Karen Johnson

The Atmospheric Radiation Measurement Program Cloud Profiling Radars: Second-Generation Sampling Strategies, Processing, and Cloud Data Products

Abstract The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates millimeter-wavelength cloud radars in several climatologically distinct regions. The digital signal processors for these radars were recently upgraded and allow for enhancements in the operational parameters running on them. Recent evaluations of millimeter-wavelength cloud radar signal processing performance relative to the range of cloud dynamical and microphysical conditions encountered at the ARM Program sites have indicated that improvements are necessary, including significant improvement in temporal resolution (i.e., less than 1 s for dwell and 2 s for dwell and processing), wider Nyquist velocities, operational dealiasing of the recorded spectra, removal of pulse compression while sampling the boundary layer, and continuous recording of Doppler spectra. A new set of millimeter-wavelength cloud radar operational modes that incorporate these enhancements is presented. A significant change in radar samplin...

The Atmospheric Radiation Measurement Program Cloud Profiling Radars: An Evaluation of Signal Processing and Sampling Strategies

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 ...

Scanning ARM Cloud Radars. Part I: Operational Sampling Strategies

AbstractThe acquisition of scanning cloud radars by the Atmospheric Radiation Measurement (ARM) program and research institutions around the world generates the need for developing operational scan strategies for cloud radars. Here, the first generation of sampling strategies for the scanning ARM cloud radars (SACRs) is presented. These scan strategies are designed to address the scientific objectives of ARM; however, they introduce an initial framework for operational scanning cloud radars. While the weather community uses scan strategies that are based on a sequence of scans at constant elevations, the SACR scan strategies are based on a sequence of scans at constant azimuth. This is attributed to the cloud geometrical properties, which are vastly different from the rain and snow shafts that are the primary targets of precipitation radars; the need to cover the cone of silence; and the scanning limitations of the SACRs. A “cloud surveillance” scan strategy is introduced that is based on a sequence of ho...

Cloud, thermodynamic, and precipitation observations in West Africa during 2006

[1] In 2006, the ARM Mobile Facility (AMF) completed a 1-year deployment at Niamey, Niger, Africa, in support of the Radiative Atmospheric Divergence using ARM Mobile Facility, GERB data and AMMA Stations (RADAGAST) field campaign, which is the subject of this special issue. Observations from the AMF instrumentation are used to analyze the relationship between clouds, precipitation, and the thermodynamic environment in this rarely observed region and to evaluate the cloud fields in the National Center for Environmental Prediction Global Forecast System (GFS) initialization product. The 1-year deployment period enabled measurements in the dry and wet (monsoon) seasons and through the transitions in May and September, respectively. Cirrus clouds in the 10- to 15-km layer with modest monthly cloud fraction and mean depth of � 1k m are ubiquitous through the observing period as observed in other regions of the tropics. The monsoon season from May to September is characterized by convective clouds of varying depth that produce precipitation of varying intensity, as indicated by cloud radar. Peak surface rainfall is observed during August, and the largest daily rainfall rates are observed during the period from July to September. The lifting condensation level (LCL) is observed to decrease as the monsoon season progresses, and a strong correlation between the height of the LCL and precipitation is demonstrated. Cooling of the lower troposphere is implicated as the probable cause of the lowering of the LCL. Conversely, the amount of convective available potential energy is found to be poorly correlated with precipitation. As in other tropical regions, the physical height at which the zero-degree isotherm is observed corresponds to gradients in the thermodynamic profiles and a gradient in the profile of cloud occurrence. Comparisons with the GFS initialization data, which are derived from a number of sources including satellites, show some systematic biases when compared to AMF measurements. There is general correspondence between the locations of clouds and the profile of vertical velocity diagnosed by the GFS initialization early in the monsoon season, but vague correspondence thereafter. The relative humidity in the GFS initialization is too large above 10 km and too small in the monsoon layer near the surface, and it seriously underestimates the amount of cloud below 10 km during August, which is the height of the West African monsoon in Niamey.

Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing

AbstractThe scanning Atmospheric Radiation Measurement (ARM) Program cloud radars (SACRs) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20–30-km radius of the ARM fixed and mobile sites. Here, the postprocessing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of...

A Technique for the Automatic Detection of Insect Clutter in Cloud Radar Returns

Abstract The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates 35-GHz millimeter-wavelength cloud radars (MMCRs) in several climatologically distinct regions. The MMCRs, which are centerpiece instruments for the observation of clouds and precipitation, provide continuous, vertically resolved information on all hydrometeors above the ARM Climate Research Facilities (ACRF). However, their ability to observe clouds in the lowest 2–3 km of the atmosphere is often obscured by the presence of strong echoes from insects, especially during the warm months at the continental midlatitude Southern Great Plains (SGP) ACRF. Here, a new automated technique for the detection and elimination of insect-contaminated echoes from the MMCR observations is presented. The technique is based on recorded MMCR Doppler spectra, a feature extractor that conditions insect spectral signatures, and the use of a neural network algorithm for the generation of an insect (clutter) mask. The technique exhibi...

Development and Applications of ARM Millimeter-Wavelength Cloud Radars

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 McGill University, Montreal, Quebec, Canada The Pennsylvania State University, University Park, Pennsylvania University of Washington, Seattle, Washington University of Miami, Miami, Florida Pacific Northwest National Laboratory, Richland, Washington National Oceanic and Atmospheric Administration, Boulder, Colorado Brookhaven National Laboratory, Upton, New York ProSensing, Inc., Amherst, Massachusetts Rutgers, The State University of New Jersey, New Brunswick, New Jersey University of Utah, Salt Lake City, Utah

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

C louds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real-world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors that are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators,” which convert model variables into pseudoinstrument observations, has evolved with the goal to facilitate and improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the GCM community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to the radar measurements made by CloudSat [a satellite carrying the first spaceborne 94-GHz (3.2-mm wavelength) cloud radar], which provides near-global sampling of profiles of cloud condensate and precipitation with a vertical resolution of 500 m (Stephens et al. 2002), ARM radar measurements occur with higher temporal resolution (10 s) and finer vertical resolution (45 m). This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to both surface contamination (Mace et al. 2007; Marchand et al. 2008) and a radar sensitivity of approximately −28 dBZ near the surface. Therefore, the ARM ground-based cloud observations complement measurements from space.

ARM: X-Band Scanning ARM Cloud Radar (XSACR) RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) RHI Scans, which can vary in elevation range and azimuth

ARM: X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)