scholarly journals Three-way Calibration Checks Using Ground-Based, Ship-Based and Spaceborne Radars

2021 ◽  
Author(s):  
Alain Protat ◽  
Valentin Louf ◽  
Joshua Soderholm ◽  
Jordan Brook ◽  
William Ponsonby
Keyword(s):  
Weather Radar ◽  
Radar Observations ◽  
Capital Cities ◽  
Radar Network ◽  
Monitoring Technique ◽  
Radar Networks ◽  
Calibration Accuracy ◽  
Global Precipitation Mission ◽  
Global Precipitation ◽  
Spaceborne Radar

Abstract. This study uses weather radar observations collected from Research Vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of 1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the coast and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar and each of the 7 operational radars is then estimated using collocated, gridded, radar observations to evaluate the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ~ 0.3 dB and ~ 1 dB, respectively).

scholarly journals Theory and Characterization of Weather Radar Networks

2009 ◽  
Vol 26 (3) ◽  
pp. 474-491 ◽  
Author(s):  
Francesc Junyent ◽  
V. Chandrasekar
Keyword(s):  
Weather Radar ◽  
Detection Sensitivity ◽  
Coverage Area ◽  
Engineering Research ◽  
Radar Network ◽  
Radar Networks ◽  
General Network ◽  
Network Characterization ◽  
Beam Height ◽  
Analytical Tools

Abstract A dense weather radar network is an emerging concept advanced by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). In a weather radar environment, the specific radar units employed and the network topology will influence the characteristics of the data obtained. To define this, a general framework is developed to describe the radar network space, and formulations are obtained that can be used for weather radar network characterization. The models developed are useful for quantifying and comparing the performance of different weather radar networks. Starting with system characteristics that are used to specify individual radars, a theoretical basis is developed to extend the concept to network configurations of interest. A general network elemental cell is defined and employed as the parameterized domain over which different coverage aspects (such as detection sensitivity, beam size, and minimum beam height) are studied using analytical tools developed in the paper. Other important parameters are the number of different radars with overlapping coverage at a given point in the network domain and the coverage area and number of radars of a network and its elemental cells. A combination of analytical and numerically derived expressions is employed to obtain these parameters for several configurations. The radar network characterization tools developed are applied to the comparison of individual radar and networked radar configurations of interest. The values used in the calculations illustrate the CASA Integrated Project 1 (IP1) radar network and are compared to other radar systems.

scholarly journals Calibration Accuracy of the Dual-Polarization Receivers of the C-Band Swiss Weather Radar Network

Atmosphere ◽  
2016 ◽  
Vol 7 (6) ◽  
pp. 76 ◽  
Author(s):  
Marco Gabella ◽  
Marco Boscacci ◽  
Maurizio Sartori ◽  
Urs Germann
Keyword(s):  
Weather Radar ◽  
Dual Polarization ◽  
Radar Network ◽  
Calibration Accuracy

scholarly journals Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

2013 ◽  
Vol 13 (5) ◽  
pp. 1229-1241 ◽  
Author(s):  
E. Picciotti ◽  
F. S. Marzano ◽  
E. N. Anagnostou ◽  
J. Kalogiros ◽  
Y. Fessas ◽  
...  
Keyword(s):  
Decision Support ◽  
High Frequency ◽  
Weather Radar ◽  
Decision Support Tool ◽  
Dual Polarization ◽  
Radar Observations ◽  
Support Tool ◽  
Radar Network ◽  
X Band ◽  
Modelling System

Abstract. Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band) has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band) and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative \\mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5 (MM5) and the Army Corps of Engineers Hydrologic Engineering Center hydrologic and hydraulic modelling chain. The characteristics of this tool make it ideal to support flood monitoring and forecasting within urban environment and small-scale basins. Preliminary results, carried out during a field campaign in Moldova, showed that the mini-radar based hydro-meteorological forecasting system can constitute a suitable solution for local flood warning and civil flood protection applications.

Objective Optimization of Weather Radar Networks for Low-Level Coverage Using a Genetic Algorithm

2012 ◽  
Vol 29 (6) ◽  
pp. 807-821 ◽  
Author(s):  
James M. Kurdzo ◽  
Robert D. Palmer
Keyword(s):  
Genetic Algorithm ◽  
Lifetime Cost ◽  
Optimization Technique ◽  
Weather Radar ◽  
The United States ◽  
Analysis Tool ◽  
Low Level ◽  
Mountainous Regions ◽  
Radar Network ◽  
Radar Networks

Abstract The current Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network is approaching 20 years of age, leading researchers to begin exploring new opportunities for a next-generation network in the United States. With a vast list of requirements for a new weather radar network, research has provided various approaches to the design and fabrication of such a network. Additionally, new weather radar networks in other countries, as well as networks on smaller scales, must balance a large number of variables in order to operate in the most effective way possible. To offer network designers an objective analysis tool for such decisions, a coverage optimization technique, utilizing a genetic algorithm with a focus on low-level coverage, is presented. Optimization is achieved using a variety of variables and methods, including the use of climatology, population density, and attenuation due to average precipitation conditions. A method to account for terrain blockage in mountainous regions is also presented. Various combinations of multifrequency radar networks are explored, and results are presented in the form of a coverage-based cost–benefit analysis, with considerations for total network lifetime cost.

scholarly journals Climatological Beam Propagation Conditions for China’s Weather Radar Network

2018 ◽  
Vol 57 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Hongyan Wang ◽  
Gaili Wang ◽  
Liping Liu
Keyword(s):  
Electromagnetic Waves ◽  
Beam Propagation ◽  
Weather Radar ◽  
Weather Forecast ◽  
Temporal Variations ◽  
Radar Observations ◽  
Radar Network ◽  
Radar Echo ◽  
Forecast Models ◽  
Numerical Weather Forecast

AbstractThe vertical refractivity gradient (VRG) is critical to weather radar beam propagation. The most common method of calculating beam paths uses the 4/3 Earth radius model, which corresponds to standard refraction conditions. In the present work, to better document propagation conditions for radar electromagnetic waves, which is essential for hydrology and numerical weather forecast models to more fully benefit from observations taken from the new-generation weather radar network in China, VRG spatial and temporal variations in the first kilometers above the surface are explored using 6-yr sounding observations. Under the effects of both regional climatic and topographic conditions, VRG values for most of the radars are generally smaller than those of the standard conditions for much of the year. There are similar or slightly larger values at only a few radar sites. Smaller VRG values are more frequent and widespread, especially during rainy seasons when weather radar observations are important. In such conditions, beam heights estimated using standard atmospheric refraction are overestimated relative to actual heights for most of the radars. Underestimates are much less common and of much shorter duration. However, height deviations are acceptable for being well within the uncertainty of radar echo height owing to the ~1° beamwidth. In coastal areas and the middle and lower reaches of the Yangtze River, radar observations should be applied with much more caution because of the greater risk of beam blockage and clutter contamination.

scholarly journals Weather Radar Network Benefit Model for Flash Flood Casualty Reduction

2020 ◽  
Vol 59 (4) ◽  
pp. 589-604 ◽  
Author(s):  
John Y. N. Cho ◽  
James M. Kurdzo
Keyword(s):  
Decision Process ◽  
Flash Flood ◽  
Weather Radar ◽  
Regression Analyses ◽  
Flood Warning ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Radar Networks ◽  
Precipitation Estimation ◽  
Meteorological Radar

ABSTRACTA monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash flood–based benefit of $316 million (M) yr−1. The remaining benefit pools are more modest ($13 M yr−1 for coverage improvement and $69 M yr−1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar network’s effectiveness in supporting the flash flood warning decision process.

scholarly journals Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

2015 ◽  
Vol 32 (7) ◽  
pp. 1341-1355 ◽  
Author(s):  
S. J. Rennie ◽  
M. Curtis ◽  
J. Peter ◽  
A. W. Seed ◽  
P. J. Steinle ◽  
...  
Keyword(s):  
Weather Radar ◽  
Training Data ◽  
Training Dataset ◽  
Bayes Classifier ◽  
Threshold Method ◽  
Ground Clutter ◽  
Radar Network ◽  
Routine Monitoring ◽  
Single Polarization ◽  
Anomalous Propagation

AbstractThe Australian Bureau of Meteorology’s operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

TOWARDS AN AUTOMATED WEATHER RADAR NETWORK

Weather ◽  
1974 ◽  
Vol 29 (6) ◽  
pp. 202-216 ◽  
Author(s):  
B. C. Taylor ◽  
K. A. Browning
Keyword(s):  
Weather Radar ◽  
Radar Network

scholarly journals Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

2019 ◽  
Author(s):  
Irene Crisologo ◽  
Maik Heistermann
Keyword(s):  
Moving Average ◽  
Weighted Average ◽  
Tropical Rainfall Measuring Mission ◽  
The Philippines ◽  
Weighted Averaging ◽  
Radar Observations ◽  
Precipitation Measurement ◽  
Formal Procedure ◽  
Calibration Techniques ◽  
Global Precipitation

Abstract. Many institutions struggle to tap the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines which is much affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA), in addition to beam blockage. This extension becomes vital for radars that operate at C- or X-band. Correspondingly, the study setup includes a C-band radar which substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

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