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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
,
Thomas P. Ackerman
,
Gerald G. Mace
,
Kenneth P. Moran
,
Roger T. Marchand
,
Mark A. Miller
, and
Brooks E. Martner

Abstract

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program is deploying sensitive, millimeter-wave cloud radars at its Cloud and Radiation Test Bed (CART) sites in Oklahoma, Alaska, and the tropical western Pacific Ocean. The radars complement optical devices, including a Belfort or Vaisala laser ceilometer and a micropulse lidar, in providing a comprehensive source of information on the vertical distribution of hydrometeors overhead at the sites. An algorithm is described that combines data from these active remote sensors to produce an objective determination of hydrometeor height distributions and estimates of their radar reflectivities, vertical velocities, and Doppler spectral widths, which are optimized for accuracy. These data provide fundamental information for retrieving cloud microphysical properties and assessing the radiative effects of clouds on climate. The algorithm is applied to nine months of data from the CART site in Oklahoma for initial evaluation. Much of the algorithm’s calculations deal with merging and optimizing data from the radar’s four sequential operating modes, which have differing advantages and limitations, including problems resulting from range sidelobes, range aliasing, and coherent averaging. Two of the modes use advanced phase-coded pulse compression techniques to yield approximately 10 and 15 dB more sensitivity than is available from the two conventional pulse modes. Comparison of cloud-base heights from the Belfort ceilometer and the micropulse lidar confirms small biases found in earlier studies, but recent information about the ceilometer brings the agreement to within 20–30 m. Merged data of the radar’s modes were found to miss approximately 5.9% of the clouds detected by the laser systems. Using data from only the radar’s two less-sensitive conventional pulse modes would increase the missed detections to 22%–34%. A significant remaining problem is that the radar’s lower-altitude data are often contaminated with echoes from nonhydrometeor targets, such as insects.

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Maximilian Maahn
,
David D. Turner
,
Ulrich Löhnert
,
Derek J. Posselt
,
Kerstin Ebell
,
Gerald G. Mace
, and
Jennifer M. Comstock

Abstract

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

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Peter T. May
,
James H. Mather
,
Geraint Vaughan
,
Christian Jakob
,
Greg M. McFarquhar
,
Keith N. Bower
, and
Gerald G. Mace

A comprehensive dataset describing tropical cloud systems and their environmental setting and impacts has been collected during the Tropical Warm Pool International Cloud Experiment (TWPICE) and Aerosol and Chemical Transport in Tropical Convection (ACTIVE) campaign in the area around Darwin, Northern Australia, in January and February 2006. The aim of the experiment was to observe the evolution of tropical cloud systems and their interaction with the environment within an observational framework optimized for a range of modeling activities with the goal of improving the representation of cloud and aerosol process in a range of models. The experiment design utilized permanent observational facilities in Darwin, including a polarimetric weather radar and a suite of cloud remote-sensing instruments. This was augmented by a dense network of soundings, together with radiation, flux, lightning, and remote-sensing measurements, as well as oceanographic observations. A fleet of five research aircraft, including two high-altitude aircraft, were taking measurements of fluxes, cloud microphysics, and chemistry; cloud radar and lidar were carried on a third aircraft. Highlights of the experiment include an intense mesoscale convective system (MCS) developed within the network, observations used to analyze the impacts of aerosol on convective systems, and observations used to relate cirrus properties to the parent storm properties.

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Peter T. May
,
James H. Mather
,
Geraint Vaughan
,
Keith N. Bower
,
Christian Jakob
,
Greg M. McFarquhar
, and
Gerald G. Mace
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THE CLOUDSAT MISSION AND THE A-TRAIN

A New Dimension of Space-Based Observations of Clouds and Precipitation

Graeme L. Stephens
,
Deborah G. Vane
,
Ronald J. Boain
,
Gerald G. Mace
,
Kenneth Sassen
,
Zhien Wang
,
Anthony J. Illingworth
,
Ewan J. O'connor
,
William B. Rossow
,
Stephen L. Durden
,
Steven D. Miller
,
Richard T. Austin
,
Angela Benedetti
,
Cristian Mitrescu
, and
the CloudSat Science Team

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASA's Aqua and Aura satellites, a NASA–CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle.

The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.

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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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Kenneth Sassen
,
David O'C. Starr
,
Gerald G. Mace
,
Michael R. Poellot
,
S.H. Melfi
,
Wynn L. Eberhard
,
James D. Spinhirne
,
E.W. Eloranta
,
Donald E. Hagen
, and
John Hallett

Abstract

In presenting an overview of the cirrus clouds comprehensively studied by ground-based and airborne sensors from Coffeyville, Kansas, during the 5–6 December 1992 Project FIRE IFO II case study period, evidence is provided that volcanic aerosols from the June 1991 Pinatubo eruptions may have significantly influenced the formation and maintenance of the cirrus. Following the local appearance of a spur of stratospheric volcanic debris from the subtropics, a series of jet streaks subsequently conditioned the troposphere through tropopause foldings with sulfur-based particles that became effective cloud-forming nuclei in cirrus clouds. Aerosol and ozone measurements suggest a complicated history of stratospheric-tropospheric exchanges embedded within the upper-level flow, and cirrus cloud formation was noted to occur locally at the boundaries of stratospheric aerosol-enriched layers that became humidified through diffusion, precipitation, or advective processes. Apparent cirrus cloud alterations include abnormally high ice crystal concentrations (up to ∼600 L−1), complex radial ice crystal types, and relatively large haze particles in cirrus uncinus cell heads at temperatures between −40° and −50°C. Implications for volcanic-cirrus cloud climate effects and usual (nonvolcanic aerosol) jet stream cirrus cloud formation are discussed.

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Jennifer M. Comstock
,
Robert d'Entremont
,
Daniel DeSlover
,
Gerald G. Mace
,
Sergey Y. Matrosov
,
Sally A . McFarlane
,
Patrick Minnis
,
David Mitchell
,
Kenneth Sassen
,
Matthew D. Shupe
,
David D. Turner
, and
Zhien Wang

The large horizontal extent, with its location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the Earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud-scale processes in largescale models and for accurately predicting the Earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper-tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement (ARM) program Cloud Properties Working Group are involved in an intercomparison of optical depth τ and ice water path in ice clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for their improvement.

Currently, there are significant discrepancies among the algorithms for ice clouds with very small optical depths (τ < 0.3) and those with 1 < τ < 5. The good news is that for thin clouds (0.3 < τ < 1), the algorithms tend to converge. In this first stage of the intercomparison, we present results from a representative case study, compare the retrieved cloud properties with aircraft and satellite measurements, and perform a radiative closure experiment to begin gauging the accuracy of these retrieval algorithms.

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D.L. Westphal
,
S. Kinne
,
P. Pilewskie
,
J.M. Alvarez
,
P. Minnis
,
D.F. Young
,
S.G. Benjamin
,
W.L. Eberhard
,
R.A. Kropfli
,
S.Y. Matrosov
,
J.B. Snider
,
T.A. Uttal
,
A.J. Heymsfield
,
G.G. Mace
,
S.H. Melfi
,
D.O'C. Starr
, and
J.J. Soden

Abstract

Observations from a wide variety of instruments and platforms are used to validate many different aspects of a three-dimensional mesoscale simulation of the dynamics, cloud microphysics, and radiative transfer of a cirrus cloud system observed on 26 November 1991 during the second cirrus field program of the First International Satellite Cloud Climatology Program (ISCCP) Regional Experiment (FIRE-II) located in southeastern Kansas. The simulation was made with a mesoscale dynamical model utilizing a simplified bulk water cloud scheme and a spectral model of radiative transfer. Expressions for cirrus optical properties for solar and infrared wavelength intervals as functions of ice water content and effective particle radius are modified for the midlatitude cirrus observed during FIRE-II and are shown to compare favorably with explicit size-resolving calculations of the optical properties. Rawinsonde, Raman lidar, and satellite data are evaluated and combined to produce a time–height cross section of humidity at the central FIRE-II site for model verification. Due to the wide spacing of rawinsondes and their infrequent release, important moisture features go undetected and are absent in the conventional analyses. The upper-tropospheric humidities used for the initial conditions were generally less than 50% of those inferred from satellite data, yet over the course of a 24-h simulation the model produced a distribution that closely resembles the large-scale features of the satellite analysis. The simulated distribution and concentration of ice compares favorably with data from radar, lidar, satellite, and aircraft. Direct comparison is made between the radiative transfer simulation and data from broadband and spectral sensors and inferred quantities such as cloud albedo, optical depth, and top-of-the-atmosphere 11-µm brightness temperature, and the 6.7-µm brightness temperature. Comparison is also made with theoretical heating rates calculated using the rawinsonde data and measured ice water size distributions near the central site. For this case study, and perhaps for most other mesoscale applications, the differences between the observed and simulated radiative quantities are due more to errors in the prediction of ice water content, than to errors in the optical properties or the radiative transfer solution technique.

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