Algorithm for Estimating the Parameters of Sea Pitching for Increasing the Accuracy of a Ship-Borne Radar Beam Positioning

2017 ◽  
Vol 18 (8) ◽  
pp. 571-576
Author(s):  
A. N. Grachev ◽  
◽  
S. A. Kurbatsky ◽  
Yu. I. Lebedenko ◽  
◽  
...  
Keyword(s):  
Radar Beam

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.

scholarly journals Effects of Atmospheric Refraction on an Airborne Weather Radar Detection and Correction Method

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Lei Wang ◽  
Ming Wei ◽  
Tao Yang ◽  
Ping Liu
Keyword(s):  
Weather Radar ◽  
Weather Conditions ◽  
Refraction Index ◽  
Correction Method ◽  
Propagation Path ◽  
Radar Detection ◽  
Atmospheric Refraction ◽  
Radar Beam ◽  
Fitting Method ◽  
Flight Parameters

This study investigates the effect of atmospheric refraction, affected by temperature, atmospheric pressure, and humidity, on airborne weather radar beam paths. Using three types of typical atmospheric background sounding data, we established a simulation model for an actual transmission path and a fitted correction path of an airborne weather radar beam during airplane take-offs and landings based on initial flight parameters and X-band airborne phased-array weather radar parameters. Errors in an ideal electromagnetic beam propagation path are much greater than those of a fitted path when atmospheric refraction is not considered. The rates of change in the atmospheric refraction index differ with weather conditions and the radar detection angles differ during airplane take-off and landing. Therefore, the airborne radar detection path must be revised in real time according to the specific sounding data and flight parameters. However, an error analysis indicates that a direct linear-fitting method produces significant errors in a negatively refractive atmosphere; a piecewise-fitting method can be adopted to revise the paths according to the actual atmospheric structure. This study provides researchers and practitioners in the aeronautics and astronautics field with updated information regarding the effect of atmospheric refraction on airborne weather radar detection and correction methods.

scholarly journals Radar Quality Index for a Mosaic of Radar Reflectivity over Chao Phraya River Basin, Thailand

2020 ◽  
pp. 92-104
Author(s):  
Nattapon Mahavik ◽  
Sarintip Tantanee
Keyword(s):  
River Basin ◽  
Quality Index ◽  
Beam Propagation ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Temporal Information ◽  
Radar Beam ◽  
Spatio Temporal ◽  
Chao Phraya River ◽  
Application Fields

The weather radar is one of the tools that can provide spatio-temporal information for nowcast which is useful for hydro-meteorological disasters warning and mitigation system. The ground-based weather radar can provide spatial and temporal information to monitor severe storm over the risky area. However, the usage of multiple radars can provide more effective information over large study area where single radar beam may be blocked by surrounding terrain Even though, the investigation of the sever storm physical characteristics needs the information from multiple radars, the mosaicked radar product has not been available for Thai researcher yet. In this study, algorithm of mosaicked radar reflectivity has been developed by using data from ground-based radar of Thai Meteorological Department over the Chao Phraya river basin in the middle of Thailand. The Python script associated with OpenCV and Wradlib libraries were used in our investigations of the mosaicking processes. The radar quality index (RQI) field has been developed by implementing an equation of a quality radar index to identify the reliability of each mosaicked radar reflectivity pixels. First, the percentage of beam blockage is computed to understand the radar beam propagation obstructed by surrounding topography in order to clarify the limitations of the observed beam on producing radar reflectivity maps. Second, the elevation of beam propagation associated with distance field has been computed. Then, these three parameters and the obtained percentage of beam blockage are utilized as the parameters in the equation of RQI. Finally, the detected radar flare, non-precipitating radar area, has been included to the RQI field. Then, the RQI field has been applied to the extracted radar reflectivity to evaluate the quality of mosaicked radar reflectivity to inform end user in any application fields over the Chao Phraya river basin.

I – Improvements to Secondary Radar for Air Traffic Control

1982 ◽  
Vol 35 (2) ◽  
pp. 193-204
Author(s):  
R.C Bowes
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
Identification System ◽  
Radar Beam ◽  
Reflected Signal

Secondary surveillance radar (SSR) is nearly as old as radar itself. It has evolved from a need in the last war for an Identification system between Friend and Foe (IFF)Primary radar has a scanning aerial beam which transmits a burst of energy, and the reflected signal from an aircraft is detected. In the simplest form of IFF the aircraft carries a receiver/transmitter (transponder), which detects the primary radar beam and transmits a pulse (on the same frequency) a few microseconds later. Thus two ‘blips’ appear on the radar display, one at the shorter range due to reflected energy from the aircraft followed by a stronger reply from the transponder. The system requires all aircraft to carry a transponder which upon receipt of an ‘up link’ signal or message transmits a ‘down link’ signal or message. SSR has evolved (and is still evolving) from this early IFF technique into the present-day system which serves both military IFF needs and the requirements for Air Traffic Control. This paper concentrates on the civil use of SSR where it is the prime sensor for the control of aircraft.

Angle-of-arrival of a radar beam in atmospheric turbulence

2003 ◽  
Author(s):  
R.W. McMillan ◽  
R.A. Smith ◽  
M. Shipman ◽  
E.J. Holder ◽  
J.C. Kerce ◽  
...  
Keyword(s):  
Atmospheric Turbulence ◽  
Angle Of Arrival ◽  
Radar Beam

scholarly journals Improving WSR-88D Radar QPE for Orographic Precipitation Using Profiler Observations

2014 ◽  
Vol 15 (3) ◽  
pp. 1135-1151 ◽  
Author(s):  
Youcun Qi ◽  
Jian Zhang ◽  
Brian Kaney ◽  
Carrie Langston ◽  
Kenneth Howard
Keyword(s):  
Sierra Nevada ◽  
West Coast ◽  
Heavy Rain ◽  
Moisture Flux ◽  
Heavy Precipitation ◽  
The United States ◽  
The West ◽  
Radar Beam ◽  
Orographic Enhancement ◽  
Coast Region

Abstract Quantitative precipitation estimation (QPE) in the West Coast region of the United States has been a big challenge for Weather Surveillance Radar-1988 Doppler (WSR-88D) because of severe blockages caused by the complex terrain. The majority of the heavy precipitation in the West Coast region is associated with strong moisture flux from the Pacific that interacts with the coastal mountains. Such orographic enhancement of precipitation occurs at low levels and cannot be observed well by WSR-88D because of severe blockages. Specifically, the radar beam either samples too high above the ground or misses the orographic enhancement at lower levels, or the beam broadens with range and cannot adequately resolve vertical variations of the reflectivity structure. The current study developed an algorithm that uses S-band Precipitation Profiler (S-PROF) radar observations in northern California to improve WSR-88D QPEs in the area. The profiler data are used to calculate two sets of reference vertical profiles of reflectivity (RVPRs), one for the coastal mountains and another for the Sierra Nevada. The RVPRs are then used to correct the WSR-88D QPEs in the corresponding areas. The S-PROF–based VPR correction methodology (S-PROF-VPR) has taken into account orographic processes and radar beam broadenings with range. It is tested using three heavy rain events and is found to provide significant improvements over the operational radar QPE.

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