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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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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 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.

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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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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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Min Deng
,
Gerald. G. Mace
, and
Zhien Wang

Abstract

The anvil productivities of tropical deep convection are investigated and compared among eight climatological regions using 4 yr of collocated and combined CloudSat and CALIPSO data. For all regions, the convective clusters become deeper while they become wider and tend to be composed of multiple rainy cores. Two strong detrainment layers from deep convection are observed at 6–8 km and above 10 km, which is consistent with the trimodal characteristics of tropical convection that are associated with different divergence, cloud detrainment, and fractional cloudiness. The anvil productivity of tropical deep convection depends on the convection scale, convective life stage or intensity, and large-scale environment. Anvil ice mass ratio related to the whole cluster starts to level off or decrease when the cluster effective scales W eff (the dimension of an equivalent rectangular with the same volume and height as the original cluster) increase to about 200 km wide, while the ratios of anvil scale and volume keep increasing from 0.4 to 0.6 and 0.15 to 0.4, respectively. The anvil clouds above 12 km can count for more than 20% of cluster volume, or more than 50% of total anvil volume, but they only count less than about 2% of total ice mass in the cluster. Anvil production of younger convection of the same W eff is higher than that of the decaying convection. The regional difference in the composite anvil productivities of tropical convective clusters sorted by W eff is subtle, while the occurrence frequencies of different scales of convection vary substantially.

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Elizabeth Berry
,
Gerald G. Mace
, and
Andrew Gettelman

Abstract

The distribution of clouds and their radiative effects in the Community Atmosphere Model, version 5 (CAM5), are compared to A-Train satellite data in Southeast Asia during the summer monsoon. Cloud radiative kernels are created based on populations of observed and modeled clouds separately in order to compare the sensitivity of the TOA radiation to changes in cloud fraction. There is generally good agreement between the observation- and model-derived cloud radiative kernels for most cloud types, meaning that the clouds in the model are heating and cooling like clouds in nature. Cloud radiative effects are assessed by multiplying the cloud radiative kernel by the cloud fraction histogram. For ice clouds in particular, there is good agreement between the model and observations, with optically thin cirrus producing a moderate warming effect and cirrostratus producing a slight cooling effect, on average. Consistent with observations, the model also shows that the median value of the ice water path (IWP) distribution, rather than the mean, is a more representative measure of the ice clouds that are responsible for heating. In addition, in both observations and the model, it is cirrus clouds with an IWP of 20 g m−2 that have the largest warming effect in this region, given their radiative heating and frequency of occurrence.

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Elizabeth Berry
,
Gerald G. Mace
, and
Andrew Gettelman

Abstract

Using information from the A-Train satellites, the properties and radiative effects of eastern Pacific Ocean boundary layer clouds are evaluated in the Community Atmosphere Model, version 5 (CAM5), from the summer of 2007 and 2008. The cloud microphysical properties are inferred using measurements from CloudSat and CALIPSO (CC) that are then used to calculate the broadband radiative flux profiles. Accounting appropriately for sampling differences between the measurements and the simulation, evidence of the “too few, too bright” low cloud bias is found in CAM5. Single-layer low clouds have a frequency of occurrence of 42% from CC, as compared with just 29% in CAM5, and the averaged cloud radiative kernel (CRK) for the model shows stronger cooling. For stratocumulus in particular, the cooling in the model CRK is larger by a factor of 2 relative to the observations, implying an overly sensitive tropical low cloud feedback. Differences in the day/night occurrence of stratocumulus help to explain some of the difference in the CRK. The cloud-type microphysics for liquid clouds is represented reasonably well by the model, with a tendency for smaller water paths and smaller effective radii. Overall, the occurrence and CRK have partially compensating errors such that the net cooling at the top of the atmosphere for eastern Pacific low clouds is −43 W m−2 in CAM5, as compared with −32 W m−2 from CC. The cooling effect in the model is accomplished by fewer low clouds with a narrower range of properties, as compared with more clouds with a broader range of properties in the observation-based dataset.

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Gerald G. Mace
,
Sally Benson
, and
Erik Vernon

Abstract

The properties of cirrus clouds observed at the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) in Oklahoma are documented from a nearly continuous 6-yr record of 35-GHz cloud radar data. Cirrus frequency over the ACRF is 23% and 28% of the time in the warm (May–September) and cold seasons (November–March), respectively, with maxima and minima during the period studied of 30% and 16% in the warm season and 34% and 24% in the cold seasons. Cirrus, as defined here, reveal a seasonal oscillation in their macroscale properties that can be traced to the seasonal deepening of the troposphere in the Southern Plains region. While the average bulk microphysical properties do not change significantly from season to season, the variability of certain parameters demonstrates seasonal change. It is shown that the properties of cirrus clouds vary perceptively with the large-scale vertical motion. Using NCEP–NCAR reanalysis data to define the large-scale meteorological state when cirrus are observed at the ACRF, the authors find that cirrus tend to exist within a maximum in upper-tropospheric humidity and downstream of the peak upper-tropospheric vertical motion. Cirrus that exist in large-scale ascent upstream of the synoptic-scale middle-tropospheric ridge axis are shown to have higher water contents than cirrus that exist in large-scale subsidence downstream of the ridge axis, although the overall nature of the statistical distributions of water contents do not change greatly, suggesting that it may be difficult to parameterize the properties of cirrus based solely on large-scale vertical motion. The layer-mean particle size, on the other hand, shows no such sensitivity to the large-scale vertical motion.

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Kevin D. Hammonds
,
Gerald G. Mace
, and
Sergey Y. Matrosov

Abstract

One of the challenges that limit the amount of information that can be inferred from radar measurements of ice and mixed-phase precipitating clouds is the variability in ice mass within hydrometeors. The variable amount of ice mass within particles of a given size drives further variability in single-scattering properties that results in uncertainties of forward-modeled remote sensing quantities. Nonspherical ice-phase hydrometeors are often approximated as spheroids to simplify the calculation of single-scattering properties, yet offline calculations remain necessary to quantify these radiative properties as a function of size in discrete increments. In this paper, a simple scaling of the Clausius–Mossotti factor is used that allows for an approximation of the scattering and extinction cross sections for an arbitrary mass–dimensional power-law relationship of a nonspherical particle given a single T-matrix calculation. Using data collected by the University of Wyoming King Air in snow clouds over the Colorado Park Range, the uncertainty in forward-modeled radar reflectivity to assumptions regarding mass–dimensional relationships is examined. This is accomplished by taking advantage of independently measured condensed mass and particle size distributions to estimate the variability of the prefactor in the mass–dimensional power law. Then, calculating the partial derivative of the radar backscatter cross sections using the scaling relationships, an estimate is made of the statistical uncertainty in forward-modeled radar reflectivity. Uncertainties on the order of 4 dB are found in this term for the dataset considered.

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Min Deng
,
Gerald G. Mace
,
Zhien Wang
, and
R. Paul Lawson

Abstract

In this study several ice cloud retrieval products that utilize active and passive A-Train measurements are evaluated using in situ data collected during the Small Particles in Cirrus (SPARTICUS) field campaign. The retrieval datasets include ice water content (IWC), effective radius re , and visible extinction σ from CloudSat level-2C ice cloud property product (2C-ICE), CloudSat level-2B radar-visible optical depth cloud water content product (2B-CWC-RVOD), radar–lidar (DARDAR), and σ from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). When the discrepancies between the radar reflectivity Ze derived from 2D stereo probe (2D-S) in situ measurements and Ze measured by the CloudSat radar are less than 10 dBZe , the flight mean ratios of the retrieved IWC to the IWC estimated from in situ data are 1.12, 1.59, and 1.02, respectively for 2C-ICE, DARDAR, and 2B-CWC-RVOD. For re , the flight mean ratios are 1.05, 1.18, and 1.61, respectively. For σ, the flight mean ratios for 2C-ICE, DARDAR, and CALIPSO are 1.03, 1.42, and 0.97, respectively. The CloudSat 2C-ICE and DARDAR retrieval products are typically in close agreement. However, the use of parameterized radar signals in ice cloud volumes that are below the detection threshold of the CloudSat radar in the 2C-ICE algorithm provides an extra constraint that leads to slightly better agreement with in situ data. The differences in assumed mass–size and area–size relations between CloudSat 2C-ICE and DARDAR also contribute to some subtle difference between the datasets: re from the 2B-CWC-RVOD dataset is biased more than the other retrieval products and in situ measurements by about 40%. A slight low (negative) bias in CALIPSO σ may be due to 5-km averaging in situations in which the cirrus layers have significant horizontal gradients in σ.

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