The Sensitivity of Single Polarization Weather Radar Beam Blockage Correction to Variability in the Vertical Refractivity Gradient

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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Technical a Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data

Remote Sensing ◽

10.3390/rs13101989 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1989

Author(s):

Raphaël Nussbaumer ◽

Baptiste Schmid ◽

Silke Bauer ◽

Felix Liechti

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Doppler Radar ◽

Animal Movement ◽

Weather Radar ◽

Gaussian Mixture ◽

Radar Data ◽

Volume Data ◽

Simple Method ◽

Single Polarization

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-14-00206.1 ◽

2015 ◽

Vol 32 (7) ◽

pp. 1341-1355 ◽

Cited By ~ 8

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.

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

Advances in Meteorology ◽

10.1155/2015/407867 ◽

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.

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

Applied Environmental Research ◽

10.35762/aer.2020.42.3.8 ◽

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.

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The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

Geomatics ◽

10.3390/geomatics1030020 ◽

2021 ◽

Vol 1 (3) ◽

pp. 347-368

Author(s):

Tomeu Rigo ◽

Maria Carmen Llasat ◽

Laura Esbrí

Keyword(s):

Quality Control ◽

Weather Radar ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

Daily Data ◽

Precipitation Estimation ◽

Weather Stations ◽

Point Data ◽

Single Polarization

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

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Real-Time Detection and Filtering of Chaff Clutter from Single-Polarization Doppler Radar Data

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-12-00158.1 ◽

2013 ◽

Vol 30 (5) ◽

pp. 873-895 ◽

Cited By ~ 19

Author(s):

Yong Hyun Kim ◽

Sungshin Kim ◽

Hye-Young Han ◽

Bok-Haeng Heo ◽

Cheol-Hwan You

Keyword(s):

Real Time ◽

Doppler Radar ◽

Meteorological Data ◽

Three Dimensional ◽

Weather Radar ◽

Cost Effective ◽

Radar Data ◽

Control Procedure ◽

South Korean ◽

Single Polarization

Abstract In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.

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