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.

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 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 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 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 Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground based weather radar network over Sweden

2015 ◽  
Vol 8 (8) ◽  
pp. 8157-8189
Author(s):  
L. Norin ◽  
A. Devasthale ◽  
T. S. L'Ecuyer ◽  
N. B. Wood ◽  
M. Smalley
Keyword(s):  
High Latitude ◽  
Precipitation Amount ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Polar Regions ◽  
Rain Gauges ◽  
Radar Network ◽  
Skill Scores ◽  
Satellite Sensors ◽  
Observing Systems

Abstract. To be able to estimate snowfall accurately is important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain-gauges to estimate precipitation in this context. The Cloud Profiling Radar (CPR) onboard CloudSat is especially proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and ability to provide near-global vertical structure. The importance of having snowfall estimates from CloudSat/CPR further increases in the high latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. Here we intercompared snowfall estimates from two observing systems, CloudSat and Swerad, the Swedish national weather radar network. Swerad offers one of the best calibrated data sets of precipitation amount at very high latitudes that are anchored to rain-gauges and that can be exploited to evaluate usefulness of CloudSat/CPR snowfall estimates in the polar regions. In total 7.2×105 matchups of CloudSat and Swerad over Sweden were inter-compared covering all but summer months (October to May) from 2008 to 2010. The intercomparison shows encouraging agreement between these two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46–82 km), when the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station as Swerad's sensitivity decreases as a function of distance and Swerad also tends to overshoots low level precipitating systems further away from the station, leading to underestimation of snowfall rate and occasionally missing the precipitation altogether. Further investigations of various statistical metrics, such as the probability of detection, false alarm rate, hit rate, and the Hanssen–Kuipers skill scores, and the sensitivity of these metrics to snowfall rate and the distance from the radar station, were carried out. The results of these investigations highlight the strengths and the limitations of both observing systems at the lower and upper ends of snowfall distributions and the range of uncertainties that could be expected from these systems in the high latitude regions.

Research on X-Band Weather Radar Network Based on Sensor Grid

2012 ◽  
Vol 468-471 ◽  
pp. 1274-1277
Author(s):  
Chen Li
Keyword(s):  
Weather Radar ◽  
Key Technology ◽  
Radar Systems ◽  
Radar Network ◽  
Additional Equipment ◽  
X Band ◽  
Architectural Framework ◽  
Information Advantage ◽  
New Generation ◽  
Network Advantage

Monitoring of precipitation using X-band weather radar systems is becoming popular. X-band weather radar network, as an additional equipment of China new generation weather radar, primarily used to measure weather echo within 3km above the ground and has a high prospect. The network, based on sensor grid, is greater information advantage and network advantage. This paper describes the design, the key technology and implementation of an architectural framework of the weather radar network based on sensor grid. The results show that the network works robustly in real time.

Improving the Accuracy of Rain Rate Estimates Using X-Band Phased-Array Weather Radar Network

2018 ◽  
Vol 56 (12) ◽  
pp. 6986-6994 ◽  
Author(s):  
Hiroshi Kikuchi ◽  
Tomoo Ushio ◽  
Fumihiko Mizutani ◽  
Masakazu Wada
Keyword(s):  
Rain Rate ◽  
Phased Array ◽  
Weather Radar ◽  
Radar Network ◽  
X Band

An automated weather radar network

Weather ◽  
1974 ◽  
Vol 29 (12) ◽  
pp. 463-463 ◽  
Author(s):  
P. S. Kelway
Keyword(s):  
Weather Radar ◽  
Radar Network
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