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Abstract
A topic of current practical interest is the accurate characterization of the synoptic-scale atmospheric state from wind profiler and radiosonde network observations. The authors have examined several related and commonly applied objective analysis techniques for performing this characterization and considered their associated level of uncertainty both from a theoretical and a practical standpoint. A case study is presented where two wind profiler triangles with nearly identical centroids and no common vertices produced strikingly different results during a 43-h period. It is concluded that the uncertainty in objectively analyzed quantities can easily be as large as the expected synoptic-scale signal. In order to quantify the statistical precision of the algorithms, the authors conducted a realistic observing system simulation experiment using output from a mesoscale model. A simple parameterization for estimating the uncertainty in horizontal gradient quantities in terms of known errors in the objectively analyzed wind components and temperature is developed from these results.
Abstract
A topic of current practical interest is the accurate characterization of the synoptic-scale atmospheric state from wind profiler and radiosonde network observations. The authors have examined several related and commonly applied objective analysis techniques for performing this characterization and considered their associated level of uncertainty both from a theoretical and a practical standpoint. A case study is presented where two wind profiler triangles with nearly identical centroids and no common vertices produced strikingly different results during a 43-h period. It is concluded that the uncertainty in objectively analyzed quantities can easily be as large as the expected synoptic-scale signal. In order to quantify the statistical precision of the algorithms, the authors conducted a realistic observing system simulation experiment using output from a mesoscale model. A simple parameterization for estimating the uncertainty in horizontal gradient quantities in terms of known errors in the objectively analyzed wind components and temperature is developed from these results.
Abstract
The algorithm described in Part I has been applied to the millimeter cloud radar observations from January 1999 to December 2005 at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (including Manus and Nauru) sites. Approximately 10 000 cirrus hours from each of these sites were analyzed. Retrieved cloud properties including condensed mass, particle size, optical depth, and in-cloud vertical air motions were analyzed in terms of their geographical, seasonal, and diurnal variations. The analysis shows that tropical ice clouds observed by millimeter radar are very different from ice clouds at SGP, with the tropical clouds having slightly larger particle sizes and greater ice masses and being more likely to be associated with ascending air motions, in addition to being colder and higher in altitude. A positive residual of derived in-cloud air motion found in the tropical data likely provides evidence for lofting of air into the tropopause transition layer as a result of radiative heating. The midlatitude cirrus demonstrate strong seasonal variations with more frequent, thicker clouds occurring during the summer than during the winter. Very subtle seasonal variations are found for tropical ice clouds, and evidence is presented that cirrus properties vary interannually and are correlated with El Niño oscillations. In addition, it is found that tropical cirrus demonstrate a stronger diurnal cycle than cirrus of the midlatitudes, with the in-cloud updrafts peaking in the early afternoon.
Abstract
The algorithm described in Part I has been applied to the millimeter cloud radar observations from January 1999 to December 2005 at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (including Manus and Nauru) sites. Approximately 10 000 cirrus hours from each of these sites were analyzed. Retrieved cloud properties including condensed mass, particle size, optical depth, and in-cloud vertical air motions were analyzed in terms of their geographical, seasonal, and diurnal variations. The analysis shows that tropical ice clouds observed by millimeter radar are very different from ice clouds at SGP, with the tropical clouds having slightly larger particle sizes and greater ice masses and being more likely to be associated with ascending air motions, in addition to being colder and higher in altitude. A positive residual of derived in-cloud air motion found in the tropical data likely provides evidence for lofting of air into the tropopause transition layer as a result of radiative heating. The midlatitude cirrus demonstrate strong seasonal variations with more frequent, thicker clouds occurring during the summer than during the winter. Very subtle seasonal variations are found for tropical ice clouds, and evidence is presented that cirrus properties vary interannually and are correlated with El Niño oscillations. In addition, it is found that tropical cirrus demonstrate a stronger diurnal cycle than cirrus of the midlatitudes, with the in-cloud updrafts peaking in the early afternoon.
Abstract
The International Satellite Cloud Climatology Project (ISCCP) provides a multidecadal and global description of cloud properties that are often grouped into joint histograms of column visible optical depth τ and effective cloud-top pressure P top. It has not been possible until recently to know the actual distributions of hydrometeor layers within the ISCCP P top–τ bins. Distributions of hydrometeor layers within the ISCCP P top–τ conditional probability space using measurements from the CloudSat Cloud Profiling Radar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar within two 40° × 40° regions in the eastern and western equatorial Pacific over a 2-yr period are examined. With the exception of thin cirrus and stratocumulus, the authors show that of the P top–τ types that are commonly analyzed, none of the types contain unique distributions of geometrically defined layer types but tend to be populated by diverse sets of hydrometeor layers whose bulk profile properties conspire to render specific radiative signatures when interpreted by two-channel visible and IR sensors from space. In comparing the geometric distribution of cloud layers for common P top–τ types, it is found that the ISCCP Cirrostratus, Deep Convection, and Stratocumulus types appear to have been drawn from a common geometric distribution of hydrometeor layers. The other six common ISCCP P top–τ types do not share this feature. The authors can confidently reject an assumption that even though they have common top-of-atmosphere radiative signatures, they do not appear to share a common distribution of cloud layers and therefore are likely to have significantly different radiative heating profiles and different surface radiative forcing even though their top-of-atmosphere radiative signatures are similar.
Abstract
The International Satellite Cloud Climatology Project (ISCCP) provides a multidecadal and global description of cloud properties that are often grouped into joint histograms of column visible optical depth τ and effective cloud-top pressure P top. It has not been possible until recently to know the actual distributions of hydrometeor layers within the ISCCP P top–τ bins. Distributions of hydrometeor layers within the ISCCP P top–τ conditional probability space using measurements from the CloudSat Cloud Profiling Radar and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar within two 40° × 40° regions in the eastern and western equatorial Pacific over a 2-yr period are examined. With the exception of thin cirrus and stratocumulus, the authors show that of the P top–τ types that are commonly analyzed, none of the types contain unique distributions of geometrically defined layer types but tend to be populated by diverse sets of hydrometeor layers whose bulk profile properties conspire to render specific radiative signatures when interpreted by two-channel visible and IR sensors from space. In comparing the geometric distribution of cloud layers for common P top–τ types, it is found that the ISCCP Cirrostratus, Deep Convection, and Stratocumulus types appear to have been drawn from a common geometric distribution of hydrometeor layers. The other six common ISCCP P top–τ types do not share this feature. The authors can confidently reject an assumption that even though they have common top-of-atmosphere radiative signatures, they do not appear to share a common distribution of cloud layers and therefore are likely to have significantly different radiative heating profiles and different surface radiative forcing even though their top-of-atmosphere radiative signatures are similar.
Abstract
A Bayesian optimal estimation methodology is applied to retrieve the time-varying ice particle mass–dimensional (M–D) relationships (i.e., M = a m D b m ) and the associated uncertainties using the in situ data that were collected by the NASA WB-57 during the Midlatitude Airborne Cirrus Properties Experiment (MACPEX) in March and April 2011. The authors utilize the coincident measurements of bulk ice water content and projected cross-sectional area to constrain M–D relationships and estimate the uncertainties. It is demonstrated that the additional information provided by the particle area with respect to size could contribute considerable improvements to the algorithm performance. Extreme variability of M–D properties is found among cases as well as within individual cases, indicating the nondiscrete nature of ice crystal habits within cloud volumes and further suggesting the risk of assuming a constant M–D relationship in different conditions. Relative uncertainties of a m are approximately from 50% to 80%, and relative uncertainties of b m range from 6% to 9.5%, which would cause approximately 2.5-dB uncertainty in forward-modeled radar reflectivity or a factor-of-2 uncertainty in ice water content.
Abstract
A Bayesian optimal estimation methodology is applied to retrieve the time-varying ice particle mass–dimensional (M–D) relationships (i.e., M = a m D b m ) and the associated uncertainties using the in situ data that were collected by the NASA WB-57 during the Midlatitude Airborne Cirrus Properties Experiment (MACPEX) in March and April 2011. The authors utilize the coincident measurements of bulk ice water content and projected cross-sectional area to constrain M–D relationships and estimate the uncertainties. It is demonstrated that the additional information provided by the particle area with respect to size could contribute considerable improvements to the algorithm performance. Extreme variability of M–D properties is found among cases as well as within individual cases, indicating the nondiscrete nature of ice crystal habits within cloud volumes and further suggesting the risk of assuming a constant M–D relationship in different conditions. Relative uncertainties of a m are approximately from 50% to 80%, and relative uncertainties of b m range from 6% to 9.5%, which would cause approximately 2.5-dB uncertainty in forward-modeled radar reflectivity or a factor-of-2 uncertainty in ice water content.
Abstract
Collocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.
Abstract
Collocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.
Abstract
Empirical knowledge of how cirrus cloud properties are coupled with the large-scale meteorological environment is a prerequisite for understanding the role of microphysical processes in the life cycle of cirrus cloud systems. Using active and passive remote sensing data from the A-Train, relationships between cirrus cloud properties and the large-scale dynamics are examined. Mesoscale cirrus events from along the A-Train track from 1 yr of data are sorted on the basis of vertical distributions of radar reflectivity and on large-scale meteorological parameters derived from the NCEP–NCAR reanalysis using a K-means cluster-analysis algorithm. With these defined regimes, the authors examine two questions: Given a cirrus cloud type defined by cloud properties, what are the large-scale dynamics? Vice versa, what cirrus cloud properties tend to emerge from large-scale dynamics regimes that tend to form cirrus? From the answers to these questions, the links between the large-scale dynamics regimes and the genre of cirrus that evolve within these regimes are identified. It is found that, to a considerable extent, the large-scale environment determines the bulk cirrus properties and that, within the dynamics regimes, cirrus cloud systems tend to evolve through life cycles, the details of which are not necessarily explained by the large-scale motions alone. These results suggest that, while simple relationships may be used to parameterize the gross properties of cirrus, more sophisticated parameterizations are required for representing the detailed structure and radiative feedbacks of these clouds.
Abstract
Empirical knowledge of how cirrus cloud properties are coupled with the large-scale meteorological environment is a prerequisite for understanding the role of microphysical processes in the life cycle of cirrus cloud systems. Using active and passive remote sensing data from the A-Train, relationships between cirrus cloud properties and the large-scale dynamics are examined. Mesoscale cirrus events from along the A-Train track from 1 yr of data are sorted on the basis of vertical distributions of radar reflectivity and on large-scale meteorological parameters derived from the NCEP–NCAR reanalysis using a K-means cluster-analysis algorithm. With these defined regimes, the authors examine two questions: Given a cirrus cloud type defined by cloud properties, what are the large-scale dynamics? Vice versa, what cirrus cloud properties tend to emerge from large-scale dynamics regimes that tend to form cirrus? From the answers to these questions, the links between the large-scale dynamics regimes and the genre of cirrus that evolve within these regimes are identified. It is found that, to a considerable extent, the large-scale environment determines the bulk cirrus properties and that, within the dynamics regimes, cirrus cloud systems tend to evolve through life cycles, the details of which are not necessarily explained by the large-scale motions alone. These results suggest that, while simple relationships may be used to parameterize the gross properties of cirrus, more sophisticated parameterizations are required for representing the detailed structure and radiative feedbacks of these clouds.
Abstract
Algorithms are developed to convert data streams from multiple airborne and spaceborne remote sensors into layer-averaged cirrus bulk microphysical properties. Radiometers such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) observe narrowband spectral radiances, and active remote sensors such as the lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the millimeter radar on CloudSat will provide vertical profiles of attenuated optical backscatter and radar reflectivity. Equivalent airborne remote sensors are also routinely flown on the NASA WB-57F and ER-2 aircraft. Algorithms designed to retrieve cirrus microphysical properties from remote sensor data must be able to handle the natural variability of cirrus that can range from optically thick layers that cause lidar attenuation to tenuous layers that are not detected by the cloud radar. An approach that is adopted here is to develop an algorithm suite that has internal consistency in its formulation and assumptions. The algorithm suite is developed around a forward model of the observations and is inverted for layer-mean cloud properties using a variational technique. The theoretical uncertainty in the retrieved ice water path retrieval is 40%–50%, and the uncertainty in the layer-mean particle size retrieval ranges from 50% to 90%. Two case studies from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field campaign as well as ground-based cases from the Atmospheric Radiation Measurement Program (ARM) are used to show the efficacy and error characteristics of the algorithms.
Abstract
Algorithms are developed to convert data streams from multiple airborne and spaceborne remote sensors into layer-averaged cirrus bulk microphysical properties. Radiometers such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) observe narrowband spectral radiances, and active remote sensors such as the lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the millimeter radar on CloudSat will provide vertical profiles of attenuated optical backscatter and radar reflectivity. Equivalent airborne remote sensors are also routinely flown on the NASA WB-57F and ER-2 aircraft. Algorithms designed to retrieve cirrus microphysical properties from remote sensor data must be able to handle the natural variability of cirrus that can range from optically thick layers that cause lidar attenuation to tenuous layers that are not detected by the cloud radar. An approach that is adopted here is to develop an algorithm suite that has internal consistency in its formulation and assumptions. The algorithm suite is developed around a forward model of the observations and is inverted for layer-mean cloud properties using a variational technique. The theoretical uncertainty in the retrieved ice water path retrieval is 40%–50%, and the uncertainty in the layer-mean particle size retrieval ranges from 50% to 90%. Two case studies from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field campaign as well as ground-based cases from the Atmospheric Radiation Measurement Program (ARM) are used to show the efficacy and error characteristics of the algorithms.
Abstract
The first three moments of the millimeter-wavelength radar Doppler spectrum provide valuable information regarding both cloud properties and air motion. An algorithm using these Doppler radar moments is developed to retrieve cirrus microphysical properties and the mean air vertical motion and their errors. The observed Doppler spectrum results from the convolution of a quiet-air radar reflectivity spectrum with the turbulence probability density function. Instead of expressing the convolution integral in terms of the particle fall velocity as in past studies, herein the convolution integral is integrated over the air motion so that the mean vertical velocity within the sample volume can be explicitly solved. To avoid an ill-conditioned problem, the turbulence is considered as a parameter in the algorithm and predetermined from the Doppler spectrum width and radar reflectivity based on the observation that the spread of the particle size distribution in the velocity domain dominates the Doppler spectrum width measurement for most cirrus. It is also shown that the assumed single mode functional shapes cannot reliably represent significant bimodalities. Nevertheless, the IWC can be retrieved more reliably than can the mass mean particle size. Error analysis also shows that the retrieval algorithm results are very sensitive to the power-law relationships describing the ice particle mass and the terminal velocity in terms of the particle maximum length. It is estimated that the algorithm errors will be on the order of 35%, 85%, and ±20 cm s−1 for mass mean particle size, IWC, and sample volume mean air motion, respectively. Algorithm validation with in situ data demonstrates that the algorithm can determine the cloud microphysical properties and air mean vertical velocity within the predicted theoretical error bounds.
Abstract
The first three moments of the millimeter-wavelength radar Doppler spectrum provide valuable information regarding both cloud properties and air motion. An algorithm using these Doppler radar moments is developed to retrieve cirrus microphysical properties and the mean air vertical motion and their errors. The observed Doppler spectrum results from the convolution of a quiet-air radar reflectivity spectrum with the turbulence probability density function. Instead of expressing the convolution integral in terms of the particle fall velocity as in past studies, herein the convolution integral is integrated over the air motion so that the mean vertical velocity within the sample volume can be explicitly solved. To avoid an ill-conditioned problem, the turbulence is considered as a parameter in the algorithm and predetermined from the Doppler spectrum width and radar reflectivity based on the observation that the spread of the particle size distribution in the velocity domain dominates the Doppler spectrum width measurement for most cirrus. It is also shown that the assumed single mode functional shapes cannot reliably represent significant bimodalities. Nevertheless, the IWC can be retrieved more reliably than can the mass mean particle size. Error analysis also shows that the retrieval algorithm results are very sensitive to the power-law relationships describing the ice particle mass and the terminal velocity in terms of the particle maximum length. It is estimated that the algorithm errors will be on the order of 35%, 85%, and ±20 cm s−1 for mass mean particle size, IWC, and sample volume mean air motion, respectively. Algorithm validation with in situ data demonstrates that the algorithm can determine the cloud microphysical properties and air mean vertical velocity within the predicted theoretical error bounds.
Abstract
The properties of clouds derived using a suite of remote sensors on board the Australian research vessel (R/V) Investigator during the 5-week Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean (CAPRICORN) voyage south of Australia during March and April 2016 are examined and compared to similar measurements collected by CloudSat and CALIPSO (CC) and from data collected at Graciosa Island, Azores (GRW). In addition, we use depolarization lidar data to examine the thermodynamic phase partitioning as a function of temperature and compare those statistics to similar information reported from the CALIPSO lidar in low-Earth orbit. We find that cloud cover during CAPRICORN was 76%, dominated by clouds based in the marine boundary layer. This was lower than comparable measurements collected by CC during these months, although the CC dataset observed significantly more high clouds. In the surface-based data, approximately 2/3 (1/2) of all low-level layers observed had a reflectivity below −20 dBZ in the CAPRICORN data (GRW) with 30% (20%) of the layers observed only by the lidar. The phase partitioning in layers based in the lower 4 km of the atmosphere was similar in the two surface-based datasets, indicating a greater occurrence of the ice phase in subfreezing low clouds than what is reported from analysis of CALIPSO data.
Abstract
The properties of clouds derived using a suite of remote sensors on board the Australian research vessel (R/V) Investigator during the 5-week Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean (CAPRICORN) voyage south of Australia during March and April 2016 are examined and compared to similar measurements collected by CloudSat and CALIPSO (CC) and from data collected at Graciosa Island, Azores (GRW). In addition, we use depolarization lidar data to examine the thermodynamic phase partitioning as a function of temperature and compare those statistics to similar information reported from the CALIPSO lidar in low-Earth orbit. We find that cloud cover during CAPRICORN was 76%, dominated by clouds based in the marine boundary layer. This was lower than comparable measurements collected by CC during these months, although the CC dataset observed significantly more high clouds. In the surface-based data, approximately 2/3 (1/2) of all low-level layers observed had a reflectivity below −20 dBZ in the CAPRICORN data (GRW) with 30% (20%) of the layers observed only by the lidar. The phase partitioning in layers based in the lower 4 km of the atmosphere was similar in the two surface-based datasets, indicating a greater occurrence of the ice phase in subfreezing low clouds than what is reported from analysis of CALIPSO data.
Abstract
The properties of clouds derived from measurements collected using a suite of remote sensors on board the Australian R/V Investigator during a 5-week voyage into the Southern Ocean during March and April 2016 are examined. Based on the findings presented in a companion paper (Part I), we focus our attention on a subset of marine boundary layer (MBL) clouds that form a substantial portion of the cloud-coverage fraction. We find that the MBL clouds that dominate the coverage fraction tend to occur in decoupled boundary layers near the base of marine inversions. The thermodynamic conditions under which these clouds are found are reminiscent of marine stratocumulus studied extensively in the subtropical eastern ocean basins except that here they are often supercooled with a rare presence of the ice phase, quite tenuous in terms of their physical properties, rarely drizzling, and tend to occur in migratory high pressure systems in cold-air advection. We develop a simple cloud property retrieval algorithm that uses as input the lidar-attenuated backscatter, the W-band radar reflectivity, and the 31-GHz brightness temperature. We find that the stratocumulus clouds examined have water paths in the 15–25 g m−2 range, effective radii near 8 μm, and number concentrations in the 20 cm−3 range in the Southern Ocean with optical depths in the range of 3–4. We speculate that addressing the high bias in absorbed shortwave radiation in climate models will require understanding the processes that form and maintain these marine stratocumulus clouds in southern mid- and high latitudes.
Abstract
The properties of clouds derived from measurements collected using a suite of remote sensors on board the Australian R/V Investigator during a 5-week voyage into the Southern Ocean during March and April 2016 are examined. Based on the findings presented in a companion paper (Part I), we focus our attention on a subset of marine boundary layer (MBL) clouds that form a substantial portion of the cloud-coverage fraction. We find that the MBL clouds that dominate the coverage fraction tend to occur in decoupled boundary layers near the base of marine inversions. The thermodynamic conditions under which these clouds are found are reminiscent of marine stratocumulus studied extensively in the subtropical eastern ocean basins except that here they are often supercooled with a rare presence of the ice phase, quite tenuous in terms of their physical properties, rarely drizzling, and tend to occur in migratory high pressure systems in cold-air advection. We develop a simple cloud property retrieval algorithm that uses as input the lidar-attenuated backscatter, the W-band radar reflectivity, and the 31-GHz brightness temperature. We find that the stratocumulus clouds examined have water paths in the 15–25 g m−2 range, effective radii near 8 μm, and number concentrations in the 20 cm−3 range in the Southern Ocean with optical depths in the range of 3–4. We speculate that addressing the high bias in absorbed shortwave radiation in climate models will require understanding the processes that form and maintain these marine stratocumulus clouds in southern mid- and high latitudes.
