scholarly journals New Polarization Basis for Polarimetric Phased Array Weather Radar: Theory and Polarimetric Variables Measurement

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jian Dong ◽  
Qingfu Liu ◽  
Xuesong Wang
Keyword(s):  
Phased Array ◽  
Time Series Data ◽  
Weather Radar ◽  
Element Model ◽  
Polarization Measurement ◽  
Series Data ◽  
Precipitation Estimation ◽  
Dual Polarized ◽  
Measurement Biases ◽  
Reception Mode

A novel scheme is developed for mitigating measurement biases in agile-beam polarimetric phased array weather radar. Based on the orthogonal Huygens source dual-polarized element model, a polarization measurement basis for planar polarimetric phased array radar (PPAR) is proposed. The proposed polarization basis is orthogonal to itself after a 90° rotation along the array’s broadside and can well measure the characteristics of dual-polarized element. With polarimetric measurements being undertaken in this polarization basis, the measurement biases caused by the unsymmetrical projections of dual-polarized element’s fields onto the local horizontal and vertical directions of radiated beam can be mitigated. Polarimetric variables for precipitation estimation and classification are derived from the scattering covariance matrix in horizontal and vertical polarization basis. In addition, the estimates of these parameters based on the time series data acquired with the new polarization basis are also investigated. Finally, autocorrelation methods for both the alternate transmission and simultaneous reception mode and the simultaneous transmission and simultaneous reception mode are developed.

Initial Observations for Precipitation Cores With X-Band Dual Polarized Phased Array Weather Radar

2020 ◽  
Vol 58 (5) ◽  
pp. 3657-3666
Author(s):  
Hiroshi Kikuchi ◽  
Taku Suezawa ◽  
Tomoo Ushio ◽  
Nobuhiro Takahashi ◽  
Hiroshi Hanado ◽  
...  
Keyword(s):  
Phased Array ◽  
Weather Radar ◽  
X Band ◽  
Dual Polarized

scholarly journals Weather Radar Time Series Simulation: Improving Accuracy and Performance

2018 ◽  
Vol 35 (11) ◽  
pp. 2169-2187 ◽  
Author(s):  
Christopher D. Curtis
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Radar ◽  
Series Data ◽  
Signal Processing Algorithms ◽  
Graphical Processing ◽  
And Performance ◽  
Time Series Simulation ◽  
Improving Accuracy ◽  
Improved Technology

AbstractTime series simulation is an important tool for developing and testing new signal processing algorithms for weather radar. The methods for simulating time series data have not changed much over the last few decades, but recent advances in computing technology call for new methods. It would seem that faster computers would make better-performing simulators less necessary, but improved technology has made comprehensive, multiday simulations feasible. Even a relatively minor performance improvement can significantly shorten the time of one of these multiday simulations. Current simulators can also be inaccurate for some sets of parameters, especially narrow spectrum widths. In this paper, three new modifications to the conventional simulators are introduced to improve accuracy and performance. Two of the modifications use thresholds to optimize both the total number of samples and the number of random variates that need to be simulated. The third modification uses an alternative method for implementing the inverse Fourier transform. These new modifications lead to fast versions of the simulators that accurately match the desired autocorrelation and spectrum for a wide variety of signal parameters. Additional recommendations for using single-precision values and graphical processing units are also suggested.

scholarly journals Polarimetric Variables Retrieval with Clutter Suppression for Staggered PRT Sequences

2015 ◽  
Vol 32 (4) ◽  
pp. 767-782 ◽  
Author(s):  
Cuong M. Nguyen ◽  
V. Chandrasekar
Keyword(s):  
Time Series Data ◽  
Doppler Radar ◽  
Radar Data ◽  
Series Data ◽  
Dual Frequency ◽  
Ground Clutter ◽  
Transmission Modes ◽  
Repetition Time ◽  
Dual Polarized ◽  
The Time Domain

AbstractThis paper presents a procedure to filter ground clutter from dual-polarized staggered pulse repetition time (PRT) radar data in simultaneous and alternating transmission modes for polarimetric variables retrieval. The filter is designed in the time domain so that polarimetric variables such as the differential phase () and the copolar correlation coefficient () can be estimated directly from clutter-filtered time series data using a conventional method. In the case of the simultaneous mode, a single filter is used for both channels to maintain the signal correlation after filtering. For the alternating mode, because the polarizations are transmitted in different waveforms, two separate filters are required. However, the filters are designed so that the responses of the filters to the signals are identical within the extended Doppler range. Based on radar simulation, it is shown that the method can provide accurate retrieval of polarimetric variables even in the case of strong clutter contamination. Also, the performance of the method is illustrated on dual-polarized staggered PRT ⅔ data from the NASA dual-frequency dual-polarized Doppler radar (D3R).

A Time Series Weather Radar Simulator Based on High-Resolution Atmospheric Models

2008 ◽  
Vol 25 (2) ◽  
pp. 230-243 ◽  
Author(s):  
B. L. Cheong ◽  
R. D. Palmer ◽  
M. Xue
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Three Dimensional ◽  
Weather Radar ◽  
Series Data ◽  
Atmospheric Conditions ◽  
Moment Maps ◽  
Weather Model ◽  
Radar Simulator

Abstract A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.

Validation of X-band multi-parameter phased array weather radar by comparing data from Doppler weather radar with a parabolic dish antenna

2021 ◽  
Author(s):  
Keitaro Asai ◽  
Hiroshi Kikuchi ◽  
Tomoo Ushio ◽  
Yasuhide Hobara
Keyword(s):  
Phased Array ◽  
Correlation Coefficients ◽  
Parabolic Type ◽  
Weather Radar ◽  
Observation Time ◽  
High Density ◽  
Time Difference ◽  
Doppler Velocity ◽  
Differential Phase ◽  
Dual Polarized

AbstractThe multi-parameter phased array weather radar (MP-PAWR) was the first dual-polarized phased array weather radar to be commissioned in Japan (2017). When conducting a volume scan, the MP-PAWR respectively uses electronic and mechanical scanning in the elevation and azimuth angles to achieve rapid scanning and high-density observations. Although the effectiveness of the MP-PAWR has been demonstrated in case studies, its observation accuracy is yet to be quantitatively analyzed. Therefore, this study compared data of MP-PAWR with that of an operational dual-polarized weather radar with a parabolic-type antenna (X-MP radar) using 2,347,097 data samples obtained over 14 h. The results showed that the observation accuracy of the MP-PAWR was approximately the same as that of the X-MP radar at low elevations. The correlations of observational parameters (radar reflectivity factor, differential resistivity, specific differential phase, and Doppler velocity) between the MP-PAWR and X-MP radar ranged from 0.77–0.99 when MP-PAWR data were recorded within 15 s of the X-MP radar observations. The correlation between the observational parameters of the two radars decreased as the observation time difference between the X-MP radar and MP-PAWR increased. In particular, the correlation coefficients between the specific differential phase and the differential reflectivity were considerably lower than the single-polarization parameter at observation time difference of 240–300 s. By providing high-frequency and high-density dual-polarization observations, the MP-PAWR can contribute to rainfall prediction in Japan and reduce the damage caused by localized, rapidly developing cumulonimbus clouds.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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