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Volker Wulfmeyer, Shravan Kumar Muppa, Andreas Behrendt, Eva Hammann, Florian Späth, Zbigniew Sorbjan, David D. Turner, and R. Michael Hardesty

Abstract

Atmospheric variables in the convective boundary layer (CBL), which are critical for turbulence parameterizations in weather and climate models, are assessed. These include entrainment fluxes, higher-order moments of humidity, potential temperature, and vertical wind, as well as dissipation rates. Theoretical relationships between the integral scales, gradients, and higher-order moments of atmospheric variables, fluxes, and dissipation rates are developed mainly focusing on the entrainment layer (EL) at the top of the CBL. These equations form the starting point for tests of and new approaches in CBL turbulence parameterizations. For the investigation of these relationships, an observational approach using a synergy of ground-based water vapor, temperature, and wind lidar systems is proposed. These systems measure instantaneous vertical profiles with high temporal and spatial resolution throughout the CBL including the EL. The resolution of these systems permits the simultaneous measurement of gradients and fluctuations of these atmospheric variables. For accurate analyses of the gradients and the shapes of turbulence profiles, the lidar system performances are very important. It is shown that each lidar profile can be characterized very well with respect to bias and system noise and that the constant bias has negligible effect on the measurement of turbulent fluctuations. It is demonstrated how different gradient relationships can be measured and tested with the proposed lidar synergy within operational measurements or new field campaigns. Particularly, a novel approach is introduced for measuring the rate of destruction of humidity and temperature variances, which is an important component of the variance budget equations.

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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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Jason A. Otkin, Daniel C. Hartung, David D. Turner, Ralph A. Petersen, Wayne F. Feltz, and Erik Janzon

Abstract

In this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

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Wayne M. Angevine, Joseph Olson, Jaymes Kenyon, William I. Gustafson Jr., Satoshi Endo, Kay Suselj, and David D. Turner

Abstract

Representation of shallow cumulus is a challenge for mesoscale numerical weather prediction models. These cloud fields have important effects on temperature, solar irradiance, convective initiation, and pollutant transport, among other processes. Recent improvements to physics schemes available in the Weather Research and Forecasting (WRF) Model aim to improve representation of shallow cumulus, in particular over land. The DOE LES ARM Symbiotic Simulation and Observation Workflow (LASSO) project provides several cases that we use here to test the new physics improvements. The LASSO cases use multiple large-scale forcings to drive large-eddy simulations (LES), and the LES output is easily compared to output from WRF single-column simulations driven with the same initial conditions and forcings. The new Mellor–Yamada–Nakanishi–Niino (MYNN) eddy diffusivity mass-flux (EDMF) boundary layer and shallow cloud scheme produces clouds with timing, liquid water path (LWP), and cloud fraction that agree well with LES over a wide range of those variables. Here we examine those variables and test the scheme’s sensitivity to perturbations of a few key parameters. We also discuss the challenges and uncertainties of single-column tests. The older, simpler total energy mass-flux (TEMF) scheme is included for comparison, and its tuning is improved. This is the first published use of the LASSO cases for parameterization development, and the first published study to use such a large number of cases with varying cloud amount. This is also the first study to use a more precise combined infrared and microwave retrieval of LWP to evaluate modeled clouds.

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Stefan Kneifel, Stephanie Redl, Emiliano Orlandi, Ulrich Löhnert, Maria P. Cadeddu, David D. Turner, and Ming-Tang Chen

Abstract

Microwave radiometers (MWR) are commonly used to quantify the amount of supercooled liquid water (SLW) in clouds; however, the accuracy of the SLW retrievals is limited by the poor knowledge of the SLW dielectric properties at microwave frequencies. Six liquid water permittivity models were compared with ground-based MWR observations between 31 and 225 GHz from sites in Greenland, the German Alps, and a low-mountain site; average cloud temperatures of observed thin cloud layers range from 0° to −33°C. A recently published method to derive ratios of liquid water opacity from different frequencies was employed in this analysis. These ratios are independent of liquid water path and equal to the ratio of α L at those frequencies that can be directly compared with the permittivity model predictions. The observed opacity ratios from all sites show highly consistent results that are generally within the range of model predictions; however, none of the models are able to approximate the observations over the entire frequency and temperature range. Findings in earlier published studies were used to select one specific model as a reference model for α L at 90 GHz; together with the observed opacity ratios, the temperature dependence of α L at 31.4, 52.28, 150, and 225 GHz was derived. The results reveal that two models fit the opacity ratio data better than the other four models, with one of the two models fitting the data better for frequencies below 90 GHz and the other for higher frequencies. These findings are relevant for SLW retrievals and radiative transfer in the 31–225-GHz frequency region.

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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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James R. Campbell, Dennis L. Hlavka, Ellsworth J. Welton, Connor J. Flynn, David D. Turner, James D. Spinhirne, V. Stanley Scott III, and I. H. Hwang

Abstract

Atmospheric radiative forcing, surface radiation budget, and top-of-the-atmosphere radiance interpretation involve knowledge of the vertical height structure of overlying cloud and aerosol layers. During the last decade, the U.S. Department of Energy, through the Atmospheric Radiation Measurement (ARM) program, has constructed four long-term atmospheric observing sites in strategic climate regimes (north-central Oklahoma; Barrow, Alaska; and Nauru and Manus Islands in the tropical western Pacific). Micropulse lidar (MPL) systems provide continuous, autonomous observation of nearly all significant atmospheric clouds and aerosols at each of the central ARM facilities. These systems are compact, and transmitted pulses are eye safe. Eye safety is achieved by expanding relatively low-powered outgoing pulse energy through a shared, coaxial transmit/receive telescope. ARM MPL system specifications and specific unit optical designs are discussed. Data normalization and calibration techniques are presented. These techniques, in tandem, represent an operational value-added processing package used to produce normalized data products for ARM cloud and aerosol research.

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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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