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  • Author or Editor: G. G. Mace x
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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
,
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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E. E. Clothiaux
,
G. G. Mace
,
T. P. Ackerman
,
T. J. Kane
,
J. D. Spinhirne
, and
V. S. Scott

Abstract

A cloud detection algorithm for a low power micropulse lidar is presented that attempts to identify all of the significant power returns from the vertical column above the lidar at all times. The main feature of the algorithm is construction of lidar power return profiles during periods of clear sky against which cloudy-sky power returns are compared. This algorithm supplements algorithms designed to detect cloud-base height in that the tops of optically thin clouds are identified and it provides an alternative approach to algorithms that identify significant power returns by analysis of changes in the slope of the backscattered powers with height. The cloud-base heights produced by the current algorithm during nonprecipitating periods are comparable with the results of a cloud-base height algorithm applied to the same data. Although an objective validation of algorithm performance on high, thin cirrus is lacking because of no truth data, the current algorithm produces few false positive and false negative classifications as determined through manual comparison of the original photoelectron count data to the final cloud mask image.

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Sergey Y. Matrosov
,
Gerald G. Mace
,
Roger Marchand
,
Matthew D. Shupe
,
Anna G. Hallar
, and
Ian B. McCubbin

Abstract

Scanning polarimetric W-band radar data were evaluated for the purpose of identifying predominant ice hydrometeor habits. Radar and accompanying cloud microphysical measurements were conducted during the Storm Peak Laboratory Cloud Property Validation Experiment held in Steamboat Springs, Colorado, during the winter season of 2010/11. The observed ice hydrometeor habits ranged from pristine and rimed dendrites/stellars to aggregates, irregulars, graupel, columns, plates, and particle mixtures. The slant 45° linear depolarization ratio (SLDR) trends as a function of the radar elevation angle are indicative of the predominant hydrometeor habit/shape. For planar particles, SLDR values increase from values close to the radar polarization cross coupling of about −21.8 dB at zenith viewing to maximum values at slant viewing. These maximum values depend on predominant aspect ratio and bulk density of hydrometeors and also show some sensitivity to particle characteristic size. The highest observed SLDRs were around −8 dB for pristine dendrites. Unlike planar-type hydrometeors, columnar-type particles did not exhibit pronounced depolarization trends as a function of viewing direction. A difference in measured SLDR values between zenith and slant viewing can be used to infer predominant aspect ratios of planar hydrometeors if an assumption about their bulk density is made. For columnar hydrometeors, SLDR offsets from the cross-coupling value are indicative of aspect ratios. Experimental data were analyzed for a number of events with prevalence of planar-type hydrometeors and also for observations when columnar particles were the dominant species. A relatively simple spheroidal model and accompanying T-matrix calculations were able to approximate most radar depolarization changes with viewing angle observed for different hydrometeor types.

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Roger Marchand
,
Gerald G. Mace
,
A. Gannet Hallar
,
Ian B. McCubbin
,
Sergey Y. Matrosov
, and
Matthew D. Shupe

Abstract

Nonspherical atmospheric ice particles can enhance radar backscattering and attenuation above that expected from spheres of the same mass. An analysis of scanning 95-GHz radar data collected during the Storm Peak Laboratory Cloud Property Validation Experiment (StormVEx) shows that at a least a small amount of enhanced backscattering was present in most radar scans, with a median enhancement of 2.4 dB at zenith. This enhancement will cause an error (bias) in ice water content (IWC) retrievals that neglect particle orientation, with a value of 2.4 dB being roughly equivalent to a relative error in IWC of 43%. Of the radar scans examined, 25% had a zenith-enhanced backscattering exceeding 3.5 dB (equivalent to a relative error in IWC in excess of 67%) and 10% of the scans had a zenith-enhanced backscattering exceeding 6.4 dB (equivalent to a relative error in IWC in excess of 150%). Cloud particle images indicate that large enhancement typically occurred when planar crystals (e.g., plates and dendrites) were present, with the largest enhancement occurring when large planar crystals were falling out of a supercooled liquid-water layer. More modest enhancement was sometimes due to planar crystals, but it was also sometimes likely a result of horizontally oriented nonspherical irregularly shaped particles. The analysis also shows there is a strong correlation (about −0.79) between the change in slant 45° depolarization ratio with radar scan elevation angle and the magnitude of the zenith-enhanced backscattering, suggesting that measurements of the slant depolarization ratio can be used to improve radar-based cloud microphysical property retrievals.

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Eugene E. Clothiaux
,
Kenneth P. Moran
,
Brooks E. Martner
,
Thomas P. Ackerman
,
Gerald G. Mace
,
Taneil Uttal
,
James H. Mather
,
Kevin B. Widener
,
Mark A. Miller
, and
Daniel J. Rodriguez

Abstract

During the past decade, the U.S. Department of Energy (DOE), through the Atmospheric Radiation Measurement (ARM) Program, has supported the development of several millimeter-wavelength radars for the study of clouds. This effort has culminated in the development and construction of a 35-GHz radar system by the Environmental Technology Laboratory (ETL) of the National Oceanic and Atmospheric Administration (NOAA). Radar systems based on the NOAA ETL design are now operating at the DOE ARM Southern Great Plains central facility in central Oklahoma and the DOE ARM North Slope of Alaska site near Barrow, Alaska. Operational systems are expected to come online within the next year at the DOE ARM tropical western Pacific sites located at Manus, Papua New Guinea, and Nauru. In order for these radars to detect the full range of atmospheric hydrometeors, specific modes of operation must be implemented on them that are tuned to accurately detect the reflectivities of specific types of hydrometeors. The set of four operational modes that are currently in use on these radars are presented and discussed. The characteristics of the data produced by these modes of operation are also presented in order to illustrate the nature of the cloud products that are, and will be, derived from them on a continuous basis.

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