Electronic Scan Strategy for Phased Array Weather Radar Using a Space–Time Characterization Model

Cuong M. Nguyen Flight Research Laboratory, National Research Council Canada, Ottawa, Ontario, Canada

Search for other papers by Cuong M. Nguyen in
Current site
Google Scholar
PubMed
Close
and
V. Chandrasekar Colorado State University, Fort Collins, Colorado

Search for other papers by V. Chandrasekar in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This paper presents an adaptive scan strategy concept for phased array weather radars (PAWR) with the objective of increasing the scan speed and capturing features of the storm system while maintaining the measurement accuracy. The adaptive scan strategy is developed based on the space–time variability of the storm under observation. Quickly evolving regions are scanned more often and the spatial sampling resolution is matched to the spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time, while the measurement accuracy controls the radar’s dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. The model and the scan strategy are demonstrated using simulation data. The results show that the proposed scan strategy can reduce the scan time significantly without compromising data quality.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Cuong M. Nguyen, cuong.nguyen@nrc-cnrc.gc.ca

Abstract

This paper presents an adaptive scan strategy concept for phased array weather radars (PAWR) with the objective of increasing the scan speed and capturing features of the storm system while maintaining the measurement accuracy. The adaptive scan strategy is developed based on the space–time variability of the storm under observation. Quickly evolving regions are scanned more often and the spatial sampling resolution is matched to the spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time, while the measurement accuracy controls the radar’s dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. The model and the scan strategy are demonstrated using simulation data. The results show that the proposed scan strategy can reduce the scan time significantly without compromising data quality.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Cuong M. Nguyen, cuong.nguyen@nrc-cnrc.gc.ca
Save
  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

    • Crossref
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Gang, X., and V. Chandrasekar, 2005: Radar storm motion estimation and beyond: A spectral algorithm and radar observation based dynamic model. Symp. on Nowcasting and Very Short Range Forecasting (WSN05), Toulouse, France, World Weather Research Programme and Météo-France, 2.41. [Available online at http://www.meteo.fr/cic/wsn05/resumes_longs/2.41-138.pdf.]

  • Heinselman, P. L., D. L. Priegnitz, K. L. Manross, T. M. Smith, and R. W. Adams, 2008: Rapid sampling of severe storms by the National Weather Radar Testbed Phased Array Radar. Wea. Forecasting, 23, 808824, doi:10.1175/2008WAF2007071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, C. L., and R. J. Hanson, 1987: Linear least squares with linear inequality constraints. Solving Least Squares Problems, Society for Industrial Mathematics, 158–173.

  • National Research Council, 2009: Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. National Academies Press, 250 pp., doi:10.17226/12540.

    • Crossref
    • Export Citation
  • Nguyen, C. M., and V. Chandrasekar, 2013: Gaussian model adaptive processing in time domain (GMAP-TD) for weather radars. J. Atmos. Oceanic Technol., 30, 25712584, doi:10.1175/JTECH-D-12-00215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, J., and I. W. Sandberg, 1991: Universal approximation using radial-basis-function networks. Neural Comput., 3, 246257, doi:10.1162/neco.1991.3.2.246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., S. Richardson, J. M. Straka, P. M. Markowski, and D. O. Blanchard, 2000: The association of significant tornadoes with a baroclinic boundary on 2 June 1995. Mon. Wea. Rev., 128, 174191, doi:10.1175/1520-0493(2000)128<0174:TAOSTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reinoso-Rondinel, R., T.-Y. Yu, and S. Torres, 2010: Multifunction phased-array radar: Time balance scheduler for adaptive weather sensing. J. Atmos. Oceanic Technol., 27, 18541867, doi:10.1175/2010JTECHA1420.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ROC, 2007: WSR-88D system specification. WSR-88D Radar Operations Center Rep. OWY55, 164 pp.

  • Rood, R. B., 1987: Numerical advection algorithms and their role in atmospheric transport and chemistry models. Rev. Geophys., 25, 71100, doi:10.1029/RG025i001p00071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruzanski, E., V. Chandrasekar, and Y. Wang, 2011: The CASA nowcasting system. J. Atmos. Oceanic Technol., 28, 640655, doi:10.1175/2011JTECHA1496.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stafford, W. K. 1990: Real time control of multifunction electronically scanned adaptive radar (MESAR). Preprints, IEE Colloquium on Real Time Management of Adaptive Radar Systems, London, United Kingdom, Siemens Plessey Radar, 1–5. [Available online at http://ieeexplore.ieee.org/document/191190/.]

  • Tripoli, G. J., and M. L. Büker, 2012: Numerical simulation of three-dimensional vortical interactions within an idealized tornado. 26th Conf. on Severe Local Storms, Nashville, TN, Amer. Meteor. Soc., 1.2. [Available online https://ams.confex.com/ams/26SLS/webprogram/Paper212293.html.]

  • Wikle, C. K., 2002: A kernel-based spectral model for non-Gaussian spatio-temporal processes. Stat. Modell., 2, 299314, doi:10.1191/1471082x02st036oa.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, T.-Y., M. B. Orescanin, C. D. Curtis, D. S. Zrnić, and D. E. Forsyth, 2007: Beam multiplexing using the phased-array weather radar. J. Atmos. Oceanic Technol., 24, 616626, doi:10.1175/JTECH2052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766, doi:10.1175/BAMS-88-11-1753.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1704 655 18
PDF Downloads 834 140 5