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

The authors derive a relationship between the vertical Doppler spectrum of the rain just below the radar bright band and that of the snow just above. It neglects vertical air motions and assumes that each snowflake simply melts to form a raindrop of the same mass, disregarding other possible effects such as aggregation to form larger particles or breakup to create smaller ones. The relationship shows that, regardless of the dependence of particle fallspeed on size, the product of the equivalent reflectivity factor and the mean Doppler velocity of the snow is proportional to the same product for the rain, with a constant proportionality factor of 0.23, which equals the ratio of the dielectric factors of ice and water. Observed values of the reflectivity and mean Doppler velocity above and below the melting layer sometimes agree with this theoretical prediction but more often deviate from it in ways that may be interpreted as indicating the predominance of either aggregation or breakup processes. The data suggest that aggregation is occurring much of the time in the melting layer but that breakup effects become dominant in heavy precipitation. The analysis is extended by assuming relations between particle size and fallspeed for rain and snow. This enables the comparison of measured spectra with those derived theoretically. A simple allowance for aggregation or breakup in the spectral transformation from snow to rain is found to give improved spectral agreement in cases where these effects are indicated.

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.

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.

The Concordiasi project is making innovative observations of the atmosphere above Antarctica. The most important goals of the Concordiasi are as follows:

• To enhance the accuracy of weather prediction and climate records in Antarctica through the assimilation of in situ and satellite data, with an emphasis on data provided by hyperspectral infrared sounders. The focus is on clouds, precipitation, and the mass budget of the ice sheets. The improvements in dynamical model analyses and forecasts will be used in chemical-transport models that describe the links between the polar vortex dynamics and ozone depletion, and to advance the under understanding of the Earth system by examining the interactions between Antarctica and lower latitudes.

• To improve our understanding of microphysical and dynamical processes controlling the polar ozone, by providing the first quasi-Lagrangian observations of stratospheric ozone and particles, in addition to an improved characterization of the 3D polar vortex dynamics. Techniques for assimilating these Lagrangian observations are being developed.

A major Concordiasi component is a field experiment during the austral springs of 2008–10. The field activities in 2010 are based on a constellation of up to 18 long-duration stratospheric super-pressure balloons (SPBs) deployed from the McMurdo station. Six of these balloons will carry GPS receivers and in situ instruments measuring temperature, pressure, ozone, and particles. Twelve of the balloons will release dropsondes on demand for measuring atmospheric parameters. Lastly, radiosounding measurements are collected at various sites, including the Concordia station.