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Abstract
In situ observations of the sizes, shapes, and phases of Arctic clouds were obtained during the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE). These particle distributions were then combined with a library of single-scattering properties, calculated using Mie theory and improved geometric ray optics, to determine the corresponding single-scattering properties (single-scattering albedo ω 0, phase function, and asymmetry parameter g) at solar wavelengths. During FIRE-ACE, mixed-phase clouds, where both water and ice were detected in 30 s of flight track, corresponding to 3.0-km horizontal extent, were observed in 33% of clouds. Because supercooled water drops generally dominate mass contents of these mixed-phase clouds, there is no statistically significant difference in the distributions of single-scattering properties of mixed-phase clouds compared to liquid-phase clouds, whereas those of ice crystals differ significantly. The average g for all mixed-phase clouds at visible wavelengths is 0.855±.005, similar to 0.863±.007 computed for water clouds, but higher than 0.767±.007 computed for ice clouds. Differences in g and ω 0 between mixed- and ice-phase clouds for near-infrared bands are also noted, whereas they are similar for mixed- and liquid-phase clouds.
Single-scattering properties computed using observations of mixed-phase clouds differ by more than 10% on average from those computed using a parameterization that describes the average fraction of water and ice in mixed-phase clouds. Simulations using a plane-parallel radiative transfer model show that these differences can cause top of the atmosphere albedos to vary between 6% and 100% depending on wavelength. However, when single-scattering properties are computed from observations over all phases (mixed, ice, and liquid), and average albedos are compared against those determined using the parameterized scattering properties, there is a difference of only 2% at visible wavelengths. Since observations show that the occurrence of phases is clustered, large-scale averages may not be representative of mixed-phase cloud climatic effects.
Abstract
In situ observations of the sizes, shapes, and phases of Arctic clouds were obtained during the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE). These particle distributions were then combined with a library of single-scattering properties, calculated using Mie theory and improved geometric ray optics, to determine the corresponding single-scattering properties (single-scattering albedo ω 0, phase function, and asymmetry parameter g) at solar wavelengths. During FIRE-ACE, mixed-phase clouds, where both water and ice were detected in 30 s of flight track, corresponding to 3.0-km horizontal extent, were observed in 33% of clouds. Because supercooled water drops generally dominate mass contents of these mixed-phase clouds, there is no statistically significant difference in the distributions of single-scattering properties of mixed-phase clouds compared to liquid-phase clouds, whereas those of ice crystals differ significantly. The average g for all mixed-phase clouds at visible wavelengths is 0.855±.005, similar to 0.863±.007 computed for water clouds, but higher than 0.767±.007 computed for ice clouds. Differences in g and ω 0 between mixed- and ice-phase clouds for near-infrared bands are also noted, whereas they are similar for mixed- and liquid-phase clouds.
Single-scattering properties computed using observations of mixed-phase clouds differ by more than 10% on average from those computed using a parameterization that describes the average fraction of water and ice in mixed-phase clouds. Simulations using a plane-parallel radiative transfer model show that these differences can cause top of the atmosphere albedos to vary between 6% and 100% depending on wavelength. However, when single-scattering properties are computed from observations over all phases (mixed, ice, and liquid), and average albedos are compared against those determined using the parameterized scattering properties, there is a difference of only 2% at visible wavelengths. Since observations show that the occurrence of phases is clustered, large-scale averages may not be representative of mixed-phase cloud climatic effects.
Abstract
Best estimates of the bulk microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration) are derived for three case studies of tropical ice clouds sampled during the Tropical Warm Pool International Cloud Experiment (TWP-ICE). Two case studies are aged cirrus clouds produced by deep convection (the so-called 27/01 and 29/01 cases), and the third (“02/02”) is a fresh anvil produced by deep convective activity over the Tiwi Islands. Using crystal images obtained by a Cloud Particle Imager (CPI), it is observed that small ice particles (with maximum dimension D < 50–100 μm) were predominantly quasi spherical, with the degree of nonsphericity increasing rapidly in the 50 < D < 100-μm range. For D > 100 μm, the aged cirrus clouds were predominantly characterized by bullet rosettes and aggregates of bullet rosettes, plates, and columns. In contrast, the fresh anvil had more frequent occurrences of plates, columns, aggregates of plates, and occasionally capped columns. The impact of shattering of large ice crystals on probe tips and the overall quality of the TWP-ICE in situ microphysical measurements are assessed. It is suggested that shattering has a relatively small impact on the CPI and cloud droplet probe (CDP) TWP-ICE data and a large impact on the Cloud Aerosol Spectrometer data, as already documented by others. It is also shown that the CPI size distributions must be multiplied by a factor of 4 to match those of the cloud imaging probe (CIP) for maximum dimension larger than 100 μm (taken as a reference). A technique [named Best Estimate of Area and Density (BEAD)] to minimize errors associated with the density (ρ)–D and projected area (A)–D assumptions in bulk microphysics calculation is introduced and applied to the TWP-ICE data. The method makes direct use of the frequency of occurrence of each particle habit as classified from the CPI data and prescribed ρ–D and A–D relationships from the literature. This approach produces ice water content (IWC) estimates that are virtually unbiased relative to bulk measures obtained from a counterflow spectrometer and impactor (CSI) probe. In contrast, the use of ρ–D and A–D relationships for single habits does produce large biases relative to the CSI observations: from −50% for bullet rosettes to +70%–80% for aggregates. The so-called width, length, area, and perimeter (WLAP) technique, which also makes use of individual CPI images, is found to produce positively biased IWCs (by 40% or so), and has a standard deviation of the errors similar to the BEAD technique. The impact of the large variability of the size distributions measured by different probe combinations on the bulk microphysical properties is characterized. The mean fractional differences with respect to the CSI measurements are small for the CPI + CIP, CPI, and CDP + CIP combinations (2.2%, −0.8%, and −1.1%, respectively), with standard deviations of the fractional differences ranging from 7% to 9%. This result provides an independent validation of the CPI scaling factor. The fractional differences produced between the CPI + CIP, CPI, and CDP + CIP combinations for extinction, effective radius, and total concentration are 33%, 10%–20%, and 90%, respectively, with relatively small standard deviations of 5%–8%. The fractional difference on total concentration varies greatly over the concentration range though, with values being larger than a factor of 2 for total concentrations smaller than 40 L−1, but reducing to 10%–20% for concentrations larger than 100 L−1. Therefore, caution should be exercised when using total concentrations smaller than 60–80 L−1 as references for radar–lidar retrieval evaluation.
Abstract
Best estimates of the bulk microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration) are derived for three case studies of tropical ice clouds sampled during the Tropical Warm Pool International Cloud Experiment (TWP-ICE). Two case studies are aged cirrus clouds produced by deep convection (the so-called 27/01 and 29/01 cases), and the third (“02/02”) is a fresh anvil produced by deep convective activity over the Tiwi Islands. Using crystal images obtained by a Cloud Particle Imager (CPI), it is observed that small ice particles (with maximum dimension D < 50–100 μm) were predominantly quasi spherical, with the degree of nonsphericity increasing rapidly in the 50 < D < 100-μm range. For D > 100 μm, the aged cirrus clouds were predominantly characterized by bullet rosettes and aggregates of bullet rosettes, plates, and columns. In contrast, the fresh anvil had more frequent occurrences of plates, columns, aggregates of plates, and occasionally capped columns. The impact of shattering of large ice crystals on probe tips and the overall quality of the TWP-ICE in situ microphysical measurements are assessed. It is suggested that shattering has a relatively small impact on the CPI and cloud droplet probe (CDP) TWP-ICE data and a large impact on the Cloud Aerosol Spectrometer data, as already documented by others. It is also shown that the CPI size distributions must be multiplied by a factor of 4 to match those of the cloud imaging probe (CIP) for maximum dimension larger than 100 μm (taken as a reference). A technique [named Best Estimate of Area and Density (BEAD)] to minimize errors associated with the density (ρ)–D and projected area (A)–D assumptions in bulk microphysics calculation is introduced and applied to the TWP-ICE data. The method makes direct use of the frequency of occurrence of each particle habit as classified from the CPI data and prescribed ρ–D and A–D relationships from the literature. This approach produces ice water content (IWC) estimates that are virtually unbiased relative to bulk measures obtained from a counterflow spectrometer and impactor (CSI) probe. In contrast, the use of ρ–D and A–D relationships for single habits does produce large biases relative to the CSI observations: from −50% for bullet rosettes to +70%–80% for aggregates. The so-called width, length, area, and perimeter (WLAP) technique, which also makes use of individual CPI images, is found to produce positively biased IWCs (by 40% or so), and has a standard deviation of the errors similar to the BEAD technique. The impact of the large variability of the size distributions measured by different probe combinations on the bulk microphysical properties is characterized. The mean fractional differences with respect to the CSI measurements are small for the CPI + CIP, CPI, and CDP + CIP combinations (2.2%, −0.8%, and −1.1%, respectively), with standard deviations of the fractional differences ranging from 7% to 9%. This result provides an independent validation of the CPI scaling factor. The fractional differences produced between the CPI + CIP, CPI, and CDP + CIP combinations for extinction, effective radius, and total concentration are 33%, 10%–20%, and 90%, respectively, with relatively small standard deviations of 5%–8%. The fractional difference on total concentration varies greatly over the concentration range though, with values being larger than a factor of 2 for total concentrations smaller than 40 L−1, but reducing to 10%–20% for concentrations larger than 100 L−1. Therefore, caution should be exercised when using total concentrations smaller than 60–80 L−1 as references for radar–lidar retrieval evaluation.
Abstract
The characteristics of Arctic mixed-phase stratiform clouds and their relation to vertical air motions are examined using ground-based observations during the Mixed-Phase Arctic Cloud Experiment (MPACE) in Barrow, Alaska, during fall 2004. The cloud macrophysical, microphysical, and dynamical properties are derived from a suite of active and passive remote sensors. Low-level, single-layer, mixed-phase stratiform clouds are typically topped by a 400–700-m-deep liquid water layer from which ice crystals precipitate. These clouds are strongly dominated (85% by mass) by liquid water. On average, an in-cloud updraft of 0.4 m s−1 sustains the clouds, although cloud-scale circulations lead to a variability of up to ±2 m s−1 from the average. Dominant scales-of-variability in both vertical air motions and cloud microphysical properties retrieved by this analysis occur at 0.5–10-km wavelengths. In updrafts, both cloud liquid and ice mass grow, although the net liquid mass growth is usually largest. Between updrafts, nearly all ice falls out and/or sublimates while the cloud liquid diminishes but does not completely evaporate. The persistence of liquid water throughout these cloud cycles suggests that ice-forming nuclei, and thus ice crystal, concentrations must be limited and that water vapor is plentiful. These details are brought together within the context of a conceptual model relating cloud-scale dynamics and microphysics.
Abstract
The characteristics of Arctic mixed-phase stratiform clouds and their relation to vertical air motions are examined using ground-based observations during the Mixed-Phase Arctic Cloud Experiment (MPACE) in Barrow, Alaska, during fall 2004. The cloud macrophysical, microphysical, and dynamical properties are derived from a suite of active and passive remote sensors. Low-level, single-layer, mixed-phase stratiform clouds are typically topped by a 400–700-m-deep liquid water layer from which ice crystals precipitate. These clouds are strongly dominated (85% by mass) by liquid water. On average, an in-cloud updraft of 0.4 m s−1 sustains the clouds, although cloud-scale circulations lead to a variability of up to ±2 m s−1 from the average. Dominant scales-of-variability in both vertical air motions and cloud microphysical properties retrieved by this analysis occur at 0.5–10-km wavelengths. In updrafts, both cloud liquid and ice mass grow, although the net liquid mass growth is usually largest. Between updrafts, nearly all ice falls out and/or sublimates while the cloud liquid diminishes but does not completely evaporate. The persistence of liquid water throughout these cloud cycles suggests that ice-forming nuclei, and thus ice crystal, concentrations must be limited and that water vapor is plentiful. These details are brought together within the context of a conceptual model relating cloud-scale dynamics and microphysics.
The Department of Energy Atmospheric Radiation Measurement (ARM) program is a climate research user facility operating stationary ground sites that provide long-term measurements of climate-relevant properties, mobile ground- and ship-based facilities to conduct shorter field campaigns (6–12 months), and the ARM Aerial Facility (AAF). The airborne observations acquired by the AAF enhance the surface-based ARM measurements by providing high-resolution in situ measurements for process understanding, retrieval-algorithm development, and model evaluation that are not possible using surface-or satellite-based techniques.
Several ARM aerial efforts were consolidated to form AAF in 2006. With the exception of a small aircraft used for routine measurements of aerosols and carbon cycle gases, AAF at the time had no dedicated aircraft and only a small number of instruments at its disposal. AAF successfully carried out several missions contracting with organizations and investigators who provided their research aircraft and instrumentation. In 2009, AAF started managing operations of the Battelle-owned Gulfstream I (G-1) large twin-turboprop research aircraft. Furthermore, the American Recovery and Reinvestment Act of 2009 provided funding for the procurement of over twenty new instruments to be used aboard the G-1 and AAF contracted aircraft. Depending on the requested scope, AAF now executes campaigns using the G-1 or contracted aircraft, producing freely available datasets for studying gas, aerosol, cloud, and radiative processes and their interactions in the atmosphere. AAF is also engaged in the maturation and testing of newly developed airborne sensors to help foster the next generation of airborne instruments.
The Department of Energy Atmospheric Radiation Measurement (ARM) program is a climate research user facility operating stationary ground sites that provide long-term measurements of climate-relevant properties, mobile ground- and ship-based facilities to conduct shorter field campaigns (6–12 months), and the ARM Aerial Facility (AAF). The airborne observations acquired by the AAF enhance the surface-based ARM measurements by providing high-resolution in situ measurements for process understanding, retrieval-algorithm development, and model evaluation that are not possible using surface-or satellite-based techniques.
Several ARM aerial efforts were consolidated to form AAF in 2006. With the exception of a small aircraft used for routine measurements of aerosols and carbon cycle gases, AAF at the time had no dedicated aircraft and only a small number of instruments at its disposal. AAF successfully carried out several missions contracting with organizations and investigators who provided their research aircraft and instrumentation. In 2009, AAF started managing operations of the Battelle-owned Gulfstream I (G-1) large twin-turboprop research aircraft. Furthermore, the American Recovery and Reinvestment Act of 2009 provided funding for the procurement of over twenty new instruments to be used aboard the G-1 and AAF contracted aircraft. Depending on the requested scope, AAF now executes campaigns using the G-1 or contracted aircraft, producing freely available datasets for studying gas, aerosol, cloud, and radiative processes and their interactions in the atmosphere. AAF is also engaged in the maturation and testing of newly developed airborne sensors to help foster the next generation of airborne instruments.
Abstract
The purpose of this paper is to assess the potential of a spaceborne 94-GHz radar for providing useful measurements of the vertical distribution and water content of ice clouds on a global scale.
Calculations of longwave (LW) fluxes for a number of model ice clouds are performed. These are used to determine the minimum cloud optical depth that will cause changes in the outgoing longwave radiation or flux divergence within a cloud layer greatear than 10 W m−2, and in surface downward LW flux greater than 5 W m−2, compared to the clear-sky value. These optical depth values are used as the definition of a “radiatively significant” cloud. Different “thresholds of radiative significance” are calculated for each of the three radiation parameters and also for tropical and midlatitude cirrus clouds. Extensive observational datasets of ice crystal size spectra from midlatitude and tropical cirrus are then used to assess the capability of a radar to meet these measurement requirements. A radar with a threshold of −30 dBZ should detect 99% (92%) of “radiatively significant” clouds in the midlatitudes (Tropics). This detection efficiency may be reduced significantly for tropical clouds at very low temperatures (−80°C).
The LW flux calculations are also used to establish the required accuracy within which the optical depth should be known in order to estimate LW fluxes or flux divergence to within specified limits of accuracy. Accuracy requirements are also expressed in terms of ice water content (IWC) because of the need to validate cloud parameterization schemes in general circulation models (GCMs). Estimates of IWC derived using radar alone and also using additional information to define the mean crystal size are considered. With crystal size information available, the IWC for samples with a horizontal scale of 1–2 km may be obtained with a bias of less than 8%. For IWC larger than 0.01 g m−3, the random error is in the range +50% to −35%, whereas for a value of 0.001 g m−3 the random error increases to between +80% and −45%. This level of accuracy also represents the best that may be achieved for estimates of the cloud optical depth and meets the requirements derived from LW flux calculations. In the absence of independent particle size information, the random error is within the range +85% to −55% for IWC greater than 0.01 g m−3. For the same IWC range, the estimated bias is few than ±15%. This accuracy is sufficient to provide useful constraints on GCM cloud parameteriation schemes.
Abstract
The purpose of this paper is to assess the potential of a spaceborne 94-GHz radar for providing useful measurements of the vertical distribution and water content of ice clouds on a global scale.
Calculations of longwave (LW) fluxes for a number of model ice clouds are performed. These are used to determine the minimum cloud optical depth that will cause changes in the outgoing longwave radiation or flux divergence within a cloud layer greatear than 10 W m−2, and in surface downward LW flux greater than 5 W m−2, compared to the clear-sky value. These optical depth values are used as the definition of a “radiatively significant” cloud. Different “thresholds of radiative significance” are calculated for each of the three radiation parameters and also for tropical and midlatitude cirrus clouds. Extensive observational datasets of ice crystal size spectra from midlatitude and tropical cirrus are then used to assess the capability of a radar to meet these measurement requirements. A radar with a threshold of −30 dBZ should detect 99% (92%) of “radiatively significant” clouds in the midlatitudes (Tropics). This detection efficiency may be reduced significantly for tropical clouds at very low temperatures (−80°C).
The LW flux calculations are also used to establish the required accuracy within which the optical depth should be known in order to estimate LW fluxes or flux divergence to within specified limits of accuracy. Accuracy requirements are also expressed in terms of ice water content (IWC) because of the need to validate cloud parameterization schemes in general circulation models (GCMs). Estimates of IWC derived using radar alone and also using additional information to define the mean crystal size are considered. With crystal size information available, the IWC for samples with a horizontal scale of 1–2 km may be obtained with a bias of less than 8%. For IWC larger than 0.01 g m−3, the random error is in the range +50% to −35%, whereas for a value of 0.001 g m−3 the random error increases to between +80% and −45%. This level of accuracy also represents the best that may be achieved for estimates of the cloud optical depth and meets the requirements derived from LW flux calculations. In the absence of independent particle size information, the random error is within the range +85% to −55% for IWC greater than 0.01 g m−3. For the same IWC range, the estimated bias is few than ±15%. This accuracy is sufficient to provide useful constraints on GCM cloud parameteriation schemes.
A comprehensive dataset describing tropical cloud systems and their environmental setting and impacts has been collected during the Tropical Warm Pool International Cloud Experiment (TWPICE) and Aerosol and Chemical Transport in Tropical Convection (ACTIVE) campaign in the area around Darwin, Northern Australia, in January and February 2006. The aim of the experiment was to observe the evolution of tropical cloud systems and their interaction with the environment within an observational framework optimized for a range of modeling activities with the goal of improving the representation of cloud and aerosol process in a range of models. The experiment design utilized permanent observational facilities in Darwin, including a polarimetric weather radar and a suite of cloud remote-sensing instruments. This was augmented by a dense network of soundings, together with radiation, flux, lightning, and remote-sensing measurements, as well as oceanographic observations. A fleet of five research aircraft, including two high-altitude aircraft, were taking measurements of fluxes, cloud microphysics, and chemistry; cloud radar and lidar were carried on a third aircraft. Highlights of the experiment include an intense mesoscale convective system (MCS) developed within the network, observations used to analyze the impacts of aerosol on convective systems, and observations used to relate cirrus properties to the parent storm properties.
A comprehensive dataset describing tropical cloud systems and their environmental setting and impacts has been collected during the Tropical Warm Pool International Cloud Experiment (TWPICE) and Aerosol and Chemical Transport in Tropical Convection (ACTIVE) campaign in the area around Darwin, Northern Australia, in January and February 2006. The aim of the experiment was to observe the evolution of tropical cloud systems and their interaction with the environment within an observational framework optimized for a range of modeling activities with the goal of improving the representation of cloud and aerosol process in a range of models. The experiment design utilized permanent observational facilities in Darwin, including a polarimetric weather radar and a suite of cloud remote-sensing instruments. This was augmented by a dense network of soundings, together with radiation, flux, lightning, and remote-sensing measurements, as well as oceanographic observations. A fleet of five research aircraft, including two high-altitude aircraft, were taking measurements of fluxes, cloud microphysics, and chemistry; cloud radar and lidar were carried on a third aircraft. Highlights of the experiment include an intense mesoscale convective system (MCS) developed within the network, observations used to analyze the impacts of aerosol on convective systems, and observations used to relate cirrus properties to the parent storm properties.
Nasa's Tropical Cloud Systems and Processes Experiment
Investigating Tropical Cyclogenesis and Hurricane Intensity Change
In July 2005, the National Aeronautics and Space Administration investigated tropical cyclogenesis, hurricane structure, and intensity change in the eastern North Pacific and western Atlantic using its ER-2 high-altitude research aircraft. The campaign, called the Tropical Cloud Systems and Processes (TCSP) experiment, was conducted in conjunction with the National Oceanic and Atmospheric Administration/Hurricane Research Division's Intensity Forecasting Experiment. A number of in situ and remote sensor datasets were collected inside and above four tropical cyclones representing a broad spectrum of tropical cyclone intensity and development in diverse environments. While the TCSP datasets directly address several key hypotheses governing tropical cyclone formation, including the role of vertical wind shear, dynamics of convective bursts, and upscale growth of the initial vortex, two of the storms sampled were also unusually strong, early season storms. Highlights from the genesis missions are described in this article, along with some of the unexpected results from the campaign. Interesting observations include an extremely intense, highly electrified convective tower in the eyewall of Hurricane Emily and a broad region of mesoscale subsidence detected in the lower stratosphere over landfalling Tropical Storm Gert.
In July 2005, the National Aeronautics and Space Administration investigated tropical cyclogenesis, hurricane structure, and intensity change in the eastern North Pacific and western Atlantic using its ER-2 high-altitude research aircraft. The campaign, called the Tropical Cloud Systems and Processes (TCSP) experiment, was conducted in conjunction with the National Oceanic and Atmospheric Administration/Hurricane Research Division's Intensity Forecasting Experiment. A number of in situ and remote sensor datasets were collected inside and above four tropical cyclones representing a broad spectrum of tropical cyclone intensity and development in diverse environments. While the TCSP datasets directly address several key hypotheses governing tropical cyclone formation, including the role of vertical wind shear, dynamics of convective bursts, and upscale growth of the initial vortex, two of the storms sampled were also unusually strong, early season storms. Highlights from the genesis missions are described in this article, along with some of the unexpected results from the campaign. Interesting observations include an extremely intense, highly electrified convective tower in the eyewall of Hurricane Emily and a broad region of mesoscale subsidence detected in the lower stratosphere over landfalling Tropical Storm Gert.
Abstract
Understanding the formation and evolution of ice in clouds requires detailed information on the size, shape, mass, and optical properties of individual cloud hydrometeors and their bulk properties over a broad range of atmospheric conditions. Since the 1960s, instrumentation and research aircraft have evolved, providing increasingly more accurate and larger quantities of data about cloud particle properties. In this chapter, the current status of electrical powered, in situ measurement systems are reviewed with respect to their strengths and weaknesses and their limitations and uncertainties are documented. There remain many outstanding challenges. These are summarized and accompanied by recommendations for moving forward through new developments that fill the remaining information gaps. Closing these gaps will remove the obstacles that continue to hinder our understanding of cloud processes in general and the evolution of ice in particular.
Abstract
Understanding the formation and evolution of ice in clouds requires detailed information on the size, shape, mass, and optical properties of individual cloud hydrometeors and their bulk properties over a broad range of atmospheric conditions. Since the 1960s, instrumentation and research aircraft have evolved, providing increasingly more accurate and larger quantities of data about cloud particle properties. In this chapter, the current status of electrical powered, in situ measurement systems are reviewed with respect to their strengths and weaknesses and their limitations and uncertainties are documented. There remain many outstanding challenges. These are summarized and accompanied by recommendations for moving forward through new developments that fill the remaining information gaps. Closing these gaps will remove the obstacles that continue to hinder our understanding of cloud processes in general and the evolution of ice in particular.
