Based on the General Active Broadband Marking Machine Radar Calibration Research

2012 ◽  
Author(s):  
Dongli Liu ◽  
Jun Teng ◽  
Xiaofang Shao
Keyword(s):  
Radar Calibration

Directional borehole radar calibration

2009 ◽  
Vol 67 (4) ◽  
pp. 352-360 ◽  
Author(s):  
O. Borchert ◽  
K. Behaimanot ◽  
A. Glasmachers
Keyword(s):  
Borehole Radar ◽  
Radar Calibration

scholarly journals On the Spectral and Polarimetric Signatures of a Bright Scatterer before and after Hardware Replacement

2021 ◽  
Vol 13 (5) ◽  
pp. 919
Author(s):  
Marco Gabella
Keyword(s):  
Low Noise ◽  
Low Noise Amplifiers ◽  
Doppler Velocity ◽  
Data Set ◽  
Differential Phase Shift ◽  
Spectral Signatures ◽  
Average Value ◽  
Before And After ◽  
Radar Calibration ◽  
Small Dispersion

A previous study has used the stable and peculiar echoes backscattered by a single “bright scatterer” (BS) during five winter days to characterize the hardware of C-band, the dual-polarization radar located at Monte Lema (1625 m altitude) in Southern Switzerland. The BS is the 90 m tall metallic tower on Cimetta (1633 m altitude, 18 km range). In this note, the statistics of the echoes from the BS were derived from other ten dry days with normal propagation conditions in winter 2015 and January 2019. The study confirms that spectral signatures, such as spectrum width, wideband noise and Doppler velocity, were persistently stable. Regarding the polarimetric signatures, the large values (with small dispersion) of the copolar correlation coefficient between horizontal and vertical polarization were also confirmed: the average value was 0.9961 (0.9982) in winter 2015 (January 2019); the daily standard deviations were very small, ranging from 0.0007 to 0.0030. The dispersion of the differential phase shift was also confirmed to be quite small: the daily standard deviation ranged from a minimum of 2.5° to a maximum of 5.3°. Radar reflectivities in both polarizations were typically around 80 dBz and were confirmed to be among the largest values observed in the surveillance volume of the Monte Lema radar. Finally, another recent 5-day data set from January 2020 was analyzed after the replacement of the radar calibration unit that includes low noise amplifiers: these five days show poorer characteristics of the polarimetric signatures and a few outliers affecting the spectral signatures. It was shown that the “historical” polarimetric and spectral signatures of a bright scatterer could represent a benchmark for an in-depth comparison after hardware replacements.

scholarly journals Absolute calibration method for frequency-modulated continuous wave (FMCW) cloud radars based on corner reflectors

2020 ◽  
Vol 13 (12) ◽  
pp. 6853-6875
Author(s):  
Felipe Toledo ◽  
Julien Delanoë ◽  
Martial Haeffelin ◽  
Jean-Charles Dupont ◽  
Susana Jorquera ◽  
...  
Keyword(s):  
Field Experiments ◽  
Continuous Wave ◽  
Intermediate Frequency ◽  
Absolute Calibration ◽  
Experimental Setup ◽  
Cloud Radar ◽  
Calibration Uncertainty ◽  
Radar Calibration ◽  
The Impact ◽  
Uncertainty Source

Abstract. This article presents a new cloud radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA) atmospheric observatory, located in Palaiseau, France, in the framework of the Aerosol Clouds Trace gases Research InfraStructure version 2 (ACTRIS-2) research and innovation program. The experimental setup includes 10 and 20 cm triangular trihedral targets installed at the top of 10 and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, temperature variations inside the radar, frequency-dependent losses in the receiver's intermediate frequency (IF), clutter and experimental setup misalignment. Setup misalignment is a source of bias, previously undocumented in the literature, that can have an impact of the order of tenths of a decibel in calibration retrievals of W-band radars. A detailed analysis enabled the quantification of the importance of each uncertainty source to the final cloud radar calibration uncertainty. The dominant uncertainty source comes from the uncharacterized reference target which reached 2 dB. Additionally, the analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is 10 times smaller than for the latter. Following the proposed methodology, it is possible to reduce the added contribution from all uncertainty terms, excluding the target characterization, down to 0.4 dB. Therefore, this procedure should enable the achievement of calibration uncertainties under 1 dB when characterized reflectors are available. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated, the cloud radar provides valid reflectivity values when sampling midtropospheric clouds. Thus, we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.

scholarly journals Close-range radar rainfall estimation and error analysis

2016 ◽  
Vol 9 (8) ◽  
pp. 3837-3850 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. Hazenberg ◽  
R. Uijlenhoet
Keyword(s):  
Positive Impact ◽  
Rain Gauge ◽  
Minor Role ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Sources Of Error ◽  
Ground Clutter ◽  
Received Power ◽  
Radar Calibration ◽  
Many Sources

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

UAV-Aided Weather Radar Calibration

2019 ◽  
Vol 57 (12) ◽  
pp. 10362-10375 ◽  
Author(s):  
Jiapeng Yin ◽  
Peter Hoogeboom ◽  
Christine Unal ◽  
Herman Russchenberg ◽  
Fred van der Zwan ◽  
...  
Keyword(s):  
Weather Radar ◽  
Radar Calibration

scholarly journals Seasonal Characteristics of Disdrometer-Observed Raindrop Size Distributions and Their Applications on Radar Calibration and Erosion Mechanism in a Semi-Arid Area of China

2020 ◽  
Vol 12 (2) ◽  
pp. 262 ◽  
Author(s):  
Zongxu Xie ◽  
Hanbo Yang ◽  
Huafang Lv ◽  
Qingfang Hu
Keyword(s):  
Rainfall Intensity ◽  
Shaanxi Province ◽  
Theoretical Formula ◽  
Size Distributions ◽  
Seasonal Characteristics ◽  
Different Seasons ◽  
Raindrop Size ◽  
Radar Calibration ◽  
The Impact ◽  
Semi Arid

Raindrop size distributions (DSDs) are the microphysical characteristics of raindrop spectra. Rainfall characterization is important to: (1) provide information on extreme rate, thus, it has an impact on rainfall related hazard; (2) provide data for indirect observation, model and forecast; (3) calibrate and validate the parameters in radar reflectivity-rainfall intensity (Z-R) relationships (quantitative estimate precipitation, QPE) and the mechanism of precipitation erosivity. In this study, the one-year datasets of raindrop spectra were measured by an OTT Parsivel-2 Disdrometer placed in Yulin, Shaanxi Province, China. At the same time, four TE525MM Gauges were also used in the same location to check the disdrometer-measured rainfall data. The theoretical formula of raindrop kinetic energy-rainfall intensity (KE-R) relationships was derived based on the DSDs to characterize the impact of precipitation characteristics and environmental conditions on KE-R relationships in semi-arid areas. In addition, seasonal rainfall intensity curves observed by the disdrometer of the area with application to erosion were characterized and estimated. The results showed that after quality control (QC), the frequencies of raindrop spectra data in different seasons varied, and rainfalls with R within 0.5–5 mm/h accounted for the largest proportion of rainfalls in each season. The parameters in Z-R relationships (Z = aRb) were different for rainfall events of different seasons (a varies from 78.3–119.0, and b from 1.8–2.1), and the calculated KE-R relationships satisfied the form of power function KE = ARm, in which A and m are parameters derived from rainfall shape factor μ. The sensitivity analysis of parameter A with μ demonstrated the applicability of the KE-R formula to different precipitation processes in the Yulin area.

A physically-based radar calibration method for improved rainfall estimation: application to the Fort-Collins flash flood of 1997

2009 ◽  
Vol 17 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Amvrossios C. Bagtzoglou ◽  
Justin M. Niedzialek ◽  
Sandrine A. Baun ◽  
Emmanouil N. Anagnostou ◽  
Fred L. Ogden
Keyword(s):  
Flash Flood ◽  
Calibration Method ◽  
Rainfall Estimation ◽  
Physically Based ◽  
Fort Collins ◽  
Radar Calibration

A Real-Time, Three-Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity, and Derived Products

2006 ◽  
Vol 21 (5) ◽  
pp. 802-823 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Kurt Hondl ◽  
Gregory J. Stumpf ◽  
Arthur Witt
Keyword(s):  
Real Time ◽  
Time Synchronization ◽  
Three Dimensional ◽  
Radar Data ◽  
Streaming Data ◽  
Weather Radars ◽  
Beam Geometry ◽  
Time Stamps ◽  
Scalar Products ◽  
Radar Calibration

Abstract With the advent of real-time streaming data from various radar networks, including most Weather Surveillance Radars-1988 Doppler and several Terminal Doppler Weather Radars, it is now possible to combine data in real time to form 3D multiple-radar grids. Herein, a technique for taking the base radar data (reflectivity and radial velocity) and derived products from multiple radars and combining them in real time into a rapidly updating 3D merged grid is described. An estimate of that radar product combined from all the different radars can be extracted from the 3D grid at any time. This is accomplished through a formulation that accounts for the varying radar beam geometry with range, vertical gaps between radar scans, the lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration, and inaccurate time stamps on radar data. Techniques for merging scalar products like reflectivity, and innovative, real-time techniques for combining velocity and velocity-derived products are demonstrated. Precomputation techniques that can be utilized to perform the merger in real time and derived products that can be computed from these three-dimensional merger grids are described.

Sign in / Sign up

Export Citation Format

Share Document