Data Mining Techniques for Pattern Recognition: Tornado Signatures in Doppler Weather Radar Data

2003 ◽  
Vol 5 (4) ◽  
pp. 347-359 ◽  
Author(s):  
Theodore B. Trafalis ◽  
Anderson White
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Data Mining Techniques

Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error

2015 ◽  
Author(s):  
Auguste Lam ◽  
Alexander Ypma ◽  
Maxime Gatefait ◽  
David Deckers ◽  
Arne Koopman ◽  
...  
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Data Mining Techniques ◽  
Wafer Processing ◽  
And Control

Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

2020 ◽  
Author(s):  
Palina Zaiko ◽  
Siarhei Barodka ◽  
Aliaksandr Krasouski
Keyword(s):  
Data Assimilation ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Numerical Weather ◽  
Convective Processes ◽  
Different Seasons

<p>Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.</p><p>The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.</p><p>Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.</p>

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

A Radar Intelligence Simulation Method Based on Data Mining Techniques

2012 ◽  
Vol 263-266 ◽  
pp. 277-282 ◽  
Author(s):  
Xiao Chao Wu ◽  
Ying Cheng ◽  
Liao Liao Yan ◽  
Fang Xia Xue
Keyword(s):  
Data Mining ◽  
Historical Data ◽  
Simulation Method ◽  
Radar Data ◽  
Second Order ◽  
Test Bed ◽  
Discrete Curvature ◽  
Data Mining Techniques ◽  
Intelligent Information ◽  
Using Data

A new method to generate radar air intelligent information by using data mining techniques based on historical radar data is proposed. This method has two stages: One is “filtering separation - piecewise fitting - feature clustering". In this stage, the radar historical data is divided into the actual true track and noise. Through computing the second-order discrete curvature, the actual true track is decomposed into several segments, such as straight line and arc, which are fitted with multinomial subsequently. On this basis, after analyzing the characteristic vector of radar historical data, the clustering database is established; the other is “feature association-track recombination”. The track in pre-deigned air scenario is segmented by the second-order discrete curvature. After the correlative feature information of the segmented scenario is searched, matched and associated with the information in clustering database, a new track will be restructured by using this output results. This method is very available for its effective application in simulation test-bed of C3I system.

scholarly journals A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data

2015 ◽  
Vol 8 (2) ◽  
pp. 593-609 ◽  
Author(s):  
L. Norin
Keyword(s):  
Quantitative Analysis ◽  
Wind Turbines ◽  
Wind Farm ◽  
Negative Impact ◽  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Spectral Moments ◽  
Using Data ◽  
The Impact

Abstract. In many countries wind turbines are rapidly growing in numbers as the demand for energy from renewable sources increases. The continued deployment of wind turbines can, however, be problematic for many radar systems, which are easily disturbed by turbines located in the radar line of sight. Wind turbines situated in the vicinity of Doppler weather radars can lead to erroneous precipitation estimates as well as to inaccurate wind and turbulence measurements. This paper presents a quantitative analysis of the impact of a wind farm, located in southeastern Sweden, on measurements from a nearby Doppler weather radar. The analysis is based on 6 years of operational radar data. In order to evaluate the impact of the wind farm, average values of all three spectral moments (the radar reflectivity factor, absolute radial velocity, and spectrum width) of the nearby Doppler weather radar were calculated, using data before and after the construction of the wind farm. It is shown that all spectral moments, from a large area at and downrange from the wind farm, were impacted by the wind turbines. It was also found that data from radar cells far above the wind farm (near 3 km altitude) were affected by the wind farm. It is shown that this in part can be explained by detection by the radar sidelobes and by scattering off increased levels of dust and turbulence. In a detailed analysis, using data from a single radar cell, frequency distributions of all spectral moments were used to study the competition between the weather signal and wind turbine clutter. It is shown that, when weather echoes give rise to higher reflectivity values than those of the wind farm, the negative impact of the wind turbines is greatly reduced for all spectral moments.

scholarly journals Thunderstorm hail and lightning detection parameters based on dual-polarization Doppler weather radar data

2017 ◽  
Vol 24 (3) ◽  
pp. 521-530 ◽  
Author(s):  
Tanel Voormansik ◽  
Pekka J. Rossi ◽  
Dmitri Moisseev ◽  
Tarmo Tanilsoo ◽  
Piia Post
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Dual Polarization ◽  
Lightning Detection

scholarly journals Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

2019 ◽  
Vol 3 (2) ◽  
pp. 316
Author(s):  
Jorza Rulianto ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Software Testing ◽  
A Priori ◽  
Marketing Strategies ◽  
Motor Oil ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Recognition Techniques ◽  
Mathematical Techniques

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month & year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.

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