Radial Velocity and Wind Measurement with NIMA–NWCA: Comparisons with Human Estimation and Aircraft Measurements

Abstract. This paper describes a new signal processing scheme for the 46.5 MHz Doppler Beam Swinging windprofiling radar at Aberystwyth, in the UK. Although the techniques used are similar to those already described in literature -i.e. the identification of multiple signal components within each spectrum and the use of radial-and time-continuity algorithms for quality-control purposes -it is shown that they must be adapted for the specific meteorological environment above Aberystwyth. In particular they need to take into account the three primary causes of unwanted signals: ground clutter, interference, and Rayleigh scatter from hydrometeors under stratiform precipitation conditions. Attention is also paid to the fact that short-period gravitywave activity can lead to an invalidation of the fundamental assumption of the wind field remaining stationary over the temporal and spatial scales encompassed by a cycle of observation. Methods of identifying and accounting for such conditions are described. The random measurement error associated with horizontal wind components is estimated to be 3.0-4.0 m s −1 for single cycle data. This reduces to 2.0-3.0 m s −1 for data averaged over 30 min. The random measurement error associated with vertical wind components is estimated to be 0.2-0.3 m s −1 . This cannot be reduced by time-averaging as significant natural variability is expected over intervals of just a few minutes under conditions of short-period gravitywave activity.

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“…It is noted that the same truncation can be achieved by fitting a Gaussian curve to the observed signal envelope (e.g. Cohn et al, 2001).…”

Section: An Overview Of the V3 Schemementioning

confidence: 99%

Validation of a new signal processing scheme for the MST radar at Aberystwyth

et al. 2008

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“…National Center for Atmospheric Research (NCAR) in USA and Improved Moments Algorithm (NIMA) calculate the first and second moments (radial velocity and spectral width) of wind-profiler [159]. Doppler spectra provide an evaluation of confidence in these calculations.…”

Section: Wind Measurementmentioning

confidence: 99%

Progress and recent trends in wind energy

Sahin

2004

Progress in Energy and Combustion Science

275

100

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“…In 1999, the 10-min confidence-weighted average winds (Goodrich et al 2002) were more reliable and consistent than those reported during the previous year when an earlier version of NIMA without the enhanced RFI processing had been used. This empirical evidence is corroborated by a more formal verification effort, described in Cohn et al (2001). In this verification study, the first moments from NIMA were compared to first moments estimated by human experts.…”

Section: B Human-truthed Datamentioning

confidence: 61%

“…For an operational warning system, confidence estimates can reduce false alarms, that is, a hazard warning may be suppressed in the case of calculated moments that indicate a wind-related hazard but have a low confidence. The details of the wind calculations, the confidence estimates for these values, and the resulting performance are the subject of related papers (Goodrich et al 2002;Cohn et al 2001). …”

Section: Introductionmentioning

confidence: 99%

The NIMA Method for Improved Moment Estimation from Doppler Spectra

Morse

Goodrich

Cornman

2002

Journal of Atmospheric and Oceanic Technology

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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.

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Radial Velocity and Wind Measurement with NIMA–NWCA: Comparisons with Human Estimation and Aircraft Measurements

Cited by 15 publications

References 9 publications

Validation of a new signal processing scheme for the MST radar at Aberystwyth

Validation of a new signal processing scheme for the MST radar at Aberystwyth

Progress and recent trends in wind energy

The NIMA Method for Improved Moment Estimation from Doppler Spectra

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