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 The Study on Detection Method of Water Vapor on Boundary Layer Based on Multiagent System

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Xu Dongdong ◽  
Zhou Jie ◽  
Xia Jingming ◽  
Lu Zhenyu ◽  
Huan Hai
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Multiagent System ◽  
Detection Method ◽  
Weather Conditions ◽  
Signal Propagation ◽  
Refraction Index ◽  
Base Station ◽  
Propagation Path ◽  
Electromagnetic Signals

A method of detecting water vapor on boundary layer based on multiagent system is proposed in this paper. Multiagent system receives electromagnetic signals emitted by the telecommunication base station. Due to the analysis of the actual electromagnetic wave signal propagation path in the atmosphere, atmospheric refraction index and moisture inversion are discussed in this paper. And the feasibility of using electromagnetic detection method is also analyzed. A multiagent system is designed to receive the electromagnetic signals. The composition and function of the multiagent system are clearly described. The atmospheric refractivity is detected by the multiagent system in three weather conditions of sunny, foggy, and rainy days. The results demonstrate the feasibility of water vapor detection method of multiagent system boundary by comparing the result of experiment with traditional method.

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 Technical Note: Novel method for water vapour monitoring using wireless communication networks measurements

2009 ◽  
Vol 9 (7) ◽  
pp. 2413-2418 ◽  
Author(s):  
N. David ◽  
P. Alpert ◽  
H. Messer
Keyword(s):  
Wireless Communication ◽  
Water Vapour ◽  
Communication Networks ◽  
Weather Conditions ◽  
Technical Note ◽  
Base Stations ◽  
Propagation Path ◽  
High Temporal Resolution ◽  
Wireless Communication Networks ◽  
Near Surface

Abstract. We propose a new technique that overcomes the obstacles of the existing methods for monitoring near-surface water vapour, by estimating humidity from data collected through existing wireless communication networks. Weather conditions and atmospheric phenomena affect the electromagnetic channel, causing attenuations to the radio signals. Thus, wireless communication networks are in effect built-in environmental monitoring facilities. The wireless microwave links, used in these networks, are widely deployed by cellular providers for backhaul communication between base stations, a few tens of meters above ground level. As a result, if all available measurements are used, the proposed method can provide moisture observations with high spatial resolution and potentially high temporal resolution. Further, the implementation cost is minimal, since the data used are already collected and saved by the cellular operators. In addition – many of these links are installed in areas where access is difficult such as orographic terrain and complex topography. As such, our method enables measurements in places that have been hard to measure in the past, or have never been measured before. The technique is restricted to weather conditions which exclude rain, fog or clouds along the propagation path. Strong winds that may cause movement of the link transmitter or receiver (or both) may also interfere with the ability to conduct accurate measurements. We present results from real-data measurements taken from two microwave links used in a backhaul cellular network that show convincing correlation to surface station humidity measurements. The measurements were taken daily in two sites, one in northern Israel (28 measurements), the other in central Israel (29 measurements). The correlation between the microwave link measurements and the humidity gauges were 0.9 and 0.82 for the north and central sites, respectively. The Root Mean Square Differences (RMSD) were 1.8 g/m3 and 3.4 g/m3 for the northern and central site measurements, respectively.

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.

Atmospheric Refraction Correction Method Based on Angular Intersection

2020 ◽  
Vol 57 (13) ◽  
pp. 130102
Author(s):  
司文涛 Si Wentao ◽  
王伟超 Wang Weichao ◽  
袁光福 Yuan Guangfu ◽  
程军练 Cheng Junlian ◽  
王卫杰 Wang Weijie
Keyword(s):  
Correction Method ◽  
Atmospheric Refraction

The Radar Detection of Dangerous Storms

1953 ◽  
Vol 6 (3) ◽  
pp. 238-239
Author(s):  
R. E. Perry
Keyword(s):  
Weather Conditions ◽  
Radar Detection ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Effective System ◽  
The Tropics

Aircraft such as the Comet normally fly above cloud formations, but their approach to the airport involves a long and more or less gradual descent which may take them through adverse weather conditions. Violent thunderstorms, for example, which are fairly common in some parts of the tropics, constitute an obvious danger, and the provision at airports of an effective system of cloud warning could provide an added measure of safety along air routes.

scholarly journals Performance Investigation of OFDM-FSO System under Diverse Weather Conditions of Bangladesh

2018 ◽  
Vol 8 (5) ◽  
pp. 3722
Author(s):  
Maliha Sultana ◽  
Agnila Barua ◽  
Jobaida Akhtar ◽  
Mohammad Istiaque Reja
Keyword(s):  
Atmospheric Turbulence ◽  
Communication Systems ◽  
Weather Conditions ◽  
Index Structure ◽  
Propagation Path ◽  
Free Space Optical ◽  
Atmospheric Attenuation ◽  
Suitable Alternative ◽  
Pointing Error ◽  
Free Spectrum

Free space optical (FSO) communication systems which are deployed for last mile access, being considered as a suitable alternative technology for optical fiber networks. It is one of the emerging technologies for broadband wireless connectivity which has also been receiving growing attention due to high data rate transmission capability with low installation cost and license free spectrum. However, the widespread use of FSO technology has been hampered by the randomly time varying characteristics of propagation path mainly due to atmospheric turbulence, sensitivity to diverse weather conditions and the nonlinear responsivity of laser diode. This paper presents the performance investigation of an OFDM-FSO system over atmospheric turbulence channels under diverse weather conditions of Bangladesh. The channel is modeled with gamma-gamma distribution using 16-QAM modulation format and 4×4 multiple transceiver FSO system. All possible challenges are imposed on the system performance such as atmospheric attenuation, turbulence, pointing error, geometric loss etc. The refractive index structure parameter and atmospheric attenuation coefficient for different weather conditions are calculated by using the data, collected from Bangladesh Meteorological Department. The acquired results can be fruitful for scheming, forecasting and assessing the OFDM-FSO system’s ability to transmit wireless services over turbulent FSO links under actual conditions of Bangladesh.

scholarly journals AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1653
Author(s):  
Gabriela Czibula ◽  
Andrei Mihai ◽  
Alexandra-Ioana Albu ◽  
Istvan-Gergely Czibula ◽  
Sorin Burcea ◽  
...  
Keyword(s):  
Binary Classification ◽  
Weather Radar ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Radar Data ◽  
Probability Of Detection ◽  
Radar Reflectivity ◽  
Classification Model ◽  
Precipitation Forecast ◽  
Short Term

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

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