Weather Radar

2014 ◽  
pp. 591-634
Author(s):  
Trostel
Keyword(s):  
Weather Radar

The Evaluation on the Observation Accuracy of Phased Array Weather Radar

2014 ◽  
Vol 134 (4) ◽  
pp. 204-210 ◽  
Author(s):  
Yuki Hirano ◽  
Kouichi Maruo ◽  
Shigeharu Shimamura ◽  
Satoru Yoshida ◽  
Tomoo Ushio ◽  
...  
Keyword(s):  
Phased Array ◽  
Weather Radar

Doppler-Polarimetric Weather Radar: Returns from Wide Spread Precipitation

2007 ◽  
Vol 66 (8) ◽  
pp. 715-727 ◽  
Author(s):  
F. J. Yanovsky ◽  
C. M. H. Unal ◽  
H. W. J. Russchenberg ◽  
L. P. Ligthart
Keyword(s):  
Weather Radar ◽  
Wide Spread ◽  
Polarimetric Weather Radar

CLASSIFICATION OF THE ATMOSPHERIC FORMATIONS BY USING BICOHERENCE-BASED FEATURES EXTRACTED FROM WEATHER RADAR BACKSCATTERING SIGNALS

2016 ◽  
Vol 75 (5) ◽  
pp. 463-475
Author(s):  
V. V. Naumenko ◽  
A. V. Totsky ◽  
G. I. Khlopov ◽  
O. A. Voitovych ◽  
J. T. Astola
Keyword(s):  
Weather Radar ◽  
Radar Backscattering

Modern Weather Surveillance Radars: Hygienic Aspects of Monitoring Electromagnetic Fields at Operators’ Workplaces and in the Environment

2020 ◽  
pp. 34-40
Author(s):  
VN Nikitina ◽  
GG Lyashko ◽  
NI Kalinina ◽  
EN Dubrovskaya ◽  
VP Plekhanov
Keyword(s):  
Electromagnetic Fields ◽  
Weather Radar ◽  
Measuring Instruments ◽  
Residential Areas ◽  
Emf Measurements ◽  
Operating Modes ◽  
Weather Radars ◽  
Radio Transmitters ◽  
Permissible Levels ◽  
Radar Antennas

Summary. Introduction: Location of weather surveillance radars near settlements, in residential areas and on airport premises makes it important to ensure safe levels of electromagnetic fields (EMF) when operating these radio transmitters. EMF maximum permissible levels for weather radars developed in the 1980s are outdated. Our objective was to analyze modern weather surveillance radars to develop proposals for improvement of radar-generated radiofrequency field monitoring. Materials and methods: We studied trends in meteorological radiolocation and technical characteristics of modern weather radars for atmospheric sensing and weather alerts, analyzed regulations for EMF measurements and hygienic assessment, and measured radiofrequency fields produced by weather radar antennas in open areas and at workplaces of operators. Results: We established that modern types of weather radars used in upper-air sensing systems and storm warning networks differ significantly in terms of technical characteristics and operating modes from previous generations. Developed in the 1980s, current hygienic standards for human exposures to radiofrequency fields from weather radar antennas are obsolete. Conclusions: It is essential to develop an up-to-date regulatory and method document specifying estimation and instrumental monitoring of EMF levels generated by weather radars and measuring instruments for monitoring of pulse-modulated electromagnetic radiation.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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