University Corporation for Atmospheric Research

Robert Kent Goodrich

Lawrence B Cornman

Corinne Sue Morse

Eugene David Albo

Scott Landolt

Nathaniel Beagley

Andrew Gaydos

Richard A Weekley

Shelly D Dalton

A method for determining a quality controlled sensor set from a redundant sensor set comprising calculating a first time correlation coefficient and a first autocorrelation coefficient based on a first sensor time series data, calculating a second time correlation coefficient and a second autocorrelation coefficient based on a second sensor time series data, calculating a first and second sensor correlation coefficient based on the first sensor time series data and the second sensor time series data, and determining the quality controlled sensor set with a highest confidence level.

Method and system for providing quality controlled data from a redundant sensor system

A processing system uses image processing technology to classify features in time series data. In atmospheric applications, the features can be used to detect outliers and/or failure modes in time series data from atmospheric measurement systems. The processing system is configured to: 1) process the time series data with a plurality of membership functions to generate a plurality of hypersurfaces, 2) process the hypersurfaces to generate a composite hypersurface, 3) process the composite hypersurface to identify clusters, and 4) process the clusters to classify the features.

Feature classification for time series data

A multi-stage processing system receives data from signals that indicate information related to scatterers, such as signals from a Doppler scanning system. The multi-stage processing system tracks allowed processing time for the data. The multi-stage processing system performs a first stage of processing for the data to generate first estimates of spectral moments for the signals. The multi-stage processing system performs additional stages of processing for the data as the allowed processing time permits and stops the additional stages of processing for the data when the allowed processing time expires. The additional stages of processing may comprise generating second estimates for at least some of the spectral moments. The additional stages of processing may use methods having increasing complexity and accuracy.

Multi-stage processing for efficient and accurate spectral moment estimation

The computation system of the present invention comprises an improved method of moment estimation for devices which measure spectra as a function of range or time. The preferred embodiment of this system is illustrated as part of an automated meteorological monitoring system for the accurate real time detection of meteorological phenomena, such as winds, wind shear and turbulence. This automated meteorological monitoring system uses a standard weather radar transmitter to scan a predetermined volume of space with a stream of radar pulses to determine the characteristics of meteorological phenomena that are extant in the predetermined volume. The computation system of the present invention utilizes novel signal processing algorithms in the improved method of moment estimation to excise the valid data from the returns echoes, which are corrupted by the presence of contaminating signals. Separating the valid data from the noise in this manner improves the responsiveness and accuracy of the system in which this method is implemented.

Method of moment estimation and feature extraction for devices which measure spectra as a function of range or time

The computation system of the present invention comprises an improved method of moment estimation for devices which measure spectra as a function of range or time. The preferred embodiment of this system is illustrated as part of an automated meteorological monitoring system for the accurate real time detection of meteorological phenomena, such as winds, wind shear and turbulence. This automated meteorological monitoring system uses a standard weather radar transmitter to scan a predetermined volume of space with a stream of radar pulses to determine the characteristics of meteorological phenomena that are extant in the predetermined volume. The computation system of the present invention utilizes novel signal processing algorithms in the improved method of moment estimation to excise the valid data from the return echoes, which are corrupted by the presence of contaminating signals. Separating the valid data from the noise in this manner improves the responsiveness and accuracy of the system in which this method is implemented.

Method of moment estimation and feature extraction for devices which

A radar station utilizes fuzzy logic processing to detect atmospheric microburst events. The detection process accesses radar velocity measurements to calculate wind shear and produce a wind shear likelihood image. Reflectivity measurements are used to create a reflectivity likelihood image as an indicator of storm activity. A clutter image of stationary objects close to the radar is used to create a clutter likelihood image as an indicator of false alarms. A weighting factor is applied to each set of wind shear, reflectivity, and clutter likelihood data, and the values are combined to produce a combined likelihood image. The combined likelihood image is subjected to averaging over a fixed area to eliminate spurious readings, and positions having a sufficient size in combination with a likelihood value exceeding a minimum threshold amount are selected as microburst events.

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Boulder, CO, United States of America

Robert Kent Goodrich