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- Author or Editor: Corrinne S. Morse x
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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.
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.
Abstract
Boundary layer wind profilers are increasingly being used in applications that require high-quality, rapidly updated winds. An example of this type of application is an airport wind hazard warning system. Wind shear can be a hazard to flight operations and is also associated with the production of turbulence. A method for calculating wind and wind shear using a linear wind field assumption is presented. This method, applied to four- or five-beam profilers, allows for the explicit accounting of the measurable shear terms. An error analysis demonstrates why some shears are more readily estimated than others, and the expected magnitudes of the variance for the wind and wind shear estimates are given. A method for computing a quality control index, or confidence, for the calculated wind is also presented. This confidence calculation is based on an assessment of the validity of the assumptions made in the calculations. Confidence values can be used as a quality control metric for the calculated wind and can also be used in generating a confidence-weighted average wind value from the rapid update values. Results are presented that show that errors in the wind estimates are reduced after removing values with low confidence. The wind and confidence methods are implemented in the NCAR Wind and Confidence Algorithm (NWCA), and have been used with the NCAR Improved Moments Algorithm (NIMA) method for calculating moments and associated moment confidence from Doppler spectra. However, NWCA may be used with any moment algorithm that also computes a first moment confidence. For example, a very simple confidence algorithm can be defined in terms of the signal-to-noise ratio.
Abstract
Boundary layer wind profilers are increasingly being used in applications that require high-quality, rapidly updated winds. An example of this type of application is an airport wind hazard warning system. Wind shear can be a hazard to flight operations and is also associated with the production of turbulence. A method for calculating wind and wind shear using a linear wind field assumption is presented. This method, applied to four- or five-beam profilers, allows for the explicit accounting of the measurable shear terms. An error analysis demonstrates why some shears are more readily estimated than others, and the expected magnitudes of the variance for the wind and wind shear estimates are given. A method for computing a quality control index, or confidence, for the calculated wind is also presented. This confidence calculation is based on an assessment of the validity of the assumptions made in the calculations. Confidence values can be used as a quality control metric for the calculated wind and can also be used in generating a confidence-weighted average wind value from the rapid update values. Results are presented that show that errors in the wind estimates are reduced after removing values with low confidence. The wind and confidence methods are implemented in the NCAR Wind and Confidence Algorithm (NWCA), and have been used with the NCAR Improved Moments Algorithm (NIMA) method for calculating moments and associated moment confidence from Doppler spectra. However, NWCA may be used with any moment algorithm that also computes a first moment confidence. For example, a very simple confidence algorithm can be defined in terms of the signal-to-noise ratio.
