Evaluation of gridded scanning ARM cloud radar reflectivity observations and vertical doppler velocity retrievals

Abstract. The scanning Atmospheric Radiation Measurement (ARM) cloud radars (SACRs) provide continuous atmospheric observations aspiring to capture the 3-D cloud-scale structure. Sampling clouds in 3-D is challenging due to their temporal–spatial scales, the need to sample the sky at high elevations and cloud radar limitations. Thus, a suggested scan strategy is to repetitively slice the atmosphere from horizon to horizon as clouds advect over the radar (Cross-Wind Range-Height Indicator – CW-RHI). Here, the processing and gridding of the SACR CW-RHI scans are presented. First, the SACR sample observations from the ARM Southern Great Plains and Cape Cod sites are post-processed (detection mask, gaseous attenuation correction, insect filtering and velocity de-aliasing). The resulting radial Doppler moment fields are then mapped to Cartesian coordinates with time as one of the dimensions. Next the Cartesian-gridded Doppler velocity fields are decomposed into the horizontal wind velocity contribution and the vertical Doppler velocity component. For validation purposes, all gridded and retrieved fields are compared to collocated zenith-pointing ARM cloud radar measurements. We consider that the SACR sensitivity loss with range, the cloud type observed and the research purpose should be considered in determining the gridded domain size. Our results also demonstrate that the gridded SACR observations resolve the main features of low and high stratiform clouds. It is established that the CW-RHI observations complemented with processing techniques could lead to robust 3-D cloud dynamical representations up to 25–30 degrees off zenith. The proposed gridded products are expected to advance our understanding of 3-D cloud morphology, dynamics and anisotropy and lead to more realistic 3-D radiative transfer calculations.

[1]  Jimmy W. Voyles,et al.  The Arm Climate Research Facility: A Review of Structure and Capabilities , 2013 .

[2]  S. McFarlane,et al.  Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations , 2011 .

[3]  K. A. Browning,et al.  The Determination of Kinematic Properties of a Wind Field Using Doppler Radar , 1968 .

[4]  Mark A. Miller,et al.  An Evaluation of a 94-GHz Radar for Remote Sensing of Cloud Properties , 1995 .

[5]  P. Kollias,et al.  Scanning ARM Cloud Radars. Part I: Operational Sampling Strategies , 2014 .

[6]  Gerald G. Mace,et al.  Cirrus microphysical properties and air motion statistics using cloud radar Doppler moments. Part I: Algorithm description , 2006 .

[7]  R. Somerville,et al.  Single-column models, arm observations, and GCM cloud-radiation schemes , 1999 .

[8]  M. Shupe,et al.  Clouds at Arctic Atmospheric Observatories. Part I: Occurrence and Macrophysical Properties , 2011 .

[9]  Peter H. Hildebrand,et al.  Feasibility Test of an Airborne Pulse-Doppler Meteorological Radar. , 1983 .

[10]  R. L. Vaughan,et al.  An Economical Procedure for Cartesian Interpolation and Display of Reflectivity Factor Data in Three-Dimensional Space , 1979 .

[11]  Bjorn Stevens,et al.  A Large-Eddy Simulation Study of Anisotropy in Fair-Weather Cumulus Cloud Fields , 2005 .

[12]  Pavlos Kollias,et al.  The turbulence structure in a continental stratocumulus cloud from millimeter-wavelength radar observations , 2000 .

[13]  P. Hildebrand,et al.  Objective Determination of the Noise Level in Doppler Spectra , 1974 .

[14]  E. Clothiaux,et al.  Objective Determination of Cloud Heights and Radar Reflectivities Using a Combination of Active Remote Sensors at the ARM CART Sites , 2000 .

[15]  Pavlos Kollias,et al.  Millimeter-Wavelength Radars: New Frontier in Atmospheric Cloud and Precipitation Research , 2007 .

[16]  S. Schwartz,et al.  The Atmospheric Radiation Measurement (ARM) Program: Programmatic Background and Design of the Cloud and Radiation Test Bed , 1994 .

[17]  S. Barnes,et al.  A Technique for Maximizing Details in Numerical Weather Map Analysis , 1964 .

[18]  G. P. Cressman AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[19]  Carl G. Mohr,et al.  The Simple Rectification to Cartesian Space of Folded Radial Velocities from Doppler Radar Sampling , 1986 .

[20]  D. Zrnic,et al.  Doppler Radar and Weather Observations , 1984 .

[21]  P. Rosenkranz Water vapor microwave continuum absorption: A comparison of measurements and models , 1998 .

[22]  Robert F. Cahalan,et al.  The Landsat Scale Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect: Implications for Cloud Remote Sensing , 1997 .

[23]  Jian Zhang,et al.  Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes , 2005 .

[24]  B. Mayer,et al.  Remote sensing of stratocumulus clouds: Uncertainties and biases due to inhomogeneity , 2006 .

[25]  S. Nelson A study of hail production in a supercell storm using a doppler derived wind field and a numerical hail growth model , 1980 .

[26]  E. Luke,et al.  Marine Boundary Layer Cloud Observations in the Azores , 2012 .

[27]  C. Fairall,et al.  Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a K , 1995 .

[28]  David A. Randall,et al.  Single-Column Models and Cloud Ensemble Models as Links between Observations and Climate Models , 1996 .

[29]  Pavlos Kollias,et al.  Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing , 2014 .

[30]  Charles A. Doswell,et al.  Radar Data Objective Analysis , 2000 .