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Title: Correction of Dual-PRF Doppler Velocity Outliers in the Presence of Aliasing

In Doppler weather radars, the presence of unfolding errors or outliers is a well-known quality issue for radial velocity fields estimated using the dual–pulse repetition frequency (PRF) technique. Postprocessing methods have been developed to correct dual-PRF outliers, but these need prior application of a dealiasing algorithm for an adequate correction. Our paper presents an alternative procedure based on circular statistics that corrects dual-PRF errors in the presence of extended Nyquist aliasing. The correction potential of the proposed method is quantitatively tested by means of velocity field simulations and is exemplified in the application to real cases, including severe storm events. The comparison with two other existing correction methods indicates an improved performance in the correction of clustered outliers. The technique we propose is well suited for real-time applications requiring high-quality Doppler radar velocity fields, such as wind shear and mesocyclone detection algorithms, or assimilation in numerical weather prediction models.
 [1] ;  [2] ;  [3] ;  [3] ;  [3] ;  [4] ;  [4]
  1. Univ. of Barcelona (Spain). Meteorological Service of Catalonia, Dept. of Astronomy and Meteorology
  2. Univ. of Barcelona (Spain). Dept. of Astronomy and Meteorology
  3. Meteorological Service of Catalonia, Barcelona (Spain)
  4. Argonne National Lab. (ANL), Argonne, IL (United States). Environmental Science Division
Publication Date:
Grant/Contract Number:
Published Article
Journal Name:
Journal of Atmospheric and Oceanic Technology
Additional Journal Information:
Journal Volume: 34; Journal Issue: 7; Journal ID: ISSN 0739-0572
American Meteorological Society
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
58 GEOSCIENCES; wind; data quality control; radars/radar observations; remote sensing; filtering techniques; statistical techniques
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1394821