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 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.

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 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 Pointing Accuracy of an Operational Polarimetric Weather Radar

2019 ◽  
Vol 11 (9) ◽  
pp. 1115 ◽  
Author(s):  
Michael Frech ◽  
Theodor Mammen ◽  
Bertram Lange
Keyword(s):  
Mechanical Design ◽  
Weather Radar ◽  
Life Time ◽  
Radar Data ◽  
Azimuthal Variation ◽  
Weather Radars ◽  
Meteorological Observatory ◽  
Pointing Accuracy ◽  
Polarimetric Weather Radar ◽  
Angle Encoder

Exact navigation of detected radar signals is crucial for usage of radar data in meteorological applications. The antenna pointing accuracy in azimuth and elevation of a polarimetric weather research radar depending on position of the sun is assessed using dedicated solar boxscans in a sequence of 10 min. The research radar of the German Meteorological Service (Deutscher Wetterdienst, DWD) is located at the meteorological observatory Hohenpeissenberg. It is identical to the 17 weather radars of the German weather radar network. A non-linear azimuthal variation of azimuthal pointing bias of up to 0.1 ∘ is found, which is significant as this is commonly viewed as the target pointing accuracy. This azimuthal variation can be attributed to the mechanical design of the drive train with the angle encoder. This includes the inherent backlash of the gear-drive assembly. The pointing bias estimates based on over 1000 boxscans from 26 days show a small case by case variability, which indicates that dedicated solar boxscans from one day are sufficient to characterize the pointing performance of a particular system. An azimuth and elevation range that is covered with this approach is limited and dependent on the time of the year. At Hohenpeißenberg, an azimuth range up to 50–300 ∘ was covered around summer solstice and about 90 boxscans were acquired. It is shown that the pointing bias based on solar boxscan data are consistent with results from the operational assessment of pointing bias using solar hits from operational scanning if we take into account the fact that the DWD operational scan definition has only a maximum elevation of 25 ∘ . The analysis of a full diurnal cycle of boxscans from four operational radar system shows that the azimuthal dependence of azimuth bias needs to be evaluated individually for each system. For one of the systems, the azimuthal variation of the pointing bias of about 0.2 ∘ seems related to the bull gear. A difference of the pointing bias for the horizontal and vertical polarization is an indication of beam squint and, eventually, that of a feed misalignment. Beam squint and, as such, the quality of the antenna assembly can easily be monitored with this method during the life-time of a weather radar.

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 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.

Design of Lifting Electromechanical System for Onboard X-Band Weather Radar Antenna and Data Filtering Analysis

2013 ◽  
Vol 579-580 ◽  
pp. 740-744
Author(s):  
Xu Hui Wei ◽  
Bin Hua Yang ◽  
Wei Dong Lu ◽  
Ling Wen Kong
Keyword(s):  
Modular Design ◽  
Weather Radar ◽  
Radar Data ◽  
Object Oriented Programming ◽  
Electromechanical System ◽  
System Stability ◽  
Data Management System ◽  
Data Filtering ◽  
X Band ◽  
Radar Antenna

Onboard X-Band Weather Radar and data filter prediction is one of core services of the Xinjiang meteorological emergency system. Based on installation conditions provided by IVECO trunk, the structure of X-band radar antenna, lifting height and antenna work requirements, combined with the modular design concept, this paper developed the X-band weather radar antenna dedicated lifting system. This system consists of radar antenna base platform, lifting rack rails, rollers, sprockets, cylinder etc. when working, the system can not only utilize the synchronizing control strategy to ensure the system stability but also quickly set up an antenna. Based on the design of Onboard X-band Weather radar antenna lifting electromechanical system, we developed the radar data management system. In this software, Object-oriented programming language, multi-threaded programming methods and software modularity method is utilized to design the platform architecture, GIS controls and dynamic mesh technology are used to make the radar map, and based on the principle of Kalman filtering, intelligent prediction approaches are studied. Computer numerical simulation and experimental results show that the electromechanical system developed by this paper has good performance and utilized the data filtering technology to provide the reliable method for meteorological warning.

scholarly journals Quality Control of Antenna Alignment and Receiver Calibration Using the Sun: Adaptation to Midrange Weather Radar Observations at Low Elevation Angles

2015 ◽  
Vol 32 (5) ◽  
pp. 927-942 ◽  
Author(s):  
Patricia Altube ◽  
Joan Bech ◽  
Oriol Argemí ◽  
Tomeu Rigo
Keyword(s):  
Quality Control ◽  
Goodness Of Fit ◽  
Local Area Network ◽  
Control Method ◽  
Selection Process ◽  
Weather Radar ◽  
Area Network ◽  
The Sun ◽  
Ground Clutter ◽  
Radar Antenna

AbstractA quality control method for combined online monitoring of weather radar antenna pointing biases and receiver calibration using solar signals detected by an operational radar is adapted for application to midrange radar data (80–150 km). As the original method was developed using long-range data, additional criteria based on robust statistical estimators are imposed in the sun signature detection and selection process, allowing to discard observations biased by ground clutter or precipitation and to remove very influential outliers. The validity ranges of the physical model describing the solar interferences detected by the scanning radar antenna are explicitly defined and an equation for estimation of the effective scanning width in reception is provided in a thorough theoretical derivation. The method proposed reveals its sensitivity to changes in the antenna pointing accuracy and receiver calibration when applied to operational data obtained with three C-band radars during one year. A comparative study on the goodness of fit between a three- and a five-parameter model highlights the effect on the stability and accuracy of the antenna and receiver parameters retrieved for each radar system, considering the dissimilar information content of the observations collected by each radar. The performance of the proposed methodology under the effects of the presence of ground clutter and radio local area network interferences is discussed in the results presented.

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.

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.

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