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- Author or Editor: Matthew J. McGill x
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Abstract
Accurate knowledge of cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, can have a significant impact on the quality of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in nonideal conditions to determine cloud phase and optical depth. Statistics and trends of these optical parameters are analyzed for 4 yr (2003–07) of cloud physics lidar data during five projects that occurred in varying geographic locations and meteorological seasons. Extinction-to-backscatter ratios (also called lidar ratios) are derived at 532 nm by calculating the transmission loss through the cloud layer and then applying it to the attenuated backscatter profile in the layer, while volume depolarization ratios are computed using the ratio of the parallel and perpendicular polarized 1064-nm channels. The majority of the cloud layers yields a lidar ratio between 10 and 40 sr, with the lidar ratio frequency distribution centered at 25 sr for ice clouds and 16 sr for altocumulus clouds. On average, for ice clouds the lidar ratio slightly decreases with decreasing temperature, while the volume depolarization ratio increases significantly as temperatures decrease. Trends for liquid water clouds (altocumulus clouds) are also observed. Ultimately, these observed trends in optical properties, as functions of temperature and geographic location, should help to improve current parameterizations of extinction-to-backscatter ratio, which in turn should yield increased accuracy in cloud optical depth and radiative forcing estimates.
Abstract
Accurate knowledge of cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, can have a significant impact on the quality of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in nonideal conditions to determine cloud phase and optical depth. Statistics and trends of these optical parameters are analyzed for 4 yr (2003–07) of cloud physics lidar data during five projects that occurred in varying geographic locations and meteorological seasons. Extinction-to-backscatter ratios (also called lidar ratios) are derived at 532 nm by calculating the transmission loss through the cloud layer and then applying it to the attenuated backscatter profile in the layer, while volume depolarization ratios are computed using the ratio of the parallel and perpendicular polarized 1064-nm channels. The majority of the cloud layers yields a lidar ratio between 10 and 40 sr, with the lidar ratio frequency distribution centered at 25 sr for ice clouds and 16 sr for altocumulus clouds. On average, for ice clouds the lidar ratio slightly decreases with decreasing temperature, while the volume depolarization ratio increases significantly as temperatures decrease. Trends for liquid water clouds (altocumulus clouds) are also observed. Ultimately, these observed trends in optical properties, as functions of temperature and geographic location, should help to improve current parameterizations of extinction-to-backscatter ratio, which in turn should yield increased accuracy in cloud optical depth and radiative forcing estimates.
Abstract
The effective radius (re ) is a crucial variable in representing the radiative properties of cloud layers in general circulation models. This parameter is proportional to the condensed water content (CWC) divided by the extinction (σ). For ice cloud layers, parameterizations for re have been developed from aircraft in situ measurements 1) indirectly, using data obtained from particle spectrometer probes and assumptions or observations about particle shape and mass to get the ice water content (IWC) and area to get σ, and recently 2) from probes that derive IWC and σ more directly, referred to as the direct approach, even though the extinction is not measured directly.
This study compares [IWC/σ] derived from the two methods using datasets acquired from comparable instruments on two aircraft, one sampling clouds at midlevels and the other at upper levels during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field program in Florida in 2002. A penetration by one of the aircraft into a cold midlatitude orographic wave cloud composed of small particles is further evaluated. The σ and IWC derived by each method are compared and evaluated in different ways for each aircraft dataset. Direct measurements of σ exceed those derived indirectly by a factor of 2–2.5. The IWC probes, relying on ice sublimation, appear to measure accurately except when the IWC is high or the particles too large to sublimate completely during the short transit time through the probe. The IWC estimated from the particle probes are accurate when direct measurements are available to provide constraints and give useful information in high IWC/large particle situations.
Because of the discrepancy in σ estimates between the direct and indirect approaches, there is a factor of 2–3 difference in [IWC/σ] between them. Although there are significant uncertainties involved in its use, comparisons with several independent data sources suggest that the indirect method is the more accurate of the two approaches. However, experiments are needed to resolve the source of the discrepancy in σ.
Abstract
The effective radius (re ) is a crucial variable in representing the radiative properties of cloud layers in general circulation models. This parameter is proportional to the condensed water content (CWC) divided by the extinction (σ). For ice cloud layers, parameterizations for re have been developed from aircraft in situ measurements 1) indirectly, using data obtained from particle spectrometer probes and assumptions or observations about particle shape and mass to get the ice water content (IWC) and area to get σ, and recently 2) from probes that derive IWC and σ more directly, referred to as the direct approach, even though the extinction is not measured directly.
This study compares [IWC/σ] derived from the two methods using datasets acquired from comparable instruments on two aircraft, one sampling clouds at midlevels and the other at upper levels during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field program in Florida in 2002. A penetration by one of the aircraft into a cold midlatitude orographic wave cloud composed of small particles is further evaluated. The σ and IWC derived by each method are compared and evaluated in different ways for each aircraft dataset. Direct measurements of σ exceed those derived indirectly by a factor of 2–2.5. The IWC probes, relying on ice sublimation, appear to measure accurately except when the IWC is high or the particles too large to sublimate completely during the short transit time through the probe. The IWC estimated from the particle probes are accurate when direct measurements are available to provide constraints and give useful information in high IWC/large particle situations.
Because of the discrepancy in σ estimates between the direct and indirect approaches, there is a factor of 2–3 difference in [IWC/σ] between them. Although there are significant uncertainties involved in its use, comparisons with several independent data sources suggest that the indirect method is the more accurate of the two approaches. However, experiments are needed to resolve the source of the discrepancy in σ.
Abstract
The Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.
Abstract
The Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.
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
A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.
Abstract
A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.
