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Yang Zhao
,
Gerald G. Mace
, and
Jennifer M. Comstock

Abstract

Data collected in midlatitude cirrus clouds by instruments on jet aircraft typically show particle size distributions that have distinct distribution modes in both the 10–30-μm maximum dimension (D) size range and the 200–300-μm D size range or larger. A literal interpretation of the small D mode in these datasets suggests that total concentrations Nt in midlatitude cirrus are, on average, well in excess of 1 cm−3 whereas more conventional analyses of in situ data and cloud process model results suggest Nt values a factor of 10 less. Given this wide discrepancy, questions have been raised regarding the influence of data artifacts caused by the shattering of large crystals on aircraft and probe surfaces. This inconsistency and the general nature of the cirrus particle size distribution are examined using a ground-based remote sensing dataset. An algorithm using millimeter-wavelength radar Doppler moments and Raman lidar-derived extinction is developed to retrieve a bimodal particle size distribution and its uncertainty. This algorithm is applied to case studies as well as to 313 h of cirrus measurements collected at the Atmospheric Radiation Measurement site near Lamont, Oklahoma, in 2000. It is shown that particle size distributions in cirrus can often be described as bimodal, and that this bimodality is a function of temperature and location within cirrus layers. However, the existence of Nt > 1 cm−3 in cirrus is rare (<1% of the time) and the Nt implied by the remote sensing data tends to be on the order of 100 cm−3.

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Gerald G. Mace
,
Min Deng
,
Brian Soden
, and
Ed Zipser

Abstract

In this paper, millimeter cloud radar (MMCR) and Geosynchronous Meteorological Satellite (GMS) data are combined to study the properties of tropical cirrus that are common in the 10–15-km layer of the tropical troposphere in the western Pacific. Millimeter cloud radar observations collected by the Atmospheric Radiation Measurement program on the islands of Manus and Nauru in the western and central equatorial Pacific during a 12-month period spanning 1999 and 2000 show differences in cirrus properties: over Manus, where clouds above 7 km are observed 48% of the time, the cirrus are thicker and warmer on average and the radar reflectivity and Doppler velocity are larger; over Nauru clouds above 7 km are observed 23% of time. To explain the differences in cloud properties, the relationship between tropical cirrus and deep convection is examined by combining the radar observations with GMS satellite-derived back trajectories. Using a data record of 1 yr, it is found that 47% of the cirrus observed over Manus can be traced to a deep convective source within the past 12 h while just 16% of the cirrus observed over Nauru appear to have a convective source within the previous 12 h. Of the cirrus that can be traced to deep convection, the evolution of the radar-observed cloud properties is examined as a function of apparent cloud age. The radar Doppler moments and ice water path of the observed cirrus at both sites generally decrease as the cirrus age increase. At Manus, it is found that cirrus during boreal winter typically advect over the site from the southeast from convection associated with the winter monsoon, while during boreal summer, the trajectories are mainly from the northeast. The properties of these two populations of cirrus are found to be different, with the winter cirrus having higher concentrations of smaller particles. Examining statistics of the regional convection using Tropical Rainfall Measuring Mission (TRMM), it is found that the properties of the winter monsoon convection in the cirrus source region are consistent with more intense convection compared to the convection in the summer source region.

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Steven M. Lazarus
,
Steven K. Krueger
, and
Gerald G. Mace

Abstract

Cloud amount statistics from three different sources were processed and compared. Surface observations from a National Centers for Environmental Prediction dataset were used. The data (Edited Cloud Report; ECR) consist of synoptic weather reports that have been edited to facilitate cloud analysis. Two stations near the Southern Great Plains (SGP) Cloud and Radiation Test Bed (CART) in north-central Oklahoma (Oklahoma City, Oklahoma and Wichita, Kansas) were selected. The ECR data span a 10-yr period from December 1981 to November 1991. The International Satellite Cloud Climatology Project (ISCCP) provided cloud amounts over the SGP CART for an 8-yr period (1983–91). Cloud amounts were also obtained from Micro Pulse Lidar (MPL) and Belfort Ceilometer (BLC) cloud-base height measurements made at the SGP CART over a 1-yr period. The annual and diurnal cycles of cloud amount as a function of cloud height and type were analyzed. The three datasets closely agree for total cloud amount. Good agreement was found in the ECR and MPL–BLC monthly low cloud amounts. With the exception of summer and midday in other seasons, the ISCCP low cloud amount estimates are generally 5%–10% less than the others. The ECR high cloud amount estimates are typically 10%–15% greater than those obtained from either the ISCCP or MPL–BLC datasets. The observed diurnal variations of altocumulus support the authors’ model results of radiatively induced circulations.

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Zhuocan Xu
,
Gerald G. Mace
, and
Derek J. Posselt

Abstract

A Bayesian Markov chain Monte Carlo (MCMC) algorithm is utilized to compare the skill of an A-Train-like observing system with a cloud, convection, and precipitation (CCP) observing system like that contemplated for the 2020s by the 2017 National Academy of Sciences Decadal Survey. The main objective is to demonstrate a framework for observational trade space studies. This initial work focuses on weakly precipitating warm shallow cumulus constructed from in situ data. Radiative computations are based on Mie theory with spherical assumptions. Simulated measurements in the CCP configuration consist of W- and Ka-band radar reflectivity and path-integrated attenuation, 31 and 94 GHz brightness temperatures (Tb), and visible and near-infrared reflectances. The collection of measurements in the CloudSat configuration is identical, but includes a single 94 GHz radar frequency, and the uncertainty in the 94 GHz microwave brightness temperature is increased to mimic the CloudSat Tb product. The experiments demonstrate that it remains a challenge to diagnose cloud properties in the presence of light rain because of the tendency of microwave remote sensing to respond to the higher moments of the hydrometeor populations. Rain properties are significantly better constrained than cloud properties, even in the optimal CCP configuration. The addition of Ka-band measurements places substantial constraints on the precipitation rain effective radius and rain rates. The Tb offers important information regarding the column-integrated condensate mass, the measurement accuracy of which appears more likely to affect the retrievals of clouds with low liquid water path. The constraints provided by reflectances are largely restricted to regions near the cloud top, particularly in the raining cases.

Open access
Roger Marchand
,
Gerald G. Mace
,
Thomas Ackerman
, and
Graeme Stephens

Abstract

In late April 2006, NASA launched Cloudsat, an earth-observing satellite that uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. The first step in using Cloudsat measurements is to distinguish clouds and other hydrometeors from radar noise. In this article the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown. A preliminary comparison of the Cloudsat hydrometeor detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.

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Gerald G. Mace
,
Alain Protat
,
Sally Benson
, and
Paul McGlynn

Abstract

We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi , the mass-mean particle size Dm , and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi , Dm , and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors.

Open access
Derek J. Posselt
,
James Kessler
, and
Gerald G. Mace

Abstract

Retrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

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Gerald G. Mace
,
Eugene E. Clothiaux
, and
Thomas P. Ackerman

Abstract

The properties of midlatitude cirrus clouds are examined using one year of continuous vertically pointing millimeter-wave cloud radar data collected at the Atmospheric Radiation Measurement Program Southern Great Plains site in Oklahoma. The goal of this analysis is to present the cloud characteristics in a manner that will aid in the evaluation and improvement of cirrus parameterizations in large-scale models. Using a temperature- and radar reflectivity–based definition of cirrus, the occurrence frequency of cirrus, the vertical location and thickness of cirrus layers, and other fundamental statistics are examined. Also the bulk microphysical properties of optically thin cirrus layers that occur in isolation from other cloud layers are examined. During 1997, it is found that cirrus were present 22% of the time, had a mean layer thickness of 2.0 km, and were most likely to occur in the 8.5–10-km height range. On average, the cirrus clouds tended to be found in layers in which the synoptic-scale vertical velocity was weakly ascending. The mean synoptic-scale vertical motion in the upper troposphere as derived from Rapid Update Cycle model output was +0.2 cm s−1. However, a significant fraction of the layers (33%) were found where the upper-tropospheric large-scale vertical velocity was clearly descending (w < −1.5 cm s−1). Microphysical properties were computed for that subset of cirrus events that were optically thin (infrared emissivity < 0.85) and occurred with no lower cloud layers. This subset of cirrus had mean values of ice water path, effective radius, and ice crystal concentration of 8 g m−2, 35 μm, and 100 L−1, respectively. Although all the cloud properties demonstrated a high degree of variability during the period considered, the statistics of these properties were fairly steady throughout the annual cycle. Consistent with previous studies, it is found that the cloud microphysical properties appear to be strongly correlated to the cloud layer thickness and mean temperature. Use of these results for parameterization of cirrus properties in large-scale models is discussed.

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Kenneth Sassen
,
Jennifer M. Comstock
,
Zhien Wang
, and
Gerald G. Mace

Since October 1987, the University of Utah Facility for Atmospheric Remote Sensing (FARS) has been applied to the probing of the atmosphere, concentrating on the study of high-level clouds. Regular FARS measurements, which currently total ~3000 h of ruby lidar polarization data, have been directed toward basic cloud research, remote sensing techniques development, and to improving satellite cloud property retrieval methods and GCM predictions by providing climatologically representative cloud datasets and parameterizations. Although the initial studies involved mainly the ruby lidar, the facility has steadily evolved to include a range of visible, infrared, and microwave passive remote sensors, and state-of-the-art, high-resolution dual-wavelength scanning lidar and W-band Doppler radar systems. All three active systems display polarization diversity. In this paper are reviewed the specifications of FARS instrumentation and the research programs to which they have been applied. Four multiple remote sensor case studies of various cloud systems are presented to illustrate the research capabilities. Like a handful of similar sites elsewhere, such research centers dedicated to extended time observation programs have great potential for contributing to atmospheric monitoring and climate research.

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Matthew D. Shupe
,
Jennifer M. Comstock
,
David D. Turner
, and
Gerald G. Mace
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