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Yuying Zhang and Gerald G. Mace

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.

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Gerald G. Mace and Alain Protat

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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.

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Gerald G. Mace and Alain Protat

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.

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Elizabeth Berry and Gerald G. Mace

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.

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Zhuocan Xu and Gerald G. Mace

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 bm) 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.

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Min Deng and Gerald G. Mace

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.

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Xiquan Dong and Gerald G. Mace

Abstract

A record of single-layer and overcast low-level Arctic stratus cloud properties has been generated using data collected from May to September 2000 at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) (71.3°N, 156.6°W) site near Barrow, Alaska. The record includes liquid-phase and liquid dominant mixed-phase Arctic stratus macrophysical, microphysical, and radiative properties, as well as surface radiation budget and cloud radiative forcing. The macrophysical properties consist of cloud fractions, cloud-base/top heights and temperatures, and cloud thickness derived from a ground-based radar and lidar pair, and rawinsonde sounding. The microphysical properties include cloud liquid water path and content, and cloud-droplet effective radius and number concentration obtained from microwave radiometer brightness temperature measurements, and the new cloud parameterization. The radiative properties contain cloud optical depth, effective solar transmission, and surface/cloud/top-of-atmosphere albedos derived from the new cloud parameterization and standard Epply precision spectral pyranometers. The shortwave, longwave, and net cloud radiative forcings at the surface are inferred from measurements by standard Epply precision spectral pyranometers and pyrgeometers. There are approximately 300 h and more than 3600 samples (5-min resolution) of single-layer and overcast low-level stratus during the study period. The 10-day averaged total and low-level cloud (Z top < 3 km) fractions are 0.87 and 0.55, and low-level cloud-base and -top heights are around 0.4 and 0.8 km. The cloud-droplet effective radii and number concentrations in the spring are similar to midlatitude continental stratus cloud microphysical properties, and in the summer they are similar to midlatitude marine stratus clouds. The total cloud fractions in this study show good agreement with the satellite and surface results compiled from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Cloud Experiment (ACE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) (∼77°N, 165°W) field experiments in 1998. The cloud microphysics derived from this study are similar, in general, to those collected in past field programs, although these comparisons are based on data collected at different locations and years. At the ARM NSA site, the summer cooling period is much longer (2–3 months vs 1–2 weeks), and the summer cooling magnitude is much larger (−100 W m−2 vs −5 W m−2) than at the SHEBA ship under the conditions of all skies at the SHEBA and overcast low-level stratus clouds at the NSA site.

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Min Deng and Gerald G. Mace

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.

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Xiquan Dong and Gerald G. Mace

Abstract

The microwave radiometer–derived cloud liquid water path (LWP) and a profile of radar reflectivity are used to derive a profile of cloud liquid water content (LWC). Two methods (M1 and M2) have been developed for inferring the profile of cloud-droplet effective radius (r e) in liquid phase or liquid dominant mixed phase stratocumulus clouds. The M1-inferred r e profile is proportional to a previously derived layer-mean r e and to the ratio of the radar reflectivity to the integrated radar reflectivity. This algorithm is independent of the radar calibration and is applicable to overcast low-level stratus clouds that occur during the day because it is dependent on solar transmission observations. In order to extend the retrieval algorithm to a wider range of conditions, a second method is described that uses an empirical relationship between effective radius and radar reflectivity based on theory and the results of M1. Sensitivity studies show that the surface-retrieved r e is more sensitive to the variation of radar reflectivity when the radar reflectivity is large, and the uncertainties of retrieved r e related to the assumed vertically constant cloud-droplet number concentration and shape of the size distribution are about 9% and 2%, respectively. For validation, a total of 10 h of aircraft data and 36 h of surface data were collected over the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site during the March 2000 cloud intensive observational period (IOP). More detailed comparisons in two cases quantify the agreement between the aircraft data and the surface retrievals. When the temporal averages of the two datasets increase from 1 min to 30 min, the means and standard deviations of differences between the two datasets decrease from −2.5% ± 84% to 1.3% ± 42.6% and their corresponding correlation coefficients increase from 0.47 to 0.8 for LWC; and decrease from −4.8% ± 36.4% to −3.3% ± 22.5% with increased coefficients from 0.64 to 0.94 for r e (both M1 and M2). The agreement between the aircraft and surface data in the 30-min averages suggests that the two platforms are capable of characterizing the cloud microphysics over this temporal scale. On average, the surface retrievals are unbiased relative to the aircraft in situ measurements. However, when only the 1-min averaged aircraft data within 3 km of the surface site were selected, the means and standard deviations of differences between the two datasets are larger (23.4% ± 113% for LWC and 28.3% ± 60.7% for r e) and their correlation coefficients are smaller (0.32 for LWC and 0.3 for r e) than those from all 1-min samples. This result suggests that restricting the comparison to the samples better matched in space and time between the surface and aircraft data does not result in a better comparison.

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Gerald G. Mace and Sally Benson

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Data collected at the Atmospheric Radiation Measurement (ARM) Program ground sites allow for the description of the atmospheric thermodynamic state, cloud occurrence, and cloud properties. This information allows for the derivation of estimates of the effects of clouds on the radiation budget of the surface and atmosphere. Herein 8 yr of continuous data collected at the ARM Southern Great Plains (SGP) Climate Research Facility (ACRF) are analyzed, and the influence of clouds on the radiative flux divergence of solar and infrared energy on annual, seasonal, and monthly time scales is documented. Given the uncertainties in derived cloud microphysical properties that result in calculated radiant flux errors, it is demonstrated that the ability to quantitatively resolve all but the largest heating and cooling influences by clouds is marginal for averaging periods less than 1 month. Concentrating on seasonal and monthly averages, it is found that the net column-integrated radiative effect of clouds on the atmosphere is nearly neutral at this middle-latitude location. However, a net heating of the upper troposphere by upper-tropospheric clouds and a cooling of the lower troposphere by boundary layer clouds is documented. The balance evolves over the course of an annual cycle as the troposphere deepens in summer and boundary layer clouds become less frequent relative to upper-tropospheric clouds. Although the top-of-atmosphere IR radiative effect is nearly invariant through the annual cycle, the seasonally varying heating profile is determined largely by the convergence of IR flux because solar heating is offset by IR cooling within the column.

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