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 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 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.

scholarly journals The Eyjafjöll explosive volcanic eruption from a microwave weather radar perspective

2011 ◽  
Vol 11 (4) ◽  
pp. 12367-12409 ◽  
Author(s):  
F. S. Marzano ◽  
M. Lamantea ◽  
M. Montopoli ◽  
S. Di Fabio ◽  
E. Picciotti
Keyword(s):  
Volcanic Ash ◽  
Discharge Rate ◽  
Volcanic Eruptions ◽  
Weather Radar ◽  
Radar Data ◽  
Maximum Height ◽  
Unique Contribution ◽  
Near Surface ◽  
Weather Radars ◽  
Ash Fallout

Abstract. The sub-glacial Eyjafjöll explosive volcanic eruptions of April and May 2010 are analyzed and quantitatively interpreted by using ground-based weather radar data and volcanic ash radar retrieval (VARR) technique. The Eyjafjöll eruptions have been continuously monitored by the Keflavík C-band weather radar, located at a distance of about 155 km from the volcano vent. Considering that the Eyjafjöll volcano is approximately 20 km far from the Atlantic Ocean and that the northerly winds stretched the plume toward the mainland Europe, weather radars are the only means to provide an estimate of the total ejected tephra. The VARR methodology is summarized and applied to available radar time series to estimate the plume maximum height, ash particle category, ash volume, ash fallout and ash concentration every 5 min near the vent. Estimates of the discharge rate of eruption, based on the retrieved ash plume top height, are provided together with an evaluation of the total erupted mass and volume. Deposited ash at ground is also retrieved from radar data by empirically reconstructing the vertical profile of radar reflectivity and estimating the near-surface ash fallout. Radar-based retrieval results cannot be compared with ground measurements, due to the lack of the latter, but further demonstrate the unique contribution of these remote sensing products to the understating and modelling of explosive volcanic ash eruptions.

Operational Monitoring of Weather Radar Receiving Chain Using the Sun

2010 ◽  
Vol 27 (1) ◽  
pp. 159-166 ◽  
Author(s):  
Iwan Holleman ◽  
Asko Huuskonen ◽  
Mikko Kurri ◽  
Hans Beekhuis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
The Sun ◽  
Volume Data ◽  
Operational Monitoring ◽  
Flux Data ◽  
Weather Radars ◽  
Receiver System ◽  
The Stability ◽  
Radar Antenna

Abstract A method for operational monitoring of a weather radar receiving chain, including the antenna gain and the receiver, is presented. The “online” method is entirely based on the analysis of sun signals in the polar volume data produced during operational scanning of weather radars. The method is an extension of that for determining the weather radar antenna pointing at low elevations using sun signals, and it is suited for routine application. The solar flux from the online method agrees very well with that obtained from “offline” sun tracking experiments at two weather radar sites. Furthermore, the retrieved sun flux is compared with data from the Dominion Radio Astrophysical Observatory (DRAO) in Canada. Small biases in the sun flux data from the Dutch and Finnish radars (between −0.93 and +0.47 dB) are found. The low standard deviations of these sun flux data against those from DRAO (0.14–0.20 dB) demonstrate the stability of the weather radar receiving chains and of the sun-based online monitoring. Results from a daily analysis of the sun signals in online radar data can be used for monitoring the alignment of the radar antenna and the stability of the radar receiver system. By comparison with the observations from a sun flux monitoring station, even the calibration of the receiving chain can be checked. The method presented in this paper has great potential for routine monitoring of weather radars in national and international networks.

scholarly journals Calibration of radar differential reflectivity using quasi-vertical profiles

2021 ◽  
Author(s):  
Daniel Sanchez-Rivas ◽  
Miguel A. Rico-Ramirez
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Vertical Profiles ◽  
Weather Radars ◽  
Precipitation Estimates ◽  
Novel Method ◽  
One Year ◽  
The Uk ◽  
Quantitative Precipitation Estimates ◽  
Differential Reflectivity

Abstract. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on vertical observations of ZDR taken in rain, in which ZDR values close to 0 dB are expected. However, not all weather radar systems are capable of producing vertical pointing measurements. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles of polarimetric variables. The method is applied to radar data collected through one year of precipitation events by two operational C-band weather radars in the UK. The proposed method proves effective in achieving the required accuracy of 0.1 dB for the calibration of ZDR as the calibration results are consistent with the traditional method based on vertical profiles. Additionally, the method is independently evaluated using disdrometers located near the radar sites. The results showed a good agreement between disdrometer-derived and radar-calibrated ZDR measurements.

scholarly journals Polarisation basis transformation of weather radar measurements in the power domain

2009 ◽  
Vol 7 ◽  
pp. 279-284
Author(s):  
T. Otto ◽  
J. Lu ◽  
M. Chandra
Keyword(s):  
Transfer Functions ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Remote Sensing ◽  
Weather Radars ◽  
Huge Data ◽  
Power Domain ◽  
Radar Measurements ◽  
Polarimetric Weather Radar ◽  
Power Measurements

Abstract. Polarisation diversity in radar remote sensing proved to be very successful in a variety of applications. Hydrometeors as raindrops or ice crystals are anisotropic radar targets giving rise to the use of polarisation diversity in weather radars. One advanced polarimetric weather radar is DLR's POLDIRAD in Oberpfaffenhofen. The huge data archive of this radar consists mainly of power measurements at diverse polarisation bases. This study investigates the possibility to apply the polarisation basis transformation directly on power measurements. As a result, empirical transfer functions for the change of the polarisation basis of radar reflectivities are derived. To check their validity they are applied to appropriate polarimetric radar data from the POLDIRAD.

scholarly journals Radar hydrometeorology using a vertically pointing radar

2000 ◽  
Vol 4 (4) ◽  
pp. 565-580 ◽  
Author(s):  
I. D. Cluckie ◽  
R. J. Griffith ◽  
A. Lane ◽  
K. A. Tilford
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Temporal Variations ◽  
Recognition Algorithm ◽  
Melting Layer ◽  
Rain Gauges ◽  
Bright Band ◽  
Weather Radars ◽  
Quantitative Measurements ◽  
Time Correction

Abstract. A Vertically Pointing Radar (VPR) has been commissioned and deployed at a number of sites in southern England, to investigate numerically spatial and temporal variations in the vertical reflectivity profile (Zvp); particularly those associated with the intersection by the radar beam of a melting layer – the bright band. Comparisons with data from other instrumentation, notably with the S-band research radar at Chilbolton, but also with disdrometer data and rainfall measurements from a number of sophisticated rain gauges, show that VPR scans of the atmosphere provide detailed and reliable quantitative measurements of the Zvp. Analysis of a three year archive of Zvp data for Manchester has shown a bright band to be present in over 80% of rainfall events, highlighting the extent of the problem of bright band errors in scanning weather radar data. The primary characteristics of the bright band such as the height and magnitude (in dBZ) of the top, bottom and peak are identified objectively from VPR Zvp data by an automatic bright band recognition algorithm. It is envisaged that this approach could form the basis of an objective, automatic real time correction procedure for scanning weather radars. Keywords: Vertically Pointing Radar, weather radar, hydrometeorology, bright-band, melting-layer, vertical radar reflectivity

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

2012 ◽  
Vol 5 (3) ◽  
pp. 547-555 ◽  
Author(s):  
X. Muth ◽  
M. Schneebeli ◽  
A. Berne
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Swiss Alps ◽  
The Sun ◽  
Sources Of Information ◽  
Weather Radars ◽  
Radar Systems ◽  
Pointing Accuracy ◽  
Instrumental Bias ◽  
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 leveling might be difficult to ensure. The sun is a source of microwave radiation, which can be detected by weather radars and used for accurate positioning of radar data. This paper presents a technique based on the similarity between theodolites and radar systems as well as on the sun echoes to quantify and hence correct 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 accuracy of the proposed method.

scholarly journals Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)

2021 ◽  
Author(s):  
Katarzyna Ośródka ◽  
Jan Szturc
Keyword(s):  
Quality Control ◽  
Wind Turbines ◽  
Geometric Analysis ◽  
Weather Radar ◽  
Radar Data ◽  
Control Algorithms ◽  
Weather Radars ◽  
Abstract Data ◽  
The Individual ◽  
New Algorithms

Abstract. Data from weather radars are commonly used in meteorology and hydrology, but they are burdened with serious disturbances, especially due to the appearance of numerous non-meteorological echoes. For this reason, these data are subject to advanced quality control algorithms. The paper presents a significant improvement of the RADVOL-QC system made necessary by the appearance of an increasing number of various disturbances. New algorithms are mainly addressed to the occurrence of clutter caused by wind turbines (DP.TURBINE algorithm) and other terrain obstacles (DP.NMET algorithm), as well as various forms of echoes caused by the interaction of a radar beam with RLAN signals (set of SPIKE algorithms). The individual algorithms are based on the employment of polarimetric data as well as on the geometric analysis of echo patterns. In the paper the algorithms are described along with examples of their performance and an assessment of their effectiveness, and finally examples of the performance of the whole system are discussed.

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