Progress toward Characterization of the Atmospheric Boundary Layer over Northern Alabama Using Observations by a Vertically Pointing, S-Band Profiling Radar during VORTEX-Southeast

During spring 2016 and spring 2017, a vertically pointing, S-band Frequency Modulated Continuous Wave radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX)-Southeast. In total, ~14 weeks of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply four automated algorithms: 1) a dealiasing algorithm to the Doppler velocities, 2) a fuzzy logic scatterer classification scheme to separate precipitation from nonprecipitation observations, 3) a brightband/melting-layer identification algorithm for stratiform precipitation, and 4) an extended Kalman filter–based convective boundary layer depth (mixing height) measurement algorithm for nonprecipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system.

[1]  Petra Seibert,et al.  Review and intercomparison of operational methods for the determination of the mixing height , 2000 .

[2]  Temple R. Lee,et al.  On the Use of Rotary-Wing Aircraft to Sample Near-Surface Thermodynamic Fields: Results from Recent Field Campaigns , 2018, Sensors.

[3]  F Rocadenbosch,et al.  Adaptive filter solution for processing lidar returns: optical parameter estimation. , 1998, Applied optics.

[4]  Stephen A. Cohn,et al.  Boundary Layer Height and Entrainment Zone Thickness Measured by Lidars and Wind-Profiling Radars , 2000 .

[5]  D. A. Merritt,et al.  Contamination of Wind Profiler Data by Migrating Birds: Characteristics of Corrupted Data and Potential Solutions , 1995 .

[6]  T. Meyers,et al.  Evaluation of the High-Resolution Rapid Refresh (HRRR) Model Using Near-Surface Meteorological and Flux Observations from Northern Alabama , 2019, Weather and Forecasting.

[7]  Anthony W. Lyza,et al.  A Background Investigation of Tornado Activity across the Southern Cumberland Plateau Terrain System of Northeastern Alabama , 2018, Monthly Weather Review.

[8]  F. Martin Ralph,et al.  An Automated Brightband Height Detection Algorithm for Use with Doppler Radar Spectral Moments , 2002 .

[9]  J. Saver,et al.  A Quantitative Study , 2005 .

[10]  Robert R. Lee,et al.  New WSR-88D Volume Coverage Pattern 12: Results of Field Tests , 2005 .

[11]  Frédéric Fabry,et al.  Long-Term Radar Observations of the Melting Layer of Precipitation and Their Interpretation , 1995 .

[12]  D. Stensrud,et al.  Convective Boundary Layer Depth Estimation from S-Band Dual-Polarization Radar , 2018, Journal of Atmospheric and Oceanic Technology.

[13]  J. Garratt.  The Atmospheric Boundary Layer , 1992 .

[14]  Jian Zhang,et al.  A real‐time automated convective and stratiform precipitation segregation algorithm in native radar coordinates , 2013 .

[15]  Allen B. White,et al.  Boundary-layer depth and entrainment zone characterization with a boundary-layer profiler , 1994 .

[16]  F Rocadenbosch,et al.  Lidar inversion of atmospheric backscatter and extinction-to-backscatter ratios by use of a Kalman filter. , 1999, Applied optics.

[17]  F. Martin Ralph,et al.  Precipitation Identification from Radar Wind Profiler Spectral Moment Data: Vertical Velocity Histograms, Velocity Variance, and Signal Power–Vertical Velocity Correlations , 1996 .

[18]  D. Seidel,et al.  Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis , 2010 .

[19]  C. Flamant,et al.  The Dryline on 22 May 2002 during IHOP_2002: Convective-Scale Measurements at the Profiling Site , 2006 .

[20]  J. Klett,et al.  Microphysics of Clouds and Precipitation , 1978, Nature.

[21]  Turker Ince,et al.  High resolution profiling of the atmospheric boundary layer , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[22]  A. Protat,et al.  Comparison of Two Convective/Stratiform Precipitation Classification Techniques: Radar Reflectivity Texture versus Drop Size Distribution–Based Approach , 2013 .

[23]  Jonathan J. Gourley,et al.  A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations , 2007 .

[24]  Witold F. Krajewski,et al.  An Efficient Methodology for Detection of Anomalous Propagation Echoes in Radar Reflectivity Data Using Neural Networks , 2000 .

[25]  K. Elmore,et al.  Radar Reflectivity–Based Estimates of Mixed Layer Depth , 2009 .

[26]  Improvements to the UMASS S-Band FM-CW Vertical Wind Profiling Radar: System Performance and Data Analysis. , 2018 .

[27]  D. Stensrud,et al.  Structures of Bragg Scatter Observed with the Polarimetric WSR-88D , 2013 .

[28]  Michael I. Biggerstaff,et al.  An Improved Scheme for Convective/Stratiform Echo Classification Using Radar Reflectivity , 2000 .

[29]  A. Bemis,et al.  A QUANTITATIVE STUDY OF THE “BRIGHT BAND” IN RADAR PRECIPITATION ECHOES , 1950 .

[30]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[31]  Jay P. Breidenbach,et al.  The WSR-88 D Rainfall Algorithm , 1998 .

[32]  Matthew Hayman,et al.  Field-deployable diode-laser-based differential absorption lidar (DIAL) for profiling water vapor , 2014 .

[33]  Travis M. Smith,et al.  An Automated Technique to Quality Control Radar Reflectivity Data , 2007 .

[34]  Matthias Steiner,et al.  Use of Three-Dimensional Reflectivity Structure for Automated Detection and Removal of Nonprecipitating Echoes in Radar Data , 2002 .

[35]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[36]  Timothy D. Crum,et al.  The WSR-88D and the WSR-88D Operational Support Facility , 1993 .

[37]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[38]  Steven E. Koch,et al.  An Overview of the International H2O Project (IHOP_2002) and Some Preliminary Highlights , 2004 .

[39]  Daniel Sempere-Torres,et al.  Identification of the bright band through the analysis of volumetric radar data , 2000 .

[40]  Earl E. Gossard,et al.  Radar observation of clear air and clouds , 1983 .

[41]  E. Rasmussen,et al.  A Baseline Climatology of Sounding-Derived Supercell and Tornado Forecast Parameters , 1998 .

[42]  B. Demoz,et al.  Double bright band observations with high‐resolution vertically pointing radar, lidar, and profilers , 2014 .

[43]  Eyal Amitai,et al.  Classification of Rain Regimes by the Three-Dimensional Properties of Reflectivity Fields , 1995 .

[44]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[45]  Francesc Rocadenbosch,et al.  Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign , 2016 .

[46]  Francesc Rocadenbosch,et al.  Atmospheric Boundary Layer Height Estimation Using a Kalman Filter and a Frequency‐Modulated Continuous‐Wave Radar , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Robert R. Lee,et al.  Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development , 2017 .

[48]  Louis J. Wicker,et al.  The Influence of Preexisting Boundaries on Supercell Evolution , 1999 .

[49]  David D. Turner,et al.  A New Generation of Ground-Based Mobile Platforms for Active and Passive Profiling of the Boundary Layer , 2019, Bulletin of the American Meteorological Society.

[50]  P. Markowski,et al.  The Characteristics of Numerically Simulated Supercell Storms Situated over Statically Stable Boundary Layers , 2011 .

[51]  F. M. Ralph,et al.  Using Spectral Moment Data from NOAA's 404-MHz Radar Wind Profilers to Observe Precipitation , 1995 .

[52]  Wayne M. Angevine,et al.  Convective Boundary Layer Height Measurement with Wind Profilers and Comparison to Cloud Base , 1998 .

[53]  D. Zrnic,et al.  Observations of Convective Thermals with Weather Radar , 2017 .

[54]  Joan Cuxart,et al.  CASES-99: a comprehensive investigation of the stable nocturnal boundary layer , 2002 .

[55]  J. Gourley,et al.  Automated Detection of the Bright Band Using WSR-88D Data , 2003 .

[56]  Stephen J. Frasier,et al.  High-Resolution Observations of Insects in the Atmospheric Boundary Layer , 2008 .

[57]  Turker Ince,et al.  An S-band frequency-modulated continuous-wave boundary layer profiler: Description and initial results , 2003 .

[58]  Jian Zhang,et al.  Brightband Identification Based on Vertical Profiles of Reflectivity from the WSR-88D , 2008 .

[59]  P. Neiman,et al.  Collocated radar and radiosonde observations of a double-brightband melting layer in Northern California , 2007 .

[60]  J. Baldasano,et al.  Performance Evaluation of the Boundary-Layer Height from Lidar and the Weather Research and Forecasting Model at an Urban Coastal Site in the North-East Iberian Peninsula , 2015, Boundary-Layer Meteorology.

[61]  William L. Smith,et al.  Atmospheric Emitted Radiance Interferometer. Part II: Instrument Performance , 2004 .

[62]  Joseph B. Klemp,et al.  The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy , 1982 .

[63]  Valliappa Lakshmanan,et al.  A Technique to Censor Biological Echoes in Radar Reflectivity Data , 2010 .

[64]  William L. Smith,et al.  Atmospheric Emitted Radiance Interferometer. Part I: Instrument Design , 2004 .

[65]  Frank D. Eaton,et al.  A new frequency‐modulated continuous wave radar for studying planetary boundary layer morphology , 1995 .

[66]  U. Löhnert,et al.  Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI) , 2014 .

[67]  Munther A. Gdeisat,et al.  Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path. , 2002, Applied optics.

[68]  Allen B. White,et al.  A Comparison of Mixing Depths Observed by Ground-Based Wind Profilers and an Airborne Lidar , 1999 .

[69]  Jonathan J. Helmus,et al.  The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language , 2016 .

[70]  Paul Markowski,et al.  Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research , 2009 .

[71]  Clemens Simmer,et al.  A network suitable microwave radiometer for operational monitoring of the cloudy atmosphere , 2005 .

[72]  A. Ryzhkov,et al.  Insights into riming and aggregation processes as revealed by aircraft, radar, and disdrometer observations for a 27 April 2011 widespread precipitation event , 2016 .

[73]  Dmitri Moisseev,et al.  Recent advances in classification of observations from dual polarization weather radars , 2013 .

[74]  David J. Stensrud,et al.  Mapping Bragg Scatter with a Polarimetric WSR-88D , 2011 .

[75]  Notes and Correspondence: Polarimetric radar observation of multiple freezing levels , 2005 .

[76]  Frédéric Fabry,et al.  The accuracy of rainfall estimates by radar as a function of range , 1992 .

[77]  Pengfei Zhang,et al.  Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data , 2016 .

[78]  Volker Wulfmeyer,et al.  A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles , 2015 .

[79]  Alexander V. Ryzhkov,et al.  The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS , 2009 .

[80]  C. Knight,et al.  Early Radar Echoes from Small, Warm Cumulus: Bragg and Hydrometeor Scattering , 1998 .

[81]  F. Martin Ralph,et al.  Using Radar-Measured Radial Vertical Velocities to Distinguish Precipitation Scattering from Clear-Air Scattering , 1995 .