Larry Cornman

Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements

The quality of atmospheric turbulence detection and forecast information for the operational meteorology and aviation communities is directly linked to the quality of real-time measurements. Currently, the only direct data are subjective, qualitative, and intermittent pilot reports. This article describes techniques, suitable for real-time application on commercial transport aircraft, to generate quantitative and comprehensive turbulence measurements. These algorithms build on standard methods used in the analysis of aircraft response to turbulence, but are specifically designed to address the limitations of the available on-board data and computational

Estimating Spatial Velocity Statistics with Coherent Doppler Lidar

The spatial statistics of a simulated turbulent velocity field are estimated using radial velocity estimates from simulated coherent Doppler lidar data. The structure functions from the radial velocity estimates are processed to estimate the energy dissipation rate e and the integral length scale Li, assuming a theoretical model for isotropic wind fields. The performance of the estimates are described by their bias, standard deviation, and percentiles. The estimates of e 2/3 are generally unbiased and robust. The distribution of the estimates of Li are highly skewed; however, the median of the distribution is generally unbiased. The effects of the spatial averaging by the atmospheric movement transverse to the lidar beam during the dwell time of each radial velocity estimate are determined, as well as the error scaling as a function of the dimensions of the total measurement region. Accurate estimates of Li require very large measurement domains in order to observe a large number of independent samples of the spatial scales that define Li.

The NIMA method for improved moment estimation from doppler spectra

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.

A Fuzzy Logic Method for Improved Moment Estimation from Doppler Spectra

A new method for estimating moments from wind measurement devices that measure Doppler spectra as a function of range is presented. Quite often the spectra are contaminated by a wide variety of sources, including (but not limited to) birds, aircraft, velocity and range folding, radio frequency interference, and ground clutter. These contamination sources can vary in space, time, and even in their basic characteristics. Human experts analyzing Doppler spectra can often identify the desired atmospheric signal among the contamination. However, it is quite difficult to build automated algorithms that can approach the skill of the human expert. The method described here relies on mathematical analyses, fuzzy logic synthesis, and global image processing algorithms to mimic the human expert. Fuzzy logic is a very simple, robust, and efficient technique that is well suited to this type of feature extraction problem. These new moment estimation algorithms were originally designed for boundary layer wind profilers; however, they are quite general and have wide applicability to any device that measures Doppler spectra as a function of range (e.g., lidars, sodars, and weather radars).

Description and Derived Climatologies of Automated In Situ Eddy-Dissipation-Rate Reports of Atmospheric Turbulence

AbstractThe statistical properties of turbulence at upper levels in the atmosphere [upper troposphere and lower stratosphere (UTLS)] are still not well known, partly because of the lack of adequate routine observations. This is despite the obvious benefit that such observations would have for alerting aircraft of potentially hazardous conditions, either in real time or for route planning. To address this deficiency, a research project sponsored by the Federal Aviation Administration has developed a software package that automatically estimates and reports atmospheric turbulence intensity levels (as EDR ≡ e1/3, where e is the energy or eddy dissipation rate). The package has been tested and evaluated on commercial aircraft. The amount of turbulence data gathered from these in situ reports is unprecedented. As of January 2014, there are ~200 aircraft outfitted with this system, contributing to over 137 million archived records of EDR values through 2013, most of which were taken at cruise levels of commerci...

Simulation of Three-Dimensional Turbulent Velocity Fields

Abstract New algorithms for the simulation of three-dimensional homogeneous turbulent velocity fields are compared with standard spectral domain algorithms. Results are presented for a von Karman model of the covariance tensor. For typical atmospheric conditions, it is impossible to produce a simulated velocity field that simultaneously satisfies a given spatial correlation and the corresponding spatial spectrum, because of spectral aliasing. The goal of the new algorithms is to produce a turbulent velocity field that has accurate spatial correlations. The algorithms are a modification of the standard spectral domain method that attempts to produce a given spatial spectrum.

An Algorithm for Classification and Outlier Detection of Time-Series Data

Abstract An algorithm to perform outlier detection on time-series data is developed, the intelligent outlier detection algorithm (IODA). This algorithm treats a time series as an image and segments the image into clusters of interest, such as “nominal data” and “failure mode” clusters. The algorithm uses density clustering techniques to identify sequences of coincident clusters in both the time domain and delay space, where the delay-space representation of the time series consists of ordered pairs of consecutive data points taken from the time series. “Optimal” clusters that contain either mostly nominal or mostly failure-mode data are identified in both the time domain and delay space. A best cluster is selected in delay space and used to construct a “feature” in the time domain from a subset of the optimal time-domain clusters. Segments of the time series and each datum in the time series are classified using decision trees. Depending on the classification of the time series, a final quality score (or ...

Coherent Doppler lidar signal spectrum with wind turbulence

The average signal spectrum (periodogram) for coherent Doppler lidar is calculated for a turbulent wind field. Simple approximations are compared with the exact calculation. The effects of random errors in the zero velocity reference, the effects of averaging spectral estimates by use of multiple lidar pulses, and the effects of the range dependence of the lidar signal power over the range gate are included. For high spatial resolution measurements the lidar signal power is concentrated around one spectral estimate (spectral bin), and correct interpretation of the contribution from turbulence is difficult because of the effects of spectral leakage. For range gates that are larger than the lidar pulse volume, the signal power is contained in many spectral bins and the effects of turbulence can be determined accurately for constant signal power over the range gate and for the far-field range dependence of the signal power.

A Horizontal Wind and Wind Confidence Algorithm for Doppler Wind Profilers

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 a...

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

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 an...