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- Author or Editor: Jennifer M. Comstock x
- Journal of Atmospheric and Oceanic Technology x
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Abstract
A method for retrieval of cirrus macrophysical and radiative properties using combined ruby lidar and infrared radiometer measurements is explained in detail. The retrieval algorithm includes estimation of a variable backscatter-to-extinction ratio for each lidar profile, which accounts for changes in cloud microphysical properties with time. The technique also utilizes a correlated K distribution radiative transfer model, where absorption coefficients K have been tabulated specifically for the bandwidth and filter function of the infrared radiometer. The radiative transfer model allows for estimation of infrared emission due to atmospheric water vapor, ozone, and carbon dioxide, which is essential for deriving cirrus radiative properties. Also described is an improved technique for estimation of upwelling IR radiation that is emitted by the surface of the earth and reflected by the cloud into the radiometer field of view. Derived cirrus cloud properties include base and top height and temperature, visible optical depth, emittance, backscatter-to-extinction ratio, and extinction-to-absorption ratio. The purpose of this algorithm is to facilitate the analysis of the extensive high-cloud dataset obtained at the University of Utah's Facility for Atmospheric Remote Sensing in Salt Lake City, Utah. To illustrate the method, a cirrus case study is presented.
Abstract
A method for retrieval of cirrus macrophysical and radiative properties using combined ruby lidar and infrared radiometer measurements is explained in detail. The retrieval algorithm includes estimation of a variable backscatter-to-extinction ratio for each lidar profile, which accounts for changes in cloud microphysical properties with time. The technique also utilizes a correlated K distribution radiative transfer model, where absorption coefficients K have been tabulated specifically for the bandwidth and filter function of the infrared radiometer. The radiative transfer model allows for estimation of infrared emission due to atmospheric water vapor, ozone, and carbon dioxide, which is essential for deriving cirrus radiative properties. Also described is an improved technique for estimation of upwelling IR radiation that is emitted by the surface of the earth and reflected by the cloud into the radiometer field of view. Derived cirrus cloud properties include base and top height and temperature, visible optical depth, emittance, backscatter-to-extinction ratio, and extinction-to-absorption ratio. The purpose of this algorithm is to facilitate the analysis of the extensive high-cloud dataset obtained at the University of Utah's Facility for Atmospheric Remote Sensing in Salt Lake City, Utah. To illustrate the method, a cirrus case study is presented.
Abstract
A feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.
Abstract
A feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.
