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The NIMA Method for Improved Moment Estimation from Doppler Spectra

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  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 National Center for Atmospheric Research, and Department of Mathematics, University of Colorado, Boulder, Colorado
  • | 3 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

The NCAR Improved Moments Algorithm (NIMA) for estimating moments from wind measurement devices that measure Doppler spectra as a function of range is described in some detail. Although NIMA's main application has been for real-time processing of wind profiler data, it has also been successfully applied to Doppler lidar and weather radar data. Profiler spectra are often contaminated by a variety of sources including aircraft, birds, velocities exceeding the Nyquist velocity, radio frequency interference, and ground clutter. The NIMA method uses mathematical analysis, fuzzy logic synthesis, and global image processing algorithms to mimic human experts' ability to identify atmospheric signals in the presence of such contaminants. NIMA is configurable and its processing can be tuned to optimize performance for a given profiler site. Once configured, NIMA is a fully automated algorithm that runs in real time to produce Doppler moments and a confidence assessment of those moments. These confidence values are useful in the generation and assessment of wind and turbulence estimates and are important when these quantities are used in critical situations such as airport operations. A simulation study is used to compare NIMA performance with that of a simple peak picking algorithm in the presence of ground clutter, RFI, and point targets. Some performance results for the NIMA confidence algorithm are also given.

Corresponding author address: Corinne S. Morse, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. Email: morse@ucar.edu

Abstract

The NCAR Improved Moments Algorithm (NIMA) for estimating moments from wind measurement devices that measure Doppler spectra as a function of range is described in some detail. Although NIMA's main application has been for real-time processing of wind profiler data, it has also been successfully applied to Doppler lidar and weather radar data. Profiler spectra are often contaminated by a variety of sources including aircraft, birds, velocities exceeding the Nyquist velocity, radio frequency interference, and ground clutter. The NIMA method uses mathematical analysis, fuzzy logic synthesis, and global image processing algorithms to mimic human experts' ability to identify atmospheric signals in the presence of such contaminants. NIMA is configurable and its processing can be tuned to optimize performance for a given profiler site. Once configured, NIMA is a fully automated algorithm that runs in real time to produce Doppler moments and a confidence assessment of those moments. These confidence values are useful in the generation and assessment of wind and turbulence estimates and are important when these quantities are used in critical situations such as airport operations. A simulation study is used to compare NIMA performance with that of a simple peak picking algorithm in the presence of ground clutter, RFI, and point targets. Some performance results for the NIMA confidence algorithm are also given.

Corresponding author address: Corinne S. Morse, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. Email: morse@ucar.edu

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