Design of Lifting Electromechanical System for Onboard X-Band Weather Radar Antenna and Data Filtering Analysis

2013 ◽  
Vol 579-580 ◽  
pp. 740-744
Author(s):  
Xu Hui Wei ◽  
Bin Hua Yang ◽  
Wei Dong Lu ◽  
Ling Wen Kong
Keyword(s):  
Modular Design ◽  
Weather Radar ◽  
Radar Data ◽  
Object Oriented Programming ◽  
Electromechanical System ◽  
System Stability ◽  
Data Management System ◽  
Data Filtering ◽  
X Band ◽  
Radar Antenna

Onboard X-Band Weather Radar and data filter prediction is one of core services of the Xinjiang meteorological emergency system. Based on installation conditions provided by IVECO trunk, the structure of X-band radar antenna, lifting height and antenna work requirements, combined with the modular design concept, this paper developed the X-band weather radar antenna dedicated lifting system. This system consists of radar antenna base platform, lifting rack rails, rollers, sprockets, cylinder etc. when working, the system can not only utilize the synchronizing control strategy to ensure the system stability but also quickly set up an antenna. Based on the design of Onboard X-band Weather radar antenna lifting electromechanical system, we developed the radar data management system. In this software, Object-oriented programming language, multi-threaded programming methods and software modularity method is utilized to design the platform architecture, GIS controls and dynamic mesh technology are used to make the radar map, and based on the principle of Kalman filtering, intelligent prediction approaches are studied. Computer numerical simulation and experimental results show that the electromechanical system developed by this paper has good performance and utilized the data filtering technology to provide the reliable method for meteorological warning.

scholarly journals Rainfall Information System Based on Weather Radar for Debris Flow Disaster Mitigation

2018 ◽  
Vol 7 (4.44) ◽  
pp. 165 ◽  
Author(s):  
Ratih Indri Hapsari ◽  
Gerard Aponno ◽  
Rosa Andrie Asmara ◽  
Satoru Oishi
Keyword(s):  
Information System ◽  
Debris Flow ◽  
Weather Radar ◽  
Active Volcano ◽  
Radar Data ◽  
Disaster Mitigation ◽  
Web Based ◽  
X Band ◽  
Secondary Hazards ◽  
Debris Flow Disaster

Rainfall-triggered debris flow has caused multiple impacts to the environment. It. is regarded as the most severe secondary hazards of volcanic eruption. However, limited access to the active volcano slope restricts the ground rain measurement as well as the direct delivery of risk information. In this study, an integrated information system is proposed for volcanic-related disaster mitigation under the framework of X-Plore/X-band Polarimetric Radar for Prevention of Water Disaster. In the first part, the acquisition and processing of high-resolution X-band dual polarimetric weather/X-MP radar data in real-time scheme for demonstrating the disaster-prone region are described. The second part presents the design of rainfall resource database and extensive maps coverage of predicted hazard information in GIS web-based platform accessible both using internet and offline. The proposed platform would be useful for communicating the disaster risk prediction based on weather radar in operational setting.  

scholarly journals A sun-tracking method to improve the pointing accuracy of weather radar

2011 ◽  
Vol 4 (4) ◽  
pp. 5569-5595
Author(s):  
X. Muth ◽  
M. Schneebeli ◽  
A. Berne
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Swiss Alps ◽  
The Sun ◽  
Sources Of Information ◽  
Weather Radars ◽  
Pointing Accuracy ◽  
Instrumental Bias ◽  
Radar Antenna ◽  
Land Cover Maps

Abstract. Accurate positioning of data collected by a weather radar is of primary importance for their appropriate georeferencing, which in turn makes it possible to combine those with additional sources of information (topography, land cover maps, meteorological simulations from numerical weather models to list a few). This issue is especially acute for mobile radar systems, for which accurate and stable levelling might be difficult to ensure. The sun is a source of microwave radiation, which can be detected by weather radars and used for the accurate positioning of the radar data. This paper presents a technique based on the sun echoes to quantify and hence correct for the instrumental errors which can affect the pointing accuracy of radar antenna. The proposed method is applied to data collected in the Swiss Alps using a mobile X-band radar system. The obtained instrumental bias values are evaluated by comparing the locations of the ground echoes predicted using these bias estimates with the observed ground echo locations. The very good agreement between the two confirms the good accuracy of the proposed method.

scholarly journals X-Band Polarimetric Weather Radar Observations of a Hailstorm

2013 ◽  
Vol 30 (9) ◽  
pp. 2143-2151 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Françoise Honoré ◽  
Pierre Tabary
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Complementary Information ◽  
Radar Observations ◽  
National Network ◽  
X Band ◽  
Polarimetric Weather Radar

Abstract This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection.

scholarly journals Clear-Air Turbulence (CAT) Identification with X-Band Dual Polarimetric Radar Based on Bayesian Approach

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1691
Author(s):  
Jianli Ma ◽  
Li Luo ◽  
Mingxuan Chen ◽  
Siteng Li
Keyword(s):  
Frequency Distribution ◽  
Removal Rate ◽  
Low Frequency ◽  
Weather Radar ◽  
Radar Data ◽  
Probability Density Distribution ◽  
Air Turbulence ◽  
X Band ◽  
Differential Reflectivity

The echo of weather radar is seriously disturbed by clear-air turbulence echo (CAT) which needs identifying and eliminating to improve the data quality of weather radar. Using the data observed with the five X-band dual polarimetric radars in Changping, Fangshan, Miyun, Shunyi, and Tongzhou, Beijing in 2018, the probability density distribution (PDD) of the horizontal texture of four radar moments reflectively factor (ZH), differential reflectivity (ZDR), correlation coefficient (ρHV), differential propagation phase shift (ΦDP), and then the CAT is identified and removed using Bayesian method. The results show that the radar data can be effectively improved after the CAT has been eliminated, which include: (1) the removal rate of CAT is more than 98.2% in the analyzed cases. (2) In the area with high-frequency distribution of CAT, the CAT can be effectively suppressed; in the area with low-frequency distribution, some weather echo in the edge with SNR < 15 dB may be mistakenly identified as CAT, but the proportion of meteorological echoes to the total echoes is more than 85%, which indicate that the error rate is very low and does not affect the radar operation.

Operational Monitoring of Weather Radar Receiving Chain Using the Sun

2010 ◽  
Vol 27 (1) ◽  
pp. 159-166 ◽  
Author(s):  
Iwan Holleman ◽  
Asko Huuskonen ◽  
Mikko Kurri ◽  
Hans Beekhuis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
The Sun ◽  
Volume Data ◽  
Operational Monitoring ◽  
Flux Data ◽  
Weather Radars ◽  
Receiver System ◽  
The Stability ◽  
Radar Antenna

Abstract A method for operational monitoring of a weather radar receiving chain, including the antenna gain and the receiver, is presented. The “online” method is entirely based on the analysis of sun signals in the polar volume data produced during operational scanning of weather radars. The method is an extension of that for determining the weather radar antenna pointing at low elevations using sun signals, and it is suited for routine application. The solar flux from the online method agrees very well with that obtained from “offline” sun tracking experiments at two weather radar sites. Furthermore, the retrieved sun flux is compared with data from the Dominion Radio Astrophysical Observatory (DRAO) in Canada. Small biases in the sun flux data from the Dutch and Finnish radars (between −0.93 and +0.47 dB) are found. The low standard deviations of these sun flux data against those from DRAO (0.14–0.20 dB) demonstrate the stability of the weather radar receiving chains and of the sun-based online monitoring. Results from a daily analysis of the sun signals in online radar data can be used for monitoring the alignment of the radar antenna and the stability of the radar receiver system. By comparison with the observations from a sun flux monitoring station, even the calibration of the receiving chain can be checked. The method presented in this paper has great potential for routine monitoring of weather radars in national and international networks.

Marine X-band weather radar data calibration

2012 ◽  
Vol 103 ◽  
pp. 33-44 ◽  
Author(s):  
S. Thorndahl ◽  
M.R. Rasmussen
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
X Band ◽  
Data Calibration

GLUE based marine X-band weather radar data calibration and uncertainty estimation

2014 ◽  
Vol 12 (4) ◽  
pp. 283-294 ◽  
Author(s):  
Jesper Ellerbæk Nielsen ◽  
Keith Beven ◽  
Søren Thorndahl ◽  
Michael R. Rasmussen
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Uncertainty Estimation ◽  
X Band ◽  
Data Calibration

scholarly journals Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

2020 ◽  
Vol 12 (21) ◽  
pp. 3629
Author(s):  
Luigi Mereu ◽  
Simona Scollo ◽  
Costanza Bonadonna ◽  
Valentin Freret-Lorgeril ◽  
Frank Silvio Marzano
Keyword(s):  
Warning System ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Lava Fountain ◽  
X Band ◽  
L Band ◽  
The Difference ◽  
Polarimetric Weather Radar

Explosive basaltic eruptions eject a great amount of pyroclastic material into the atmosphere, forming columns rising to several kilometers above the eruptive vent and causing significant disruption to both proximal and distal communities. Here, we analyze data, collected by an X-band polarimetric weather radar and an L-band Doppler fixed-pointing radar, as well as by a thermal infrared (TIR) camera, in relation to lava fountain-fed tephra plumes at the Etna volcano in Italy. We clearly identify a jet, mainly composed of lapilli and bombs mixed with hot gas in the first portion of these volcanic plumes and here called the incandescent jet region (IJR). At Etna and due to the TIR camera configuration, the IJR typically corresponds to the region that saturates thermal images. We find that the IJR is correlated to a unique signature in polarimetric radar data as it represents a zone with a relatively high reflectivity and a low copolar correlation coefficient. Analyzing five recent Etna eruptions occurring in 2013 and 2015, we propose a jet region radar retrieval algorithm (JR3A), based on a decision-tree combining polarimetric X-band observables with L-band radar constraints, aiming at the IJR height detection during the explosive eruptions. The height of the IJR does not exactly correspond to the height of the lava fountain due to a different altitude, potentially reached by lapilli and blocks detected by the X-band weather radar. Nonetheless, it can be used as a proxy of the lava fountain height in order to obtain a first approximation of the exit velocity of the mixture and, therefore, of the mass eruption rate. The comparisons between the JR3A estimates of IJR heights with the corresponding values recovered from TIR imagery, show a fairly good agreement with differences of less than 20% in clear air conditions, whereas the difference between JR3A estimates of IJR height values and those derived from L-band radar data only are greater than 40%. The advantage of using an X-band polarimetric weather radar in an early warning system is that it provides information in all weather conditions. As a matter of fact, we show that JR3A retrievals can also be obtained in cloudy conditions when the TIR camera data cannot be processed.

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