Modelling weather radar beam propagation and topographical blockage at northern high latitudes

10.1002/qj.98 ◽  
2007 ◽  
Vol 133 (626) ◽  
pp. 1191-1204 ◽  
Author(s):  
J. Bech ◽  
U. Gjertsen ◽  
G. Haase
Keyword(s):  
Beam Propagation ◽  
Weather Radar ◽  
High Latitudes ◽  
Radar Beam

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.

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 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 Radar Beam Tracing Methods Based on Atmospheric Refractive Index

2014 ◽  
Vol 31 (12) ◽  
pp. 2650-2670 ◽  
Author(s):  
Yuefei Zeng ◽  
Ulrich Blahak ◽  
Malte Neuper ◽  
Dorit Jerger
Keyword(s):  
Refractive Index ◽  
Ad Hoc ◽  
Beam Propagation ◽  
New Method ◽  
Actual State ◽  
Practical Implementation ◽  
Atmospheric Conditions ◽  
Radar Beam ◽  
Snell’S Law ◽  
Snell's Law

Abstract Simulation of radar beam propagation is an important component of numerous radar applications in meteorology, including height assignment, quality control, and especially the so-called radar forward operator. Although beam propagation in the atmosphere depends on the refractive index and its vertical variation, which themselves depend on the actual state of the atmosphere, the most common method is to apply the 4/3 earth radius model, based on climatological standard conditions. Serious deviations from the climatological value can occur under so-called ducting conditions, where radar beams at low elevations can be trapped or propagate in a waveguide-like fashion, such that this model is unsuitable in this case. To account for the actual atmospheric conditions, sophisticated methods have been developed in literature. However, concerning the practical implementation of these methods, it was determined that the description in the literature is not always complete with respect to possible pitfalls for practical implementations. In this paper, a revised version of an existing method (one example for the above-mentioned “pitfall” statement) is introduced that exploits Snell’s law for spherically stratified media. From Snell’s law, the correct sign of the local elevation is a priori ambiguous, and the revised method explicitly applies (i) a total reflection criterion and (ii) another ad hoc criterion to solve the problem. Additionally, a new method, based on an ordinary differential equation with respect to range, is proposed in this paper that has no ambiguity. Sensitivity experiments are conducted to investigate the properties of these three methods. The results show that both the revised and new methods are robust under nonstandard conditions. But considering the need to catch an elevation sign ambiguity in the revised method (which cannot be excluded to fail in rare instances), the new method is regarded as more robust and unproblematic, for example, for applications in radar forward operators.

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