Quality Control of Doppler Spectra from a Vertically Pointing, S-Band Profiling Radar

Susan L. Belak aDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

Search for other papers by Susan L. Belak in
Current site
Google Scholar
PubMed
Close
,
Robin L. Tanamachi aDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

Search for other papers by Robin L. Tanamachi in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-5450-3012
,
Matthew L. Asel aDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

Search for other papers by Matthew L. Asel in
Current site
Google Scholar
PubMed
Close
,
Grant Dennany aDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana

Search for other papers by Grant Dennany in
Current site
Google Scholar
PubMed
Close
,
Abhiram Gnanasambandam bDepartment of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana

Search for other papers by Abhiram Gnanasambandam in
Current site
Google Scholar
PubMed
Close
,
Stephen J. Frasier cMicrowave Remote Sensing Laboratory, University of Massachusetts Amherst, Amherst, Massachusetts

Search for other papers by Stephen J. Frasier in
Current site
Google Scholar
PubMed
Close
, and
Francesc Rocadenbosch dDepartment of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain

Search for other papers by Francesc Rocadenbosch in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments.

Significance Statement

This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

Belak’s current affiliation: National Weather Service Sterling Field Support Center, Sterling, Virginia.

Asel’s current affiliation: National Wind Institute, Texas Tech University, Lubbock, Texas.

Dennany’s current affiliation: Celonis, Los Angeles, California.

Gnanasambandam’s current affiliation: Samsung Research America, Mountain View, California.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robin L. Tanamachi, rtanamachi@purdue.edu

Abstract

This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments.

Significance Statement

This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

Belak’s current affiliation: National Weather Service Sterling Field Support Center, Sterling, Virginia.

Asel’s current affiliation: National Wind Institute, Texas Tech University, Lubbock, Texas.

Dennany’s current affiliation: Celonis, Los Angeles, California.

Gnanasambandam’s current affiliation: Samsung Research America, Mountain View, California.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robin L. Tanamachi, rtanamachi@purdue.edu
Save
  • Atlas, D., K. R. Hardy, and K. Naito, 1966: Optimizing the radar detection of clear air turbulence. J. Appl. Meteor., 5, 450460, https://doi.org/10.1175/1520-0450(1966)005<0450:OTRDOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Banks, R. F., J. Tiana-Alsina, F. Rocadenbosch, and J. M. Baldasano, 2015: 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. Bound.-Layer Meteor., 157, 265292, https://doi.org/10.1007/s10546-015-0056-2.

    • Search Google Scholar
    • Export Citation
  • Bianco, L., and J. M. Wilczak, 2002: Convective boundary layer depth: Improved measurement by Doppler radar wind profiler using fuzzy logic methods. J. Atmos. Oceanic Technol., 19, 17451758, https://doi.org/10.1175/1520-0426(2002)019<1745:CBLDIM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boyd, S., N. Parikh, E. Chu, B. Peleato, and J. Eckstein, 2011: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Now Publishers Inc., 125 pp.

  • Chadwick, R. B., K. P. Moran, R. G. Strauch, G. E. Morrison, and W. C. Campbell, 1976: Microwave radar wind measurements in the clear air. Radio Sci., 11, 795802, https://doi.org/10.1029/RS011i010p00795.

    • Search Google Scholar
    • Export Citation
  • Chan, S. H., X. Wang, and O. A. Elgendy, 2017: Plug-and-play ADMM for image restoration: Fixed-point convergence and applications. IEEE Trans. Comput. Imaging, 3, 8498, https://doi.org/10.1109/TCI.2016.2629286.

    • Search Google Scholar
    • Export Citation
  • da Silva, M. P. A., F. Rocadenbosch, R. L. Tanamachi, and U. Saeed, 2022: Motivating a synergistic mixing-layer height retrieval method using backscatter lidar returns and microwave-radiometer temperature observations. IEEE Trans. Geosci. Remote Sens., 60, 4107418, https://doi.org/10.1109/TGRS.2022.3158401.

    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Eaton, F. D., S. A. McLaughlin, and J. R. Hines, 1995: A new frequency-modulated continuous wave radar for studying planetary boundary layer morphology. Radio Sci., 30, 7588, https://doi.org/10.1029/94RS01937.

    • Search Google Scholar
    • Export Citation
  • Frasier, S. J., J. Waldinger, and NOAA, 2016: UMass S-band FMCW Profiling Radar Data. UCAR, accessed 19 December 2019, https://doi.org/10.5065/D67P8WS3.

  • Gage, K. S., and B. B. Balsley, 1978: Doppler radar probing of the clear atmosphere. Bull. Amer. Meteor. Soc., 59, 10741094, https://doi.org/10.1175/1520-0477(1978)059<1074:DRPOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gossard, E. E., 1990: Radar research on the atmospheric boundary layer. Radar in Meteorology, Amer. Meteor. Soc., 477–527, https://doi.org/10.1007/978-1-935704-15-7_35.

  • Hardy, K. R., and I. Katz, 1969: Probing the clear atmosphere with high power, high resolution radars. Proc. IEEE, 57, 468480, https://doi.org/10.1109/PROC.1969.7001.

    • Search Google Scholar
    • Export Citation
  • Hardy, K. R., D. Atlas, and K. M. Glover, 1966: Multiwavelength backscatter from the clear atmosphere. J. Geophys. Res., 71, 15371552, https://doi.org/10.1029/JZ071i006p01537.

    • Search Google Scholar
    • Export Citation
  • Holzworth, G. C., 1964: Estimates of mean maximum mixing depths in the contiguous United States. Mon. Wea. Rev., 92, 235242, https://doi.org/10.1175/1520-0493(1964)092<0235:EOMMMD>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • İnce, T., A. Pazmany, and S. Frasier, 2000: High resolution profiling of the atmospheric boundary layer. Proc. IEEE 2000 Int. Geoscience and Remote Sensing Symp., Honolulu, HI, Institute of Electrical and Electronics Engineers, 209–212, https://doi.org/10.1109/IGARSS.2000.860470.

  • İnce, T., S. J. Frasier, A. Muschinski, and A. L. Pazmany, 2003: An S-band frequency-modulated continuous-wave boundary layer profiler: Description and initial results. Radio Sci., 38, 1072, https://doi.org/10.1029/2002RS002753.

    • Search Google Scholar
    • Export Citation
  • Knight, C., and L. J. Miller, 1998: Early radar echoes from small warm cumulus: Bragg and hydrometeor scattering. J. Atmos. Sci., 55, 29742992, https://doi.org/10.1175/1520-0469(1998)055<2974:EREFSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Koch, S., 2016: VORTEX-SE: Program and activities. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 3.1, https://ams.confex.com/ams/28SLS/webprogram/Paper300782.html.

  • Kotthaus, S., and Coauthors, 2023: Atmospheric boundary layer height from ground-based remote sensing: A review of capabilities and limitations. Atmos. Meas. Tech., 16, 433479, https://doi.org/10.5194/amt-16-433-2023.

    • Search Google Scholar
    • Export Citation
  • Kropfli, R., I. Katz, T. Konrad, and E. Dobson, 1968: Simultaneous radar reflectivity measurements and refractive index spectra in the clear atmosphere. Radio Sci., 3, 991994, https://doi.org/10.1002/rds1968310991.

    • Search Google Scholar
    • Export Citation
  • Lane, J., 1969: Radar echoes from clear air in relation to refractive-index variations in the troposphere. Proc. IEE, 116, 16561660, https://doi.org/10.1049/piee.1969.0299.

    • Search Google Scholar
    • Export Citation
  • Lange, D., F. Rocadenbosch, J. Tiana-Alsina, and S. Frasier, 2015: Atmospheric boundary layer height estimation using a Kalman filter and a frequency-modulated continuous-wave radar. IEEE Trans. Geosci. Remote Sens., 53, 33383349, https://doi.org/10.1109/TGRS.2014.2374233.

    • Search Google Scholar
    • Export Citation
  • Lee, T., M. Buban, and T. Meyers, 2016: NOAA/ATDD mobile radiosonde data, version 1.0. UCAR/NCAR–Earth Observing Laboratory, accessed 9 July 2023, https://doi.org/10.5065/D68K77FN.

  • Melnikov, V., and D. S. Zrnić, 2017: Observations of convective thermals with weather radar. J. Atmos. Oceanic Technol., 34, 15851590, https://doi.org/10.1175/JTECH-D-17-0068.1.

    • Search Google Scholar
    • Export Citation
  • Ottersten, H., 1969: Atmospheric structure and radar backscattering in clear air. Radio Sci., 4, 11791193, https://doi.org/10.1029/RS004i012p01179.

    • Search Google Scholar
    • Export Citation
  • Pazmany, A. L., and S. J. Haimov, 2018: Coherent power measurements with a compact airborne Ka-band precipitation radar. J. Atmos. Oceanic Technol., 35, 320, https://doi.org/10.1175/JTECH-D-17-0058.1.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., 1995: Using radar-measured radial vertical velocities to distinguish precipitation scattering from clear-air scattering. J. Atmos. Oceanic Technol., 12, 257267, https://doi.org/10.1175/1520-0426(1995)012<0257:URMRVV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E., 2015: VORTEX–Southeast Program overview. National Severe Storms Laboratory Tech. Rep., 36 pp.

  • Rasmussen, E., and S. Koch, 2016: VORTEX-SE: Lessons learned and early results. 28th Conf. on Severe Local Storms, Portland, OR, Amer. Meteor. Soc., 3.2, https://ams.confex.com/ams/28SLS/webprogram/Paper301782.html.

  • Rauber, R. M., and S. W. Nesbitt, 2018: Radar Meteorology: A First Course. John Wiley & Sons, 496 pp.

  • Richter, J. H., 1969: High resolution tropospheric radar sounding. Radio Sci., 4, 12611268, https://doi.org/10.1029/RS004i012p01261.

  • Rocadenbosch, F., R. Barragán, S. J. Frasier, J. Waldinger, D. D. Turner, R. L. Tanamachi, and D. T. Dawson, 2020: Ceilometer-based rain-rate estimation: A case-study comparison with S-band radar and disdrometer retrievals in the context of VORTEX-SE. IEEE Trans. Geosci. Remote Sens., 58, 82688284, https://doi.org/10.1109/TGRS.2020.2984458.

    • Search Google Scholar
    • Export Citation
  • Saeed, U., F. Rocadenbosch, and S. Crewell, 2016: Adaptive estimation of the stable boundary layer height using combined lidar and microwave radiometer observations. IEEE Trans. Geosci. Remote Sens., 54, 68956906, https://doi.org/10.1109/TGRS.2016.2586298.

    • Search Google Scholar
    • Export Citation
  • Seibert, P., F. Beyrich, S.-E. Gryning, S. Joffre, A. Rasmussen, and P. Tercier, 2000: Review and intercomparison of operational methods for the determination of the mixing height. Atmos. Environ., 34, 10011027, https://doi.org/10.1016/S1352-2310(99)00349-0.

    • Search Google Scholar
    • Export Citation
  • Tanamachi, R. L., S. J. Frasier, J. Waldinger, A. LaFleur, D. D. Turner, and F. Rocadenbosch, 2019: Progress toward characterization of the atmospheric boundary layer over northern Alabama using observations by a vertically pointing, S-band profiling radar during VORTEX–Southeast. J. Atmos. Oceanic Technol., 36, 22212246, https://doi.org/10.1175/JTECH-D-18-0224.1.

    • Search Google Scholar
    • Export Citation
  • Ulaby, F. T., R. K. Moore, and A. K. Fung, 1982: Microwave Remote Sensing: Active and Passive. Vol. II, Radar Remote Sensing and Surface Scattering and Emission Theory. Addision-Wesley, 634 pp.

  • Villalonga, J., S. L. Beveridge, M. P. A. Da Silva, R. L. Tanamachi, F. Rocadenbosch, D. D. Turner, and S. J. Frasier, 2020: Convective boundary-layer height estimation from combined radar and Doppler lidar observations in VORTEX-SE. Proc. SPIE, 11531, 115310X, https://doi.org/10.1117/12.2576046.

    • Search Google Scholar
    • Export Citation
  • Waldinger, J., 2018: Improvements to the UMass S-band FM-CW vertical wind profiling radar: System performance and data analysis. Ph.D. thesis, University of Massachusetts Libraries, 97 pp.

  • Waldinger, J., T. Hartley, W. Heberling, S. Frasier, and R. Tanamachi, 2017: S-band FMCW boundary layer profiler: System upgrades and results. 2017 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Fort Worth, TX, Institute of Electrical and Electronics Engineers, 4526–4529, https://doi.org/10.1109/IGARSS.2017.8128008.

  • Wolff, C., 1998: Radartutorial (English version). http://www.radartutorial.eu/index.en.html.

All Time Past Year Past 30 Days
Abstract Views 434 434 178
Full Text Views 95 95 41
PDF Downloads 107 107 56