scholarly journals Automatic Detection of Wind Turbine Clutter for Weather Radars

2010 ◽  
Vol 27 (11) ◽  
pp. 1868-1880 ◽  
Author(s):  
Kenta Hood ◽  
Sebastián Torres ◽  
Robert Palmer
Keyword(s):  
Wind Turbine ◽  
Weather Radar ◽  
Real Data ◽  
Radar Data ◽  
Detection Algorithm ◽  
Weather Data ◽  
Atmospheric Conditions ◽  
Fuzzy Logic Algorithm ◽  
Weather Radars ◽  
Anomalous Propagation

Abstract Wind turbines cause contamination of weather radar signals that is often detrimental and difficult to distinguish from cloud returns. Because the turbines are always at the same location, it would seem simple to identify where wind turbine clutter (WTC) contaminates the weather radar data. However, under certain atmospheric conditions, anomalous propagation of the radar beam can occur such that WTC corrupts weather data on constantly evolving locations, or WTC can be relatively weak such that contamination on predetermined locations does not occur. Because of the deficiency of using turbine locations as a proxy for WTC, an effective detection algorithm is proposed to perform automatic flagging of contaminated weather radar data, which can then be censored or filtered. Thus, harmful effects can be reduced that may propagate to automatic algorithms or may hamper the forecaster’s ability to issue timely warnings. In this work, temporal and spectral features related to WTC signatures are combined in a fuzzy logic algorithm to classify the radar return as being contaminated by WTC or not. The performance of the algorithm is quantified using simulations and the algorithm is applied to a real data case from the radar facility in Dodge City, Kansas (KDDC). The results illustrate that WTC contamination can be detected automatically, thereby improving the quality control of weather radar data.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

2009 ◽  
Vol 26 (7) ◽  
pp. 1181-1197 ◽  
Author(s):  
J. C. Hubbert ◽  
M. Dixon ◽  
S. M. Ellis
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Weather Radar ◽  
Radar Data ◽  
Weather Data ◽  
Fuzzy Logic Algorithm ◽  
Zero Velocity ◽  
Ground Clutter ◽  
Phase Alignment ◽  
Real Time Identification

Abstract The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

scholarly journals Fireworks on Weather Radar and Camera

2020 ◽  
Vol 101 (2) ◽  
pp. E90-E108
Author(s):  
D. S. Zrnić ◽  
P. Zhang ◽  
V. Melnikov ◽  
E. Kabela
Keyword(s):  
High Sensitivity ◽  
Weather Radar ◽  
Video Camera ◽  
Radar Data ◽  
Maximum Diameter ◽  
Visible Image ◽  
Weather Radars ◽  
Technical Developments ◽  
Maximum Reflectivity ◽  
Operation Center

Abstract High-sensitivity weather radars easily detect nonmeteorological phenomena characterized by weak radar returns. Fireworks are the example presented here. To understand radar observations, an experiment was conducted in which the National Severe Storms Laboratory (NSSL)’s research (3-cm wavelength) dual-polarization radar and a video camera were located at 1 km from fireworks in Norman, Oklahoma. The fireworks from the 4 July 2017 celebration were recorded by both instruments. The experiment is described. Few bursts recorded by the camera are analyzed to obtain the height of the explosion, its maximum diameter, number of stars, and the duration of the visible image. Radar volume scans are examined to characterize the height of the observation, the maximum reflectivity, and its distribution with height. The fireworks location is close to the Terminal Doppler Weather Radar (TDWR) that operates in single polarization at a 5-cm wavelength and monitors hazardous weather over the Oklahoma City airport. A third radar with data from the event is the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman. It has a wavelength of 10 cm and supports technical developments at the Radar Operation Center. Reflectivity factors measured by the three radars are compared to infer the size of dominant scatterers. The polarimetric characteristics of fireworks returns are analyzed. Although these differ from those of precipitation, they are indistinguishable from insect returns. Radar observation of larger fireworks in Fort Worth, Texas, with a WSR-88D is included and compared with the observations of the smaller fireworks in Norman. We expect the detectability of explosions would be similar as of fireworks. Pinpointing locations would be useful to first responders, or air quality forecasters. A benefit of fireworks recognition in weather radar data is that it can prevent contamination of precipitation accumulations.

scholarly journals Scan-to-Scan Correlation of Weather Radar Signals to Identify Ground Clutter

2013 ◽  
Vol 10 (4) ◽  
pp. 855-859 ◽  
Author(s):  
Yinguang Li ◽  
Guifu Zhang ◽  
Richard Doviak ◽  
Darcy Saxion
Keyword(s):  
Spatial Resolution ◽  
Correlation Time ◽  
Correlation Method ◽  
Weather Radar ◽  
Detection Algorithm ◽  
Weather Radars ◽  
Ground Clutter ◽  
Radar Echoes ◽  
Radar Signals

The scan-to-scan correlation method to discriminate weather signals from ground clutter, described in this letter, takes advantage of the fact that the correlation time of radar echoes from hydrometeors is typically much shorter than that from ground objects. In this letter, the scan-to-scan correlation method is applied to data from the WSR-88D, and its results are compared with those produced by the WSR-88D's ground clutter detector. A subjective comparison with an operational clutter detection algorithm used on the network of weather radars shows that the scan-to-scan correlation method produces a similar clutter field but presents clutter locations with higher spatial resolution.

scholarly journals Staggered-PRT Sequences for Doppler Weather Radars. Part I: Spectral Analysis Using the Autocorrelation Spectral Density

2017 ◽  
Vol 34 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Sebastián M. Torres ◽  
David A. Warde
Keyword(s):  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Weather Radar ◽  
Real Data ◽  
Pulse Repetition ◽  
Spectral Processing ◽  
Weather Radars ◽  
Power Spectral ◽  
Repetition Time

AbstractThe autocorrelation spectral density (ASD) was introduced as a generalization of the classical periodogram-based power spectral density (PSD) and as an alternative tool for spectral analysis of uniformly sampled weather radar signals. In this paper, the ASD is applied to staggered pulse repetition time (PRT) sequences and is related to both the PSD and the ASD of the underlying uniform-PRT sequence. An unbiased autocorrelation estimator based on the ASD is introduced for use with staggered-PRT sequences when spectral processing is required. Finally, the strengths and limitations of the ASD for spectral analysis of staggered-PRT sequences are illustrated using simulated and real data.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

scholarly journals A sun-tracking method to improve the pointing accuracy of weather radar

2011 ◽  
Vol 4 (4) ◽  
pp. 5569-5595
Author(s):  
X. Muth ◽  
M. Schneebeli ◽  
A. Berne
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Swiss Alps ◽  
The Sun ◽  
Sources Of Information ◽  
Weather Radars ◽  
Pointing Accuracy ◽  
Instrumental Bias ◽  
Radar Antenna ◽  
Land Cover Maps

Abstract. Accurate positioning of data collected by a weather radar is of primary importance for their appropriate georeferencing, which in turn makes it possible to combine those with additional sources of information (topography, land cover maps, meteorological simulations from numerical weather models to list a few). This issue is especially acute for mobile radar systems, for which accurate and stable levelling might be difficult to ensure. The sun is a source of microwave radiation, which can be detected by weather radars and used for the accurate positioning of the radar data. This paper presents a technique based on the sun echoes to quantify and hence correct for the instrumental errors which can affect the pointing accuracy of radar antenna. The proposed method is applied to data collected in the Swiss Alps using a mobile X-band radar system. The obtained instrumental bias values are evaluated by comparing the locations of the ground echoes predicted using these bias estimates with the observed ground echo locations. The very good agreement between the two confirms the good accuracy of the proposed method.

scholarly journals Estimating reflectivity values from wind turbines for analyzing the potential impact on weather radar services

2015 ◽  
Vol 8 (2) ◽  
pp. 1477-1509
Author(s):  
I. Angulo ◽  
O. Grande ◽  
D. Jenn ◽  
D. Guerra ◽  
D. de la Vega
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Weather Radar ◽  
Radar Signal ◽  
Weather Radars ◽  
Potential Impact ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Do So

Abstract. The World Meteorological Organization (WMO) has repeatedly expressed concern over the increasing number of impact cases of wind turbine farms on weather radars. Since nowadays signal processing techniques to mitigate Wind Turbine Clutter (WTC) are scarce, the most practical approach to this issue is the assessment of the potential interference from a wind farm before it is installed. To do so, and in order to obtain a WTC reflectivity model, it is crucial to estimate the Radar Cross Section (RCS) of the wind turbines to be built, which represents the power percentage of the radar signal that is backscattered to the radar receiver. This paper first characterizes the RCS of wind turbines in the weather radar frequency bands by means of computer simulations based on the Physical Optics theory, and then proposes a simplified model to estimate wind turbine RCS values. This model is of great help in the evaluation of the potential impact of a certain wind farm on the weather radar operation.

scholarly journals Polarimetric radar characteristics of lightning initiation and propagating channels

2019 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Nicolau Pineda ◽  
Nikola Besic ◽  
Jacopo Grazioli ◽  
Alessandro Hering ◽  
...  
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Propagation Path ◽  
Atmospheric Conditions ◽  
Lightning Discharges ◽  
Mapping Array ◽  
Lightning Initiation ◽  
The Alps ◽  
Polarimetric Weather Radar ◽  
First Time

Abstract. In this paper we present an analysis of a large dataset of lightning and polarimetric weather radar data collected in the course of a lightning measurement campaign that took place in the summer of 2017 in the area surrounding the Säntis mountain, in the northeastern part of Switzerland. For this campaign, and for the first time in the Alps, a lightning mapping array (LMA) was deployed. The main objective of the campaign was to study the atmospheric conditions leading to lightning production with particular focus on the lightning discharges generated due to the presence of the 124 m tall Säntis telecommunications tower. In this paper we relate LMA VHF sources data with co-located radar data in order to characterize the main features (location, timing, polarimetric signatures, etc.) of both the flash's origin and its propagation path. We provide this type of analysis first for the whole data and then we separate the datasets into intra-cloud and cloud-to-ground flashes (and within this category positive and negative flashes) and also upward lightning. We show that polarimetric weather radar data can be helpful in determining regions were lightning is more likely to occur but that lightning climatology and/or knowledge of the orography and man-made structures is also relevant.

Sign in / Sign up

Export Citation Format

Share Document