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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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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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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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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
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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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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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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Bryan A. Baum
,
Richard A. Frey
,
Gerald G. Mace
,
Monica K. Harkey
, and
Ping Yang

Abstract

This study reports on recent progress toward the discrimination between pixels containing multilayered clouds, specifically optically thin cirrus overlying lower-level water clouds, and those containing single-layered clouds in nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data. Cloud heights are determined from analysis of the 15-μm CO2 band data (i.e., the CO2-slicing method). Cloud phase is inferred from the MODIS operational bispectral technique using the 8.5- and 11-μm IR bands. Clear-sky pixels are identified from application of the MODIS operational cloud-clearing algorithm. The primary assumption invoked is that over a relatively small spatial area, it is likely that two cloud layers exist with some areas that overlap in height. The multilayered cloud pixels are identified through a process of elimination, where pixels from single-layered upper and lower cloud layers are eliminated from the data samples. For two case studies (22 April 2001 and 28 March 2001), ground-based lidar and radar observations are provided by the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) Clouds and Radiation Test Bed (CART) site in Oklahoma. The surface-based cloud observations provide independent information regarding the cloud layering and cloud height statistics in the time period surrounding the MODIS overpass.

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Gerald G. Mace
,
Yuying Zhang
,
Steven Platnick
,
Michael D. King
,
Patrick Minnis
, and
Ping Yang

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite has been collecting global data since March 2000 and the one on the Aqua satellite since June 2002. In this paper, cirrus cloud properties derived from ground-based remote sensing data are compared with similar cloud properties derived from MODIS data on Terra. To improve the space–time correlation between the satellite and ground-based observations, data from a wind profiler are used to define the cloud advective streamline along which the comparisons are made. In this paper, approximately two dozen cases of cirrus are examined and a statistical approach to the comparison that relaxes the requirement that clouds occur over the ground-based instruments during the overpass instant is explored. The statistical comparison includes 168 cloudy MODIS overpasses of the Southern Great Plains (SGP) region and approximately 300 h of ground-based cirrus observations. The physical and radiative properties of cloud layers are derived from MODIS data separately by the MODIS Atmospheres Team and the Clouds and the Earth’s Radiant Energy System (CERES) Science Team using multiwavelength reflected solar and emitted thermal radiation measurements. Using two ground-based cloud property retrieval algorithms and the two MODIS algorithms, a positive correlation in the effective particle size, the optical thickness, the ice water path, and the cloud-top pressure between the various methods is shown, although sometimes there are significant biases. Classifying the clouds by optical thickness, it is demonstrated that the regionally averaged cloud properties derived from MODIS are similar to those diagnosed from the ground. Because of a conservative approach toward identifying thin cirrus pixels over this region, the area-averaged cloud properties derived from the MODIS Atmospheres MOD06 product tend to be biased slightly toward the optically thicker pixels. This bias tendency has implications for model validation and parameterization development applied to thin cirrus retrieved over SGP-like land surfaces. A persistent bias is also found in the derived cloud tops of thin cirrus with both satellite algorithms reporting cloud top several hundred meters less than that reported by the cloud radar. Overall, however, it is concluded that the MODIS retrieval algorithms characterize with reasonable accuracy the properties of thin cirrus over this region.

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