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P. Kollias
,
E. E. Clothiaux
,
M. A. Miller
,
B. A. Albrecht
,
G. L. Stephens
, and
T. P. Ackerman

During the past 20 yr there has been substantial progress on the development and application of millimeter-wavelength (3.2 and 8.6 mm, corresponding to frequencies of 94 and 35 GHz) radars in atmospheric cloud research, boosted by continuous advancements in radar technology and the need to better understand clouds and their role in the Earth's climate. Applications of millimeter-wavelength radars range from detailed cloud and precipitation process studies to long-term monitoring activities that strive to improve our understanding of cloud processes over a wide range of spatial and temporal scales. These activities are the result of a long period of successful research, starting from the 1980s, in which research tools and sophisticated retrieval techniques were developed, tested, and evaluated in field experiments. This paper presents a cohesive, chronological overview of millimeter-wavelength radar advancements during this period and describes the potential of new applications of millimeter-wavelength radars on sophisticated platforms and the benefits of both lower- and higher-frequency radars for cloud and precipitation research.

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Pavlos Kollias
,
Bruce A. Albrecht
,
Eugene E. Clothiaux
,
Mark A. Miller
,
Karen L. Johnson
, and
Kenneth P. Moran

Abstract

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program operates millimeter-wavelength cloud radars (MMCRs) in several specific locations within different climatological regimes. These vertically pointing cloud profiling radars supply the three most important Doppler spectrum moment estimates, which are the radar reflectivity (or zero moment), the mean Doppler velocity (or first moment), and the Doppler spectrum width (or second moment), as a function of time and height. The ARM MMCR Doppler moment estimates form the basis of a number of algorithms for retrieving cloud microphysical and radiative properties. The retrieval algorithms are highly sensitive to the quality and accuracy of the MMCR Doppler moment estimates. The significance of these sensitivities should not be underestimated, because the inherent physical variability of clouds, instrument-induced noise, and sampling strategy limitations all potentially introduce errors into the Doppler moment estimates. In this article, the accuracies of the first three Doppler moment estimates from the ARM MMCRs are evaluated for a set of typical cloud conditions from the three DOE ARM program sites. Results of the analysis suggest that significant errors in the Doppler moment estimates are possible in the current configurations of the ARM MMCRs. In particular, weakly reflecting clouds with low signal-to-noise ratios (SNRs), as well as turbulent clouds with nonzero updraft and downdraft velocities that are coupled with high SNR, are shown to produce degraded Doppler moment estimates in the current ARM MMCR operational mode processing strategies. Analysis of the Doppler moment estimates and MMCR receiver noise characteristics suggests that the introduction of a set of quality control criteria is necessary for identifying periods of degraded receiver performance that leads to larger uncertainties in the Doppler moment estimates. Moreover, the temporal sampling of the ARM MMCRs was found to be insufficient for representing the actual dynamical states in many types of clouds, especially boundary layer clouds. New digital signal processors (DSPs) are currently being developed for the ARM MMCRs. The findings presented in this study will be used in the design of a new set of operational strategies for the ARM MMCRs once they have been upgraded with the new DSPs.

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Michael P. Jensen
,
James H. Flynn
,
Laura M. Judd
,
Pavlos Kollias
,
Chongai Kuang
,
Greg Mcfarquhar
,
Raj Nadkarni
,
Heath Powers
, and
John Sullivan
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J. Rémillard
,
A. M. Fridlind
,
A. S. Ackerman
,
G. Tselioudis
,
P. Kollias
,
D. B. Mechem
,
H. E. Chandler
,
E. Luke
,
R. Wood
,
M. K. Witte
,
P. Y. Chuang
, and
J. K. Ayers

Abstract

A case study of persistent stratocumulus over the Azores is simulated using two independent large-eddy simulation (LES) models with bin microphysics, and forward-simulated cloud radar Doppler moments and spectra are compared with observations. Neither model is able to reproduce the monotonic increase of downward mean Doppler velocity with increasing reflectivity that is observed under a variety of conditions, but for differing reasons. To a varying degree, both models also exhibit a tendency to produce too many of the largest droplets, leading to excessive skewness in Doppler velocity distributions, especially below cloud base. Excessive skewness appears to be associated with an insufficiently sharp reduction in droplet number concentration at diameters larger than ~200 μm, where a pronounced shoulder is found for in situ observations and a sharp reduction in reflectivity size distribution is associated with relatively narrow observed Doppler spectra. Effectively using LES with bin microphysics to study drizzle formation and evolution in cloud Doppler radar data evidently requires reducing numerical diffusivity in the treatment of the stochastic collection equation; if that is accomplished sufficiently to reproduce typical spectra, progress toward understanding drizzle processes is likely.

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Katia Lamer
,
Edward P. Luke
,
Brian Walsh Jr.
,
Steven Andrade
,
Zackary Mages
,
Zeen Zhu
,
Erin Leghart
,
Bernat P. Treserras
,
Ann Emrick
,
Pavlos Kollias
,
Andrew Vogelmann
, and
Martin Schoonen

Abstract

The Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step toward this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties, including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY, reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY, offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

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Pavlos Kollias
,
Eugene E. Clothiaux
,
Thomas P. Ackerman
,
Bruce A. Albrecht
,
Kevin B. Widener
,
Ken P. Moran
,
Edward P. Luke
,
Karen L. Johnson
,
Nitin Bharadwaj
,
James B. Mead
,
Mark A. Miller
,
Johannes Verlinde
,
Roger T. Marchand
, and
Gerald G. Mace
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A Focus On Mixed-Phase Clouds

The Status of Ground-Based Observational Methods

Matthew D. Shupe
,
John S. Daniel
,
Gijs de Boer
,
Edwin W. Eloranta
,
Pavlos Kollias
,
Charles N. Long
,
Edward P. Luke
,
David D. Turner
, and
Johannes Verlinde

The phase composition and microphysical structure of clouds define the manner in which they modulate atmospheric radiation and contribute to the hydrologic cycle. Issues regarding cloud phase partitioning and transformation come to bear directly in mixed-phase clouds, and have been difficult to address within current modeling frameworks. Ground-based, remote-sensing observations of mixed-phase clouds can contribute a significant body of knowledge with which to better understand, and thereby more accurately model, clouds and their phase-defining processes. Utilizing example observations from the Mixed-Phase Arctic Cloud Experiment (M-PACE), which occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Climate Research Facility in Barrow, Alaska, during autumn 2004, we review the current status of ground-based observation and retrieval methods used in characterizing the macrophysical, microphysical, radiative, and dynamical properties of stratiform mixed-phase clouds. In general, cloud phase, boundaries, ice properties, liquid water path, optical depth, and vertical velocity are available from a combination of active and passive sensors. Significant deficiencies exist in our ability to vertically characterize the liquid phase, to distinguish ice crystal habits, and to understand aerosol-cloud interactions. Further validation studies are needed to evaluate, improve, and expand our retrieval abilities in mixed-phase clouds.

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Virendra P. Ghate
,
Pavlos Kollias
,
Susanne Crewell
,
Ann M. Fridlind
,
Thijs Heus
,
Ulrich Löehnert
,
Maximilian Maahn
,
Greg M. McFarquhar
,
Dmitri Moisseev
,
Mariko Oue
,
Manfred Wendisch
, and
Christopher Williams
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P. Kollias
,
N. Bharadwaj
,
E. E. Clothiaux
,
K. Lamer
,
M. Oue
,
J. Hardin
,
B. Isom
,
I. Lindenmaier
,
A. Matthews
,
E. P. Luke
,
S. E. Giangrande
,
K. Johnson
,
S. Collis
,
J. Comstock
, and
J. H. Mather

Abstract

Improving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud–aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities toward providing holistic and multiscale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. In addition, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. Although the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data–model integration methods. The overarching goal is to provide a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes.

Free access
P. Kollias
,
N. Bharadwaj
,
E. E. Clothiaux
,
K. Lamer
,
M. Oue
,
J. Hardin
,
B. Isom
,
I. Lindenmaier
,
A. Matthews
,
E. P. Luke
,
S. E. Giangrande
,
K. Johnson
,
S. Collis
,
J. Comstock
, and
J. H. Mather
Full access