistic observational estimates, including the aerosol DRF uncertainty given in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This disconnect between our observation-based uncertainty and that found in past aerosol DRF studies that rely, at least in part, on modeling is discussed. Also quantified is a potential reduction in the current observational uncertainty possible with a future generation of satellite observations that would leverage aerosol typing and more refined vertical information.
Abstract
A lower bound on the uncertainty in observational estimates of the aerosol direct radiative effect (DRE; the direct interaction with solar radiation by all aerosols) and the aerosol direct radiative forcing [DRF; the radiative effect of just anthropogenic aerosols (RFari)] is quantified by making the optimistic assumption that global aerosol observations can be made with the accuracy found in the Aerosol Robotic Network (AERONET) sun photometer retrievals. The global-mean all-sky aerosol DRE uncertainty was found to be 1.1 W m−2 (one standard deviation). The global-mean all-sky aerosol DRF (RFari) uncertainty was determined to be 0.31 W m−2. The total uncertainty in both quantities is dominated by contributions from the aerosol single scattering albedo uncertainty. These uncertainty estimates were compared to a literature survey of mostly satellite-based aerosol DRE/DRF values. Comparisons to previous studies reveal that most have significantly underestimated the aerosol DRE uncertainty. Past estimates of the aerosol DRF uncertainty are smaller (on average) than our optimistic observational estimates, including the aerosol DRF uncertainty given in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This disconnect between our observation-based uncertainty and that found in past aerosol DRF studies that rely, at least in part, on modeling is discussed. Also quantified is a potential reduction in the current observational uncertainty possible with a future generation of satellite observations that would leverage aerosol typing and more refined vertical information.
Abstract
Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel-scale variations in cloud-top height (i.e., “cloud bumps”). Monte Carlo simulations comparing 1D model radiances to radiances from overcast cloud fields with 1) cloud-top height variations but constant cloud volume extinction, 2) flat tops but horizontal variations in cloud volume extinction, and 3) variations in both cloud-top height and cloud extinction are performed over a ≈4 km × 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance differences. The reason 3D–1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.
Abstract
Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel-scale variations in cloud-top height (i.e., “cloud bumps”). Monte Carlo simulations comparing 1D model radiances to radiances from overcast cloud fields with 1) cloud-top height variations but constant cloud volume extinction, 2) flat tops but horizontal variations in cloud volume extinction, and 3) variations in both cloud-top height and cloud extinction are performed over a ≈4 km × 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance differences. The reason 3D–1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.
Abstract
Tropospheric aerosols are calculated to cause global-scale changes in the earth's heat balance, but these forcings are space/time integrals over highly variable quantities. Accurate quantification of these forcings will require an unprecedented synergy among satellite, airborne, and surface-based observations, as well as models. This study considers one aspect of achieving this synergy—the need to treat aerosol variability in a consistent and realistic way. This need creates a requirement to rationalize the differences in spatiotemporal resolution and coverage among the various observational and modeling approaches. It is shown, based on aerosol optical data from diverse regions, that mesoscale variability (specifically, for horizontal scales of 40–400 km and temporal scales of 2–48 h) is a common and perhaps universal feature of lower-tropospheric aerosol light extinction. Such variation is below the traditional synoptic or “airmass” scale (where the aerosol is often assumed to be essentially homogeneous except for plumes from point sources) and below the scales that are readily resolved by chemical transport models. The present study focuses on documenting this variability. Possible physical causes and practical implications for coordinated observational strategies are also discussed.
Abstract
Tropospheric aerosols are calculated to cause global-scale changes in the earth's heat balance, but these forcings are space/time integrals over highly variable quantities. Accurate quantification of these forcings will require an unprecedented synergy among satellite, airborne, and surface-based observations, as well as models. This study considers one aspect of achieving this synergy—the need to treat aerosol variability in a consistent and realistic way. This need creates a requirement to rationalize the differences in spatiotemporal resolution and coverage among the various observational and modeling approaches. It is shown, based on aerosol optical data from diverse regions, that mesoscale variability (specifically, for horizontal scales of 40–400 km and temporal scales of 2–48 h) is a common and perhaps universal feature of lower-tropospheric aerosol light extinction. Such variation is below the traditional synoptic or “airmass” scale (where the aerosol is often assumed to be essentially homogeneous except for plumes from point sources) and below the scales that are readily resolved by chemical transport models. The present study focuses on documenting this variability. Possible physical causes and practical implications for coordinated observational strategies are also discussed.
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
Both to reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE) and to optimize the design of future observing systems, this study builds a framework for assessing aerosol DRE uncertainty. Shortwave aerosol DRE radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels give the differential response of the aerosol DRE to perturbations in the aerosol extinction coefficient, aerosol single-scattering albedo, aerosol asymmetry factor, surface albedo, cloud fraction, and cloud optical depth. This comprehensive set of kernels provides a convenient way to consistently and accurately assess the aerosol DRE uncertainties that result from observational or model-based uncertainties. The aerosol DRE kernels were used to test the effect of simplifying the full vertical profile of aerosol scattering properties into column-integrated quantities. This analysis showed that, although the clear-sky aerosol DRE can be had fairly accurately, more significant errors occur for the all-sky DRE. The sensitivity in determining the broadband spectral dependencies of the aerosol scattering properties directly from a limited set of wavelengths was quantified. These spectral dependencies can be reasonably constrained using column-integrated aerosol scattering properties in the midvisible and near-infrared wavelengths. Separating the aerosol DRE and its kernels by scene type shows that accurate aerosol properties in the clear sky are the most crucial component of the global aerosol DRE. In cloudy skies, determining aerosol properties in the presence of optically thin cloud is more radiatively important than doing so when optically thick cloud is present.
Abstract
Both to reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE) and to optimize the design of future observing systems, this study builds a framework for assessing aerosol DRE uncertainty. Shortwave aerosol DRE radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels give the differential response of the aerosol DRE to perturbations in the aerosol extinction coefficient, aerosol single-scattering albedo, aerosol asymmetry factor, surface albedo, cloud fraction, and cloud optical depth. This comprehensive set of kernels provides a convenient way to consistently and accurately assess the aerosol DRE uncertainties that result from observational or model-based uncertainties. The aerosol DRE kernels were used to test the effect of simplifying the full vertical profile of aerosol scattering properties into column-integrated quantities. This analysis showed that, although the clear-sky aerosol DRE can be had fairly accurately, more significant errors occur for the all-sky DRE. The sensitivity in determining the broadband spectral dependencies of the aerosol scattering properties directly from a limited set of wavelengths was quantified. These spectral dependencies can be reasonably constrained using column-integrated aerosol scattering properties in the midvisible and near-infrared wavelengths. Separating the aerosol DRE and its kernels by scene type shows that accurate aerosol properties in the clear sky are the most crucial component of the global aerosol DRE. In cloudy skies, determining aerosol properties in the presence of optically thin cloud is more radiatively important than doing so when optically thick cloud is present.
Abstract
Reliably determining low-cloud heights using a cloud-top temperature from satellite infrared imagery is often challenging because of difficulties in characterizing the local thermal structure of the lower troposphere with the necessary precision and accuracy. To improve low-cloud-top height estimates over water surfaces, various methods have employed lapse rates anchored to the sea surface temperature to replace the boundary layer temperature profiles that relate temperature to altitude. To further improve low-cloud-top height retrievals, collocated Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data taken from July 2006 to June 2007 and from June 2009 to May 2010 (2 yr) for single-layer low clouds are used here with numerical weather model analyses to develop regional mean boundary apparent lapse rates. These parameters are designated as apparent lapse rates because they are defined using the cloud-top temperatures from satellite retrievals and surface skin temperatures; they do not represent true lapse rates. Separate day and night, seasonal mean lapse rates are determined for 10′-resolution snow-free land, water, and coastal regions, while zonally dependent lapse rates are developed for snow/ice-covered areas for use in the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 cloud property retrieval system (CCPRS-4). The derived apparent lapse rates over ice-free water range from 5 to 9 K km−1 with mean values of about 6.9 and 7.2 K km−1 during the day and night, respectively. Over land, the regional values vary from 3 to 8 K km−1, with day and night means of 5.5 and 6.2 K km−1, respectively. The zonal-mean apparent lapse rates over snow and ice surfaces generally decrease with increasing latitude, ranging from 4 to 8 K km−1. All of the CCPRS-4 lapse rates were used along with five other lapse rate techniques to retrieve cloud-top heights for 2 months of independent Aqua MODIS data. When compared with coincident CALIPSO data for October 2007, the mean cloud-top height differences between CCPRS-4 and CALIPSO during the daytime (nighttime) are 0.04 ± 0.61 km (0.10 ± 0.62 km) over ice-free water, −0.06 ± 0.85 km (−0.01 ± 0.83 km) over snow-free land, and 0.38 ± 0.95 km (0.03 ± 0.92 km) over snow-covered areas. The CCPRS-4 regional monthly means are generally unbiased and lack spatial error gradients seen in the comparisons for most of the other techniques. Over snow-free land, the regional monthly-mean errors range from −0.28 ± 0.74 km during daytime to 0.04 ± 0.78 km at night. The water regional monthly means are, on average, 0.04 ± 0.44 km less than the CALIPSO values during day and night. Greater errors are realized for snow-covered regions. Overall, the CCPRS-4 lapse rates yield the smallest RMS differences for all times of day over all areas both for individual retrievals and monthly means. These new regional apparent lapse rates, used in processing CERES Edition 4 data, should provide more accurate low-cloud-type heights than previously possible using satellite imager data.
Abstract
Reliably determining low-cloud heights using a cloud-top temperature from satellite infrared imagery is often challenging because of difficulties in characterizing the local thermal structure of the lower troposphere with the necessary precision and accuracy. To improve low-cloud-top height estimates over water surfaces, various methods have employed lapse rates anchored to the sea surface temperature to replace the boundary layer temperature profiles that relate temperature to altitude. To further improve low-cloud-top height retrievals, collocated Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data taken from July 2006 to June 2007 and from June 2009 to May 2010 (2 yr) for single-layer low clouds are used here with numerical weather model analyses to develop regional mean boundary apparent lapse rates. These parameters are designated as apparent lapse rates because they are defined using the cloud-top temperatures from satellite retrievals and surface skin temperatures; they do not represent true lapse rates. Separate day and night, seasonal mean lapse rates are determined for 10′-resolution snow-free land, water, and coastal regions, while zonally dependent lapse rates are developed for snow/ice-covered areas for use in the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 cloud property retrieval system (CCPRS-4). The derived apparent lapse rates over ice-free water range from 5 to 9 K km−1 with mean values of about 6.9 and 7.2 K km−1 during the day and night, respectively. Over land, the regional values vary from 3 to 8 K km−1, with day and night means of 5.5 and 6.2 K km−1, respectively. The zonal-mean apparent lapse rates over snow and ice surfaces generally decrease with increasing latitude, ranging from 4 to 8 K km−1. All of the CCPRS-4 lapse rates were used along with five other lapse rate techniques to retrieve cloud-top heights for 2 months of independent Aqua MODIS data. When compared with coincident CALIPSO data for October 2007, the mean cloud-top height differences between CCPRS-4 and CALIPSO during the daytime (nighttime) are 0.04 ± 0.61 km (0.10 ± 0.62 km) over ice-free water, −0.06 ± 0.85 km (−0.01 ± 0.83 km) over snow-free land, and 0.38 ± 0.95 km (0.03 ± 0.92 km) over snow-covered areas. The CCPRS-4 regional monthly means are generally unbiased and lack spatial error gradients seen in the comparisons for most of the other techniques. Over snow-free land, the regional monthly-mean errors range from −0.28 ± 0.74 km during daytime to 0.04 ± 0.78 km at night. The water regional monthly means are, on average, 0.04 ± 0.44 km less than the CALIPSO values during day and night. Greater errors are realized for snow-covered regions. Overall, the CCPRS-4 lapse rates yield the smallest RMS differences for all times of day over all areas both for individual retrievals and monthly means. These new regional apparent lapse rates, used in processing CERES Edition 4 data, should provide more accurate low-cloud-type heights than previously possible using satellite imager data.
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.
