Robert K. Goodrich

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).

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 ...

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...