Search Results
You are looking at 1 - 10 of 16 items for
- Author or Editor: Jennifer M. Comstock x
- Refine by Access: All Content x
Abstract
In Part III of a series of papers describing the extended time high-cloud observations from the University of Utah Facility for Atmospheric Remote Sensing (FARS) supporting the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment, the visible and infrared radiative properties of cirrus clouds over Salt Lake City, Utah, are examined. Using ∼860 h of combined ruby (0.694 μm) lidar and midinfrared (9.5–11.5 μm) radiometer data collected between 1992 and 1999 from visually identified cirrus clouds, the visible optical depths τ and infrared layer emittance ϵ of the varieties of midlatitude cirrus are characterized. The mean and median values for the cirrus sample are 0.75 ± 0.91 and 0.61 for τ, and 0.30 ± 0.22 and 0.25 for ϵ. Other scattering parameters studied are the visible extinction and infrared absorption coefficients, and their ratio, and the lidar backscatter-to-extinction ratio, which has a mean value of 0.041 sr−1. Differences among cirrus clouds generated by general synoptic (e.g., jet stream), thunderstorm anvil, and orographic mechanisms are found, reflecting basic cloud microphysical effects. The authors draw parameterizations in terms of midcloud temperature T m and physical cloud thickness Δz for ϵ and τ: both macrophysical variables are needed to adequately address the impact of the adiabatic process on ice cloud content, which modulates radiative transfer as a function of temperature. For the total cirrus dataset, the authors find ϵ = 1 − exp [−8.5 × 10−5 (T m + 80°C) Δz]. These parameterizations, based on a uniquely comprehensive dataset, hold the potential for improving weather and climate model predictions, and satellite cloud property retrieval methods.
Abstract
In Part III of a series of papers describing the extended time high-cloud observations from the University of Utah Facility for Atmospheric Remote Sensing (FARS) supporting the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment, the visible and infrared radiative properties of cirrus clouds over Salt Lake City, Utah, are examined. Using ∼860 h of combined ruby (0.694 μm) lidar and midinfrared (9.5–11.5 μm) radiometer data collected between 1992 and 1999 from visually identified cirrus clouds, the visible optical depths τ and infrared layer emittance ϵ of the varieties of midlatitude cirrus are characterized. The mean and median values for the cirrus sample are 0.75 ± 0.91 and 0.61 for τ, and 0.30 ± 0.22 and 0.25 for ϵ. Other scattering parameters studied are the visible extinction and infrared absorption coefficients, and their ratio, and the lidar backscatter-to-extinction ratio, which has a mean value of 0.041 sr−1. Differences among cirrus clouds generated by general synoptic (e.g., jet stream), thunderstorm anvil, and orographic mechanisms are found, reflecting basic cloud microphysical effects. The authors draw parameterizations in terms of midcloud temperature T m and physical cloud thickness Δz for ϵ and τ: both macrophysical variables are needed to adequately address the impact of the adiabatic process on ice cloud content, which modulates radiative transfer as a function of temperature. For the total cirrus dataset, the authors find ϵ = 1 − exp [−8.5 × 10−5 (T m + 80°C) Δz]. These parameterizations, based on a uniquely comprehensive dataset, hold the potential for improving weather and climate model predictions, and satellite cloud property retrieval methods.
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
Data collected in midlatitude cirrus clouds by instruments on jet aircraft typically show particle size distributions that have distinct distribution modes in both the 10–30-μm maximum dimension (D) size range and the 200–300-μm D size range or larger. A literal interpretation of the small D mode in these datasets suggests that total concentrations Nt in midlatitude cirrus are, on average, well in excess of 1 cm−3 whereas more conventional analyses of in situ data and cloud process model results suggest Nt values a factor of 10 less. Given this wide discrepancy, questions have been raised regarding the influence of data artifacts caused by the shattering of large crystals on aircraft and probe surfaces. This inconsistency and the general nature of the cirrus particle size distribution are examined using a ground-based remote sensing dataset. An algorithm using millimeter-wavelength radar Doppler moments and Raman lidar-derived extinction is developed to retrieve a bimodal particle size distribution and its uncertainty. This algorithm is applied to case studies as well as to 313 h of cirrus measurements collected at the Atmospheric Radiation Measurement site near Lamont, Oklahoma, in 2000. It is shown that particle size distributions in cirrus can often be described as bimodal, and that this bimodality is a function of temperature and location within cirrus layers. However, the existence of Nt > 1 cm−3 in cirrus is rare (<1% of the time) and the Nt implied by the remote sensing data tends to be on the order of 100 cm−3.
Abstract
Data collected in midlatitude cirrus clouds by instruments on jet aircraft typically show particle size distributions that have distinct distribution modes in both the 10–30-μm maximum dimension (D) size range and the 200–300-μm D size range or larger. A literal interpretation of the small D mode in these datasets suggests that total concentrations Nt in midlatitude cirrus are, on average, well in excess of 1 cm−3 whereas more conventional analyses of in situ data and cloud process model results suggest Nt values a factor of 10 less. Given this wide discrepancy, questions have been raised regarding the influence of data artifacts caused by the shattering of large crystals on aircraft and probe surfaces. This inconsistency and the general nature of the cirrus particle size distribution are examined using a ground-based remote sensing dataset. An algorithm using millimeter-wavelength radar Doppler moments and Raman lidar-derived extinction is developed to retrieve a bimodal particle size distribution and its uncertainty. This algorithm is applied to case studies as well as to 313 h of cirrus measurements collected at the Atmospheric Radiation Measurement site near Lamont, Oklahoma, in 2000. It is shown that particle size distributions in cirrus can often be described as bimodal, and that this bimodality is a function of temperature and location within cirrus layers. However, the existence of Nt > 1 cm−3 in cirrus is rare (<1% of the time) and the Nt implied by the remote sensing data tends to be on the order of 100 cm−3.
Abstract
A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around −12.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ~20% of clouds at −30°C, ~50% of clouds at −20°C, and ~65% of clouds at −10°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.
Abstract
A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around −12.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ~20% of clouds at −30°C, ~50% of clouds at −20°C, and ~65% of clouds at −10°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.
Abstract
Cloud radiative effects are examined using long-term datasets collected at the U.S. Department of Energy’s three Atmospheric Radiation Measurement Program Climate Research Facilities in the tropical western Pacific Ocean. The surface radiation budget, cloud populations, and cloud radiative effects are quantified by partitioning the data by cloud type, time of day, and large-scale modes of variability such as El Niño–Southern Oscillation (ENSO) phase and wet/dry seasons at Darwin, Australia. The novel aspect of this analysis is the breakdown of aggregate cloud radiative effects by cloud type across the diurnal cycle. The Nauru Island (Republic of Nauru) cloud populations and subsequently the surface radiation budget are strongly impacted by ENSO variability, whereas the cloud populations over Manus Island (Papua New Guinea) shift only slightly in response to changes in ENSO phase. The Darwin site exhibits large seasonal monsoon-related variations. When present, deeper convective clouds have a strong influence on the amount of radiation that reaches the surface. Their limited frequency reduces their aggregate radiative impact, however. The largest source of shortwave cloud radiative effects at all three sites comes from low clouds. The observations are used to demonstrate that potential model biases in the amplitude of the diurnal cycle and mean cloud frequency would lead to larger errors in the surface energy budget when compared with biases in the timing of the diurnal cycle of cloud frequency. These results provide solid benchmarks to evaluate model simulations of cloud radiative effects in the tropics.
Abstract
Cloud radiative effects are examined using long-term datasets collected at the U.S. Department of Energy’s three Atmospheric Radiation Measurement Program Climate Research Facilities in the tropical western Pacific Ocean. The surface radiation budget, cloud populations, and cloud radiative effects are quantified by partitioning the data by cloud type, time of day, and large-scale modes of variability such as El Niño–Southern Oscillation (ENSO) phase and wet/dry seasons at Darwin, Australia. The novel aspect of this analysis is the breakdown of aggregate cloud radiative effects by cloud type across the diurnal cycle. The Nauru Island (Republic of Nauru) cloud populations and subsequently the surface radiation budget are strongly impacted by ENSO variability, whereas the cloud populations over Manus Island (Papua New Guinea) shift only slightly in response to changes in ENSO phase. The Darwin site exhibits large seasonal monsoon-related variations. When present, deeper convective clouds have a strong influence on the amount of radiation that reaches the surface. Their limited frequency reduces their aggregate radiative impact, however. The largest source of shortwave cloud radiative effects at all three sites comes from low clouds. The observations are used to demonstrate that potential model biases in the amplitude of the diurnal cycle and mean cloud frequency would lead to larger errors in the surface energy budget when compared with biases in the timing of the diurnal cycle of cloud frequency. These results provide solid benchmarks to evaluate model simulations of cloud radiative effects in the tropics.
Since October 1987, the University of Utah Facility for Atmospheric Remote Sensing (FARS) has been applied to the probing of the atmosphere, concentrating on the study of high-level clouds. Regular FARS measurements, which currently total ~3000 h of ruby lidar polarization data, have been directed toward basic cloud research, remote sensing techniques development, and to improving satellite cloud property retrieval methods and GCM predictions by providing climatologically representative cloud datasets and parameterizations. Although the initial studies involved mainly the ruby lidar, the facility has steadily evolved to include a range of visible, infrared, and microwave passive remote sensors, and state-of-the-art, high-resolution dual-wavelength scanning lidar and W-band Doppler radar systems. All three active systems display polarization diversity. In this paper are reviewed the specifications of FARS instrumentation and the research programs to which they have been applied. Four multiple remote sensor case studies of various cloud systems are presented to illustrate the research capabilities. Like a handful of similar sites elsewhere, such research centers dedicated to extended time observation programs have great potential for contributing to atmospheric monitoring and climate research.
Since October 1987, the University of Utah Facility for Atmospheric Remote Sensing (FARS) has been applied to the probing of the atmosphere, concentrating on the study of high-level clouds. Regular FARS measurements, which currently total ~3000 h of ruby lidar polarization data, have been directed toward basic cloud research, remote sensing techniques development, and to improving satellite cloud property retrieval methods and GCM predictions by providing climatologically representative cloud datasets and parameterizations. Although the initial studies involved mainly the ruby lidar, the facility has steadily evolved to include a range of visible, infrared, and microwave passive remote sensors, and state-of-the-art, high-resolution dual-wavelength scanning lidar and W-band Doppler radar systems. All three active systems display polarization diversity. In this paper are reviewed the specifications of FARS instrumentation and the research programs to which they have been applied. Four multiple remote sensor case studies of various cloud systems are presented to illustrate the research capabilities. Like a handful of similar sites elsewhere, such research centers dedicated to extended time observation programs have great potential for contributing to atmospheric monitoring and climate research.
Abstract
Employing a new approach based on combined Raman lidar and millimeter-wave radar measurements and a parameterization of the infrared absorption coefficient σ a (km−1) in terms of retrieved cloud microphysics, a statistical relation between σ a and cirrus cloud temperature is derived. The relations σ a = 0.3949 + 5.3886 × 10−3 T + 1.526 × 10−5 T 2 for ambient temperature T(°C) and σ a = 0.2896 + 3.409 × 10−3 T m for midcloud temperature T m (°C) are found using a second-order polynomial fit. Comparison with two σ a -versus-T m relations obtained primarily from midlatitude cirrus using the combined lidar–infrared radiometer (LIRAD) approach reveals significant differences. However, it is shown that this reflects both the previous convention used in curve fitting (i.e., σ a → 0 at ∼−80°C) and the types of clouds included in the datasets. Without such constraints, convergence is found in the three independent remote sensing datasets within the range of conditions considered to be valid for cirrus (i.e., cloud visible optical depth less than ∼3.0 and T m less than ∼−20°C). Hence, for completeness, reanalyzed parameterizations for a visible extinction coefficient σ e -versus-T m relation for midlatitude cirrus and a data sample involving cirrus that evolved into midlevel altostratus clouds with higher optical depths are also provided.
Abstract
Employing a new approach based on combined Raman lidar and millimeter-wave radar measurements and a parameterization of the infrared absorption coefficient σ a (km−1) in terms of retrieved cloud microphysics, a statistical relation between σ a and cirrus cloud temperature is derived. The relations σ a = 0.3949 + 5.3886 × 10−3 T + 1.526 × 10−5 T 2 for ambient temperature T(°C) and σ a = 0.2896 + 3.409 × 10−3 T m for midcloud temperature T m (°C) are found using a second-order polynomial fit. Comparison with two σ a -versus-T m relations obtained primarily from midlatitude cirrus using the combined lidar–infrared radiometer (LIRAD) approach reveals significant differences. However, it is shown that this reflects both the previous convention used in curve fitting (i.e., σ a → 0 at ∼−80°C) and the types of clouds included in the datasets. Without such constraints, convergence is found in the three independent remote sensing datasets within the range of conditions considered to be valid for cirrus (i.e., cloud visible optical depth less than ∼3.0 and T m less than ∼−20°C). Hence, for completeness, reanalyzed parameterizations for a visible extinction coefficient σ e -versus-T m relation for midlatitude cirrus and a data sample involving cirrus that evolved into midlevel altostratus clouds with higher optical depths are also provided.
Abstract
In this fifth of a series of papers describing the extended-time high cloud observation program from the University of Utah Facility for Atmospheric Remote Sensing, the structural properties of cirrus clouds over Salt Lake City, Utah, are examined. Wavelet analysis is applied to a 10-yr record of cirrus cloud ruby (0.694 μm) lidar backscatter data as a function of cloud height in order to study the presence of periodic cloud structures, such as the signatures of Kelvin–Helmholtz instabilities, cirrus mammata, and uncinus cells (all with length scales of ∼1–10 km), as well as mesoscale cloud organizations generally believed to be induced by gravity waves. About 8.4% of the data display structures after passing a 95% confidence level test, but an 80% confidence level, which seems better able to resolve structures spread over long periods, yields 16.4%. The amount of identified cloud structures does not change significantly with length scale from 0.2 to 200 km, although the frequency of mesoscale cloud structures tends to increase as length scales increase. The middle-to-lower portion of cirrus clouds contains the most identified cloud structures, which seems related to the mesoscale organization of fall streaks from cloud-top-generating cells. The variability of cirrus cloud optical depth τ (defined by the standard deviation over mean τ) derived from a combined lidar and infrared radiometer (LIRAD) analysis is shown to be largely independent of τ. Because visual examination of the lidar displays also indicates that few cirrus layers can be considered horizontally homogeneous over our typical 3-h lidar data collection period, the authors conclude that the clouds in their sample are inherently inhomogeneous even though most cirrus structures are not revealed as periodic by wavelet analysis.
Abstract
In this fifth of a series of papers describing the extended-time high cloud observation program from the University of Utah Facility for Atmospheric Remote Sensing, the structural properties of cirrus clouds over Salt Lake City, Utah, are examined. Wavelet analysis is applied to a 10-yr record of cirrus cloud ruby (0.694 μm) lidar backscatter data as a function of cloud height in order to study the presence of periodic cloud structures, such as the signatures of Kelvin–Helmholtz instabilities, cirrus mammata, and uncinus cells (all with length scales of ∼1–10 km), as well as mesoscale cloud organizations generally believed to be induced by gravity waves. About 8.4% of the data display structures after passing a 95% confidence level test, but an 80% confidence level, which seems better able to resolve structures spread over long periods, yields 16.4%. The amount of identified cloud structures does not change significantly with length scale from 0.2 to 200 km, although the frequency of mesoscale cloud structures tends to increase as length scales increase. The middle-to-lower portion of cirrus clouds contains the most identified cloud structures, which seems related to the mesoscale organization of fall streaks from cloud-top-generating cells. The variability of cirrus cloud optical depth τ (defined by the standard deviation over mean τ) derived from a combined lidar and infrared radiometer (LIRAD) analysis is shown to be largely independent of τ. Because visual examination of the lidar displays also indicates that few cirrus layers can be considered horizontally homogeneous over our typical 3-h lidar data collection period, the authors conclude that the clouds in their sample are inherently inhomogeneous even though most cirrus structures are not revealed as periodic by wavelet analysis.
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.
