associations between aircraft measurements of turbulence and weather radar measurements1

1965 ◽  
Vol 46 (8) ◽  
pp. 443-447 ◽  
Author(s):  
Edwin Kessler ◽  
Jean T. Lee ◽  
Kenneth E. Wilk
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Time Dependent ◽  
Aircraft Measurements ◽  
Radar Echoes ◽  
Radar Echo

Aircraft have been guided with the aid of radar data to measure turbulence in thunderstorm areas. Although turbulence is frequently encountered in areas containing highly reflective and sharp-edged echoes, no unique correspondence has been discovered between single-echo parameters and collocated within-storm turbulence. A theory embracing some of the time-dependent relationships between fields of wind and precipitation suggests that the correspondence between instantaneous distributions of radar echoes and turbulence is statistical rather than precise. Statistical bases for study of radar echo-turbulence relationships are outlined.

scholarly journals Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

2022 ◽  
Author(s):  
Haoxuan Yuan ◽  
Rahat Ihsan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Super Resolution ◽  
Weather Radar ◽  
Reconstruction Algorithm ◽  
Radar Data ◽  
Structure Information ◽  
Radar Echo ◽  
Domain Selection

Abstract Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

scholarly journals Fuzzy Logic Algorithms to Identify Birds, Precipitation, and Ground Clutter in S-Band Radar Data Using Polarimetric and Nonpolarimetric Variables

2019 ◽  
Vol 36 (12) ◽  
pp. 2401-2414 ◽  
Author(s):  
Basivi Radhakrishna ◽  
Frédéric Fabry ◽  
Alamelu Kilambi
Keyword(s):  
Fuzzy Logic ◽  
Target Identification ◽  
Radar Data ◽  
Radar Observations ◽  
Fuzzy Logic Algorithm ◽  
Ground Clutter ◽  
Independent Events ◽  
Radar Echoes ◽  
Radar Echo ◽  
Migrating Birds

AbstractThe statistical properties of the radar echoes from biological, precipitation, and ground targets observed with the McGill S-band dual-polarization radar have been used to devise a polarimetric and a nonpolarimetric fuzzy logic algorithm for pixel-by-pixel target identification. Radar observations of migrating birds show distinctly different polarimetric features during their relative approach and departure from the radar site illustrating the dependency of radar parameters on the canting angle and scattering cross section. The devised algorithms have been tested with two independent events, each consisting of 2 h of radar observations with a 5-min temporal resolution. One event consisted of precipitation without birds while the other contained only birds. The misclassifications were 10.12% and 9.6%, respectively, for the two cases for the nonpolarimetric algorithm, and 1.99% and 0.92% for the polarimetric algorithm. The results indicate that even though nonpolarimetric radar membership functions may be considered adequate for separating radar echo returns from birds, precipitation, and ground targets, they are not sufficiently skilled if a greater accuracy is required. Target identification without polarimetric variables especially fails in the region of zero isodop and in precipitation with an echo top below 4 km.

scholarly journals A new method based on stacked auto-encoders to identify abnormal weather radar echo images

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ling Yang ◽  
Yun Wang ◽  
Zhongke Wang ◽  
Yang Qi ◽  
Yong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recognition Rate ◽  
Weather Radar ◽  
Practical Significance ◽  
Automatic Identification ◽  
Hardware Failure ◽  
Radar Echoes ◽  
Radar Echo ◽  
Novel Method ◽  
Classical Image

Abstract It is not denied that real-time monitoring of radar products is an important part in actual meteorological operations. But the weather radar often brings out abnormal radar echoes due to various factors, such as climate and hardware failure. So it is of great practical significance and research value to realize automatic identification of radar anomaly products. However, the traditional algorithms to identify anomalies of weather radar echo images are not the most accurate and efficient. In order to improve the efficiency of the anomaly identification, a novel method combining the theory of classical image processing and deep learning was proposed. The proposed method mainly includes three parts: coordinate transformation, integral projection, and classification using deep learning. Furthermore, extensive experiments have been done to validate the performance of the new algorithm. The results show that the recognition rate of the proposed method can reach up to more than 95%, which can successfully achieve the goal of screening abnormal radar echo images; also, the computation speed of it is fairly satisfactory.

Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases

2020 ◽  
Vol 148 (3) ◽  
pp. 1099-1120 ◽  
Author(s):  
Kao-Shen Chung ◽  
I-An Yao
Keyword(s):  
Numerical Model ◽  
Radar Data ◽  
Motion Field ◽  
Disaster Prevention ◽  
Model Forecast ◽  
Weather Information ◽  
Extrapolation Algorithm ◽  
Radar Echoes ◽  
Radar Echo ◽  
Central Weather Bureau

Abstract Severe weather nowcasting is a crucial mission of atmospheric science for the betterment of society to save life, limb, and property. In this study, composite radar data from the Central Weather Bureau of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan, an extrapolation algorithm that predicts future precipitation based on current radar echoes. In addition, instead of mixing the precipitation between radar extrapolation and numerical model forecast as in previous studies, a blending system is formed by synthesizing the wind information from model forecast with the echo extrapolation motion field via a variational algorithm to improve the nowcasting system. The statistical results of the radar echo extrapolation for 16 typhoon cases show that while the quantitative precipitation nowcasting skill can persist for up to 2 h, significant distortion for the rotational system is found after 2 h. On the other hand, the blending system helps to capture and maintain the rotation of typhoon rainband structures. The blending system extends the nowcasting skill by 1 h to a total of 3 h. Furthermore, the blending scheme performs especially well after the typhoon makes landfall in Taiwan. For disaster prevention and mitigation, this blending nowcasting technique may provide effective weather information immediately.

Intelligent Parameters Identification of Radar Echoes in Yunnan Province

2014 ◽  
Vol 886 ◽  
pp. 568-571
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Peng Li ◽  
Zuo Jiang ◽  
Zhong Wen Xie ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Reference Value ◽  
Radar Data ◽  
Severe Weather ◽  
Movement Direction ◽  
Warning Systems ◽  
Weather Modification ◽  
Movement Velocity ◽  
Radar Echoes ◽  
Radar Echo

In view of the important reference value of weather radar data in the area of weather-modification-decision-making and inspired by the theories of graphics and image processing, we propose an algorithm of calculating the movement direction and movement velocity of radar echo based on radar data analysis. The implementation of the algorithm can effectively improve the early warning ability towards severe weather and has great significance in the construction of command and warning systems for weather modification.

scholarly journals Asymmetric Radar Echo Patterns from Insects

2015 ◽  
Vol 32 (4) ◽  
pp. 659-674 ◽  
Author(s):  
Valery M. Melnikov ◽  
Michael J. Istok ◽  
John K. Westbrook
Keyword(s):  
Correlation Coefficient ◽  
Critical Role ◽  
Radar Data ◽  
Model Output ◽  
Differential Phase ◽  
Radar Echoes ◽  
Polarized Waves ◽  
Radar Echo ◽  
Differential Reflectivity

AbstractRadar echoes from insects, birds, and bats in the atmosphere exhibit both symmetry and asymmetry in polarimetric patterns. Symmetry refers to similar magnitudes of polarimetric variables at opposite azimuths, and asymmetry relegates to differences in these magnitudes. Asymmetry can be due to different species observed at different azimuths. It is shown in this study that when both polarized waves are transmitted simultaneously, asymmetric patterns can also be caused by insects of the same species that are oriented in the same direction. A model for scattering of simultaneously transmitted horizontally and vertically polarized radar waves by insects is developed. The model reproduces the main features of asymmetric patterns in differential reflectivity: the copolar correlation coefficient and the differential phase. The radar differential phase on transmit between horizontally and vertically polarized waves plays a critical role in the formations of the asymmetric patterns. The width-to-length ratios of insects’ bodies and their orientation angles are retrieved from matching the model output with radar data.

scholarly journals MLC-LSTM: Exploiting the Spatiotemporal Correlation between Multi-Level Weather Radar Echoes for Echo Sequence Extrapolation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3988 ◽  
Author(s):  
Jinrui Jing ◽  
Qian Li ◽  
Xuan Peng
Keyword(s):  
Deep Learning ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Extrapolation Methods ◽  
Short Term ◽  
Spatiotemporal Correlation ◽  
Radar Echoes ◽  
Radar Echo ◽  
Adversarial Training ◽  
Multi Level

Weather radar echo is the data detected by the weather radar sensor and reflects the intensity of meteorological targets. Using the technique of radar echo extrapolation, which is the prediction of future echoes based on historical echo observations, the approaching short-term weather conditions can be forecasted, and warnings can be raised with regard to disastrous weather. Recently, deep learning based extrapolation methods have been proposed and show significant application potential. However, there are two limitations of existing extrapolation methods which should be considered. First, few methods have investigated the impact of the evolutionary process of weather systems on extrapolation accuracy. Second, current deep learning methods usually encounter the problem of blurry echo prediction as extrapolation goes deeper. In this paper, we aim to address the two problems by proposing a Multi-Level Correlation Long Short-Term Memory (MLC-LSTM) and integrate the adversarial training into our approach. The MLC-LSTM can exploit the spatiotemporal correlation between multi-level radar echoes and model their evolution, while the adversarial training can help the model extrapolate realistic and sharp echoes. To train and test our model, we build a real-life multi-level weather radar echoes dataset based on raw CINRAD/SA radar observations provided by the National Meteorological Information Center, China. Extrapolation experiments show that our model can accurately forecast the motion and evolution of an echo while keeping the predicted echo looking realistic and fine-grained. For quantitative evaluation on probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS) metrics, our model can reach average scores of 0.6538 POD, 0.2818 FAR, 0.5348 CSI, and 0.6298 HSS, respectively when extrapolating 15 echoes into the future, which outperforms the current state-of-the-art extrapolation methods. Both the qualitative and quantitative experimental results demonstrate the effectiveness of our model, suggesting that it can be effectively applied to operational weather forecasting practice.

scholarly journals Automated Algorithm for Accurate Doppler Profile Detection of MST Radar Signals Considering Removal of Interference

2019 ◽  
Vol 8 (2) ◽  
pp. 1134-1138
Keyword(s):  
Radar Data ◽  
Hilbert Huang Transform ◽  
Automated Algorithm ◽  
Radar Echoes ◽  
Mst Radar ◽  
Radar Echo ◽  
Interference Signals ◽  
Radar Signals ◽  
Doppler Power ◽  
Low Amplitude

Identifying the accurate radar echo returns from the MST (Mesosphere-Stratosphere–Troposphere) region poses serious challenges, as the radar echo signals are of very low amplitude and hidden in noise. The process of extraction of the relevant signal information becomes intricate when the spectra sometimes are infected with interference signals of non-atmospheric origin. An automated algorithm is suggested for eliminating the interference bands in the Doppler spectra of MST radar signals. The process of removal of interference and de-noising of the radar echoes using soft thresholding techniques was achieved simultaneously. The algorithm was practically applied to interference contaminated MST radar data taken from National Atmospheric Research Laboratory (NARL), Gadanki, Tirupati and studied for removal of interference bands. It was found that the projected algorithm effectively removes multiple interference bands and also improves signal detectability. The power and Doppler values (moments) are estimated using FFT and HHT (Hilbert Huang Transform) and median Doppler is plotted on Doppler power spectrum and compared.

scholarly journals Comparison of linear and logarithmic receiver signals from polarimetric weather radar echoes and their temporal decorrelation properties

2005 ◽  
Vol 3 ◽  
pp. 401-411
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Dynamic Range ◽  
Weather Radar ◽  
Radar Data ◽  
Wide Field ◽  
Least Mean Square ◽  
Depolarisation Ratio ◽  
Radar Echoes ◽  
Linear Receiver ◽  
Temporal Decorrelation ◽  
Polarimetric Weather Radar

Abstract. Usually common polarimetric weather radar DSP-products (e.g.: reflectivity, differential reflectivity, linear depolarisation ratio - for both - co-polar and cross-polar signal components) are based on the logarithmic receiver output, because of the large dynamic range provided by the logarithmic receiver. In this paper for the first time we also use the linear receiver output to calculate common weather radar DSP-Products. Using the raw time series radar data recorded with the coherent polarimetric C-band weather radar of the DLR (''Poldirad'', Wessling, Germany) it is possible to do a comparison between processed weather radar echoes from the linear receiver and the logarithmic receiver. After the comparison showed very good results, we continued the work with the linear receiver data, especially on the topic named temporal decorrelation properties of the linear receiver data. This paper includes the first results obtained from two observables that belong to our working topic. The first observable is the ''Time Decorrelation Factor-TDF'' and the second one is the ''Decorrelation Time DTτ''The results have been summarised in the form of empirical relationships, plots and the least mean square (LMS) method of curve fitting was used to give the mathematical relationship for the observables TDF and DTτ. Generally, the paper will also reflect on the statistical properties of radar echoes measured with linear receivers. The usage of the linear receiver data opens a wide field of new applications and products for the work with polarimetric weather radar data, because the linear receiver data also provides phase information which a logarithmic receiver does not.

scholarly journals Adaptive Sparse Domain Selection for Weather Radar Superresolution

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Haoxuan Yuan ◽  
Qiangyu Zeng ◽  
Jianxin He
Keyword(s):  
Visual Quality ◽  
Weather Radar ◽  
Reconstruction Algorithm ◽  
Radar Data ◽  
Detailed Structure ◽  
Structure Information ◽  
Radar Echo ◽  
Domain Selection ◽  
Selection For ◽  
Information Focus

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

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