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David D. Turner, P. Jonathan Gero, and David C. Tobin
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Timothy J. Wagner, Petra M. Klein, and David D. Turner

Abstract

Mobile systems equipped with remote sensing instruments capable of simultaneous profiling of temperature, moisture, and wind at high temporal resolutions can offer insights into atmospheric phenomena that the operational network cannot. Two recently developed systems, the Space Science and Engineering Center (SSEC) Portable Atmospheric Research Center (SPARC) and the Collaborative Lower Atmosphere Profiling System (CLAMPS), have already experienced great success in characterizing a variety of phenomena. Each system contains an Atmospheric Emitted Radiance Interferometer for thermodynamic profiling and a Halo Photonics Stream Line Doppler wind lidar for kinematic profiles. These instruments are augmented with various in situ and remote sensing instruments to provide a comprehensive assessment of the evolution of the lower troposphere at high temporal resolution (5 min or better). While SPARC and CLAMPS can be deployed independently, the common instrument configuration means that joint deployments with well-coordinated data collection and analysis routines are easily facilitated.

In the past several years, SPARC and CLAMPS have participated in numerous field campaigns, which range from mesoscale campaigns that require the rapid deployment and teardown of observing systems to multiweek fixed deployments, providing crucial insights into the behavior of many different atmospheric boundary layer processes while training the next generation of atmospheric scientists. As calls for a nationwide ground-based profiling network continue, SPARC and CLAMPS can play an important role as test beds and prototype nodes for such a network.

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Kevin R. Haghi, Bart Geerts, Hristo G. Chipilski, Aaron Johnson, Samuel Degelia, David Imy, David B. Parsons, Rebecca D. Adams-Selin, David D. Turner, and Xuguang Wang

Abstract

There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.

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Greg McFarquhar, Beat Schmid, Alexei Korolev, John A. Ogren, Philip B. Russell, Jason Tomlinson, David D. Turner, and Warren Wiscombe

No Abstract available.

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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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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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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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Matthew D. Shupe, David D. Turner, Von P. Walden, Ralf Bennartz, Maria P. Cadeddu, Benjamin B. Castellani, Christopher J. Cox, David R. Hudak, Mark S. Kulie, Nathaniel B. Miller, Ryan R. Neely III, William D. Neff, and Penny M. Rowe

Cloud and atmospheric properties strongly influence the mass and energy budgets of the Greenland Ice Sheet (GIS). To address critical gaps in the understanding of these systems, a new suite of cloud- and atmosphere-observing instruments has been installed on the central GIS as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project. During the first 20 months in operation, this complementary suite of active and passive ground-based sensors and radiosondes has provided new and unique perspectives on important cloud–atmosphere properties.

High atop the GIS, the atmosphere is extremely dry and cold with strong near-surface static stability predominating throughout the year, particularly in winter. This low-level thermodynamic structure, coupled with frequent moisture inversions, conveys the importance of advection for local cloud and precipitation formation. Cloud liquid water is observed in all months of the year, even the particularly cold and dry winter, while annual cycle observations indicate that the largest atmospheric moisture amounts, cloud water contents, and snowfall occur in summer and under southwesterly flow. Many of the basic structural properties of clouds observed at Summit, Greenland, particularly for low-level stratiform clouds, are similar to their counterparts in other Arctic regions.

The ICECAPS observations and accompanying analyses will be used to improve the understanding of key cloud–atmosphere processes and the manner in which they interact with the GIS. Furthermore, they will facilitate model evaluation and development in this data-sparse but environmentally unique region.

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Gijs de Boer, Mark Ivey, Beat Schmid, Dale Lawrence, Darielle Dexheimer, Fan Mei, John Hubbe, Albert Bendure, Jasper Hardesty, Matthew D. Shupe, Allison McComiskey, Hagen Telg, Carl Schmitt, Sergey Y. Matrosov, Ian Brooks, Jessie Creamean, Amy Solomon, David D. Turner, Christopher Williams, Maximilian Maahn, Brian Argrow, Scott Palo, Charles N. Long, Ru-Shan Gao, and James Mather

Abstract

Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems [UASs, also known as unmanned aerial vehicles (UAVs) and drones] and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.

Open access
Volker Wulfmeyer, David D. Turner, B. Baker, R. Banta, A. Behrendt, T. Bonin, W. A. Brewer, M. Buban, A. Choukulkar, E. Dumas, R. M. Hardesty, T. Heus, J. Ingwersen, D. Lange, T. R. Lee, S. Metzendorf, S. K. Muppa, T. Meyers, R. Newsom, M. Osman, S. Raasch, J. Santanello, C. Senff, F. Späth, T. Wagner, and T. Weckwerth

Abstract

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.

Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

Open access