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Marcia K. Politovich, R. Kent Goodrich, Corrinne S. Morse, Alan Yates, Robert Barron, and Steven A. Cohn

Abstract

The Juneau, Alaska, airport vicinity experiences frequent episodes of moderate and severe turbulence, which affect arriving and departing air traffic. The Federal Aviation Administration funded the National Center for Atmospheric Research to develop a warning system, consisting of carefully placed anemometers and wind profilers, along with data communications, an algorithm, and display, to warn pilots of potentially hazardous situations. The system uses regressions based on comparisons of research aircraft data with measurements from the ground-based sensors to estimate the turbulence intensity along selected flight paths. This paper describes the development of the turbulence warning system, from meteorological characteristics through sensor placement, algorithm construction and evaluation, and display design. The discussion includes how best estimates of winds were made in adverse meteorological and topographic conditions, how turbulence was calculated from aircraft conducting various flight maneuvers, how bad data were identified and removed from the system, how the regressors were selected, and the skill of the system.

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Stephen A. Cohn, Robert K. Goodrich, Corinne S. Morse, Eli Karplus, Steven W. Mueller, Larry B. Cornman, and R. Andrew Weekley

Abstract

The National Center for Atmospheric Research (NCAR) Improved Moments Algorithm (NIMA) calculates the first and second moments (radial velocity and spectral width) of wind-profiler Doppler spectra and provides an evaluation of confidence in these calculations. The first moments and their confidences are used by the NCAR Winds And Confidence Algorithm (NWCA), to estimate the horizontal wind. NIMA–NWCA has been used for several years in a real-time application for three wind profilers in Juneau, Alaska. This paper presents results of an effort to evaluate the first moments produced by NIMA and horizontal winds produced by NIMA–NWCA through comparison with estimates from “human experts” and also presents a comparison of NIMA–NWCA winds with in situ aircraft measurements. NIMA uses fuzzy logic to separate the atmospheric component of Doppler spectra from ground clutter and other sources of interference. The fuzzy logic rules are based on similar features humans consider when identifying atmospheric and contamination signals in Doppler spectra. Furthermore, NIMA attempts to mimic the human experts’ assignment of confidence to the moments. A Human Moment Analysis (HMA) tool was developed to assist the human experts in quantifying moments. This tool is described and a methodology of tuning NIMA rules based on human truth specification is presented. NIMA performed well on a dataset specifically chosen to be difficult. The average absolute error between the HMA estimate and NIMA-derived radial wind estimate was slightly more than 0.3 m s−1 when data with low NIMA confidence were excluded, which is comparable to the Doppler spectrum resolution. The correlation between winds derived from NIMA–NWCA and from HMA first-moment estimates exceeded 0.96 when the data with low NWCA confidence were excluded. The correlation coefficient between NIMA winds and in situ measurements by aircraft was 0.93 when aircraft winds that were believed to be accurate were used.

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