Towards a Data Processing Architecture for the Weather Radar of the INTA Anguil

2015 ◽  
Author(s):  
Mario Jose Divan ◽  
Yanina Bellini Saibene ◽  
Maria De Los Angeles Martin ◽  
Maria Laura Belmonte ◽  
Guillermo Lafuente ◽  
...  
Keyword(s):  
Data Processing ◽  
Weather Radar ◽  
Processing Architecture

scholarly journals Weather radar data processing on graphic cards

2017 ◽  
Vol 74 (2) ◽  
pp. 868-885 ◽  
Author(s):  
Mónica Denham ◽  
Enrico Lamperti ◽  
Javier Areta
Keyword(s):  
Data Processing ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Data Processing

Distributed real time data processing architecture for the TJ-II data acquisition system

2004 ◽  
Vol 75 (10) ◽  
pp. 4261-4264 ◽  
Author(s):  
M. Ruiz ◽  
E. Barrera ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Processing Architecture

scholarly journals Processing algorithms for finding the boundaries of areas of dangerous wind shear for on-board weather radar

2021 ◽  
Vol 2094 (3) ◽  
pp. 032050
Author(s):  
Yu A Novikova ◽  
M B Ryzhikov
Keyword(s):  
Data Processing ◽  
Wind Shear ◽  
Weather Radar ◽  
Radar Data ◽  
International Standards ◽  
Joint Processing ◽  
Input Parameters ◽  
Processing Algorithms

Abstract This report considers the results of the development of algorithms for processing radar data when working in the mode of detecting dangerous areas of wind shear, which use binary values of hazard signs in each direction of sensing as input parameters, are presented. The first of them implements data processing for each individual direction, and the second-joint processing in all directions. As a result of their work, it is possible to identify dangerous areas of wind shear that meet the spatial requirements of the international standards ARING-708A and DO-220.

The automated data processing architecture for the GPI Exoplanet Survey

2017 ◽  
Author(s):  
Jason J. Wang ◽  
Marshall D. Perrin ◽  
Dmitry Savransky ◽  
Pauline Arriaga ◽  
Jeffrey K. Chilcote ◽  
...  
Keyword(s):  
Data Processing ◽  
Automated Data Processing ◽  
Processing Architecture

scholarly journals Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research

2019 ◽  
Vol 11 (19) ◽  
pp. 2293
Author(s):  
Hao An ◽  
Wei Yan ◽  
Shuangshuang Bian ◽  
Shuo Ma
Keyword(s):  
Phase Shift ◽  
Data Processing ◽  
Detection System ◽  
Experimental System ◽  
Weather Radar ◽  
Heavy Rain ◽  
Theoretical Research ◽  
Processing Algorithm ◽  
Data Processing Algorithm ◽  
Gps Satellites

In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, the paper aims to detect rain using polarimetric GNSS signals from a ground-based experiment. Firstly, a conical horn antenna specially designed for receiving dual-polarized (H, horizontal, and V, vertical) GNSS signals was developed, and an experimental system for polarimetric GNSS rain detection was built. Then, taking Global Positioning System (GPS) satellites as signal source, a ground-based experiment was carried out at a mountain in Nanjing, where heavy rain tends to occur frequently in rainy season. Additionally, a data processing algorithm mainly following Padullés et al. (2016) to solve the problems of quality control, unlocking, hardware effect, phase ambiguity, multipath effect was applied independently to this ground-based data from the polarimetric GNSS rain detection system. Also, the multi-source data from nearby weather radar and weather stations was used for verification. Results from 14 GPS satellites show that the obtained phase shift is zero in all no-rain days while it is not zero during rainy days, which is in accordance with the actual situation. Compared with weather radar and rain gauges’ data, the results verify that the phase shift is caused by rain. Besides, when individual cases are examined, many show that their tendencies of accumulated phase shift are quite similar to that of a weather station’s rainfall data, even some correlation coefficients are up to 0.99. These demonstrate the reliability of our experimental system and the feasibility of the data processing algorithm. This study will provide technical support for future spaceborne experiment, which has promising applications in global rain monitoring.

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