A Software Tool for Processing the Displacement Time Series Extracted from Raw Radar Data

2010 ◽  
Author(s):  
Francesco Coppi ◽  
Carmelo Gentile ◽  
Pier Paolo Ricci ◽  
E. P. Tomasini
Keyword(s):  
Time Series ◽  
Software Tool ◽  
Radar Data

Software Tool for Assessment of Complexity and Variability in Physiological Signals of Respiration

2011 ◽  
Vol 1 (2) ◽  
pp. 28-53
Author(s):  
Ireneusz Jablonski ◽  
Kamil Subzda ◽  
Janusz Mroczka
Keyword(s):  
Complex System ◽  
Experimental Data ◽  
Time Series ◽  
Data Analysis ◽  
Input Signal ◽  
Software Tool ◽  
Point Of View ◽  
Software Implementation ◽  
Physiological Signals ◽  
Event Time

In this paper, the authors examine software implementation and the initial preprocessing of data and tools during the assessment of the complexity and variability of long physiological time-series. The algorithms presented advance a bigger Matlab library devoted to complex system and data analysis. Commercial software is unavailable for many of these functions and is generally unsuitable for use with multi-gigabyte datasets. Reliable inter-event time extraction from input signal is an important step for the presented considerations. Knowing the distribution of the inter-event time distances, it is possible to calculate exponents due to power-law scaling. From a methodology point of view, simulations and considerations with experimental data supported each stage of the work presented. In this paper, initial calibration of the procedures with accessible data confirmed assessments made during earlier studies, which raise objectivity of measurements planned in the future.

scholarly journals Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1272 ◽  
Author(s):  
Stephanie Olen ◽  
Bodo Bookhagen
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Natural Hazard ◽  
Low Cost ◽  
Region Of Interest ◽  
Radar Data ◽  
Coherence Time ◽  
Rapid Identification ◽  
Before And After ◽  
Event Distribution

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.

scholarly journals Controls on short-term variations in Greenland glacier dynamics

2013 ◽  
Vol 59 (217) ◽  
pp. 883-892 ◽  
Author(s):  
A.V. Sundal ◽  
A. Shepherd ◽  
M. van den Broeke ◽  
J. Van Angelen ◽  
N. Gourmelen ◽  
...  
Keyword(s):  
Time Series ◽  
Radar Data ◽  
Early Summer ◽  
Short Term ◽  
Synthetic Aperture Radar Data ◽  
Velocity Fluctuations ◽  
Speed Up ◽  
Front Advance ◽  
Enhanced Surface ◽  
Front Dynamics

AbstractShort-term ice-dynamical processes at Greenland’s Jakobshavn and Kangerdlugssuaq glaciers were studied using a 3 day time series of synthetic aperture radar data acquired during the 2011 European Remote-sensing Satellite-2 (ERS-2) 3 day repeat campaign together with modelled meteorological parameters. The time series spans the period March–July 2011 and captures the first ∼30% of the summer melting season. In both study areas, we observe velocity fluctuations at the lower ∼10 km of the glacier. At Jakobshavn Isbræ, where our dataset covers the first part of the seasonal calving-front retreat, we identify ten calving episodes, with a mean calving-front area loss of 1.29 ± 0.4 km2. Significant glacier speed-up was observed in the near-terminus area following all calving episodes. We identify changes in calving-front geometry as the dominant control on velocity fluctuations on both glaciers, apart from a <15% early-summer speed-up at Kangerdlugssuaq Glacier during a period of calving-front advance, which we attribute to enhanced surface melt-induced basal lubrication. Our 3 day velocity maps show new spatial characteristics of the ice melange flow variability in the Jakobshavn and Kangerdlugssuaq fjord systems, which are primarily controlled by calving-front dynamics and fjord geometry.

scholarly journals Targets Detection in Sea Clutter Based on Echo State Network

2012 ◽  
Vol 4 ◽  
pp. 255-258
Author(s):  
Zhan Xu ◽  
Jian Wei Wan ◽  
Gang Li ◽  
Fang Su
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Real Life ◽  
Radar Data ◽  
Recursive Least Squares ◽  
Echo State Network ◽  
Sea Clutter ◽  
Rls Algorithm ◽  
Real Life Data ◽  
Novel Method

A novel method to predict the sea clutter time series and detect target embedded in sea clutter is presented. The method is actually a recurrent neural network called an echo state network (ESN). A recursive least squares (RLS) algorithm is used for updating the output weights of ESN. A set of time series from IPIX radar data is tested. Numerical experiments reveal that the proposed network shows higher prediction precision in pure sea clutter data. Moreover, the mean squared error (MSE) between real-life data and prediction value by ESN can be used to detect target effectively.

An Automated Python Algorithm to Quantify ZDR Arc and KDP-ZDR Separation Signatures in Supercells

2020 ◽  
Author(s):  
Matthew B. Wilson ◽  
Matthew S. Van Den Broeke
Keyword(s):  
Time Series ◽  
Recent Work ◽  
Radar Data ◽  
Dual Polarization ◽  
Low Level ◽  
Supercell Storms ◽  
Size Sorting ◽  
Supercell Thunderstorms ◽  
Differential Reflectivity

AbstractSupercell thunderstorms often have pronounced signatures of hydrometeor size sorting within their forward flank regions, including an arc-shaped region of high differential reflectivity (ZDR) along the inflow edge of the forward flank known as the ZDR arc and a clear horizontal separation between this area of high ZDP values and and an area of enhanced KDP values deeper into the storm core. Recent work has indicated that ZDR arc and KDP-ZDR separation signatures in supercell storms may be related to environmental storm-relative helicity and low-level shear. Thus, characteristics of these signatures may be helpful to indicate whether a given storm is likely to produce a tornado. Although ZDR arc and KDP-ZDR separation signatures are typically easy to qualitatively identify in dual-polarization radar fields, quantifying their characteristics can be time-consuming and makes research into these signatures and their potential operational applications challenging. To address this problem, this paper introduces an automated Python algorithm to objectively identify and track these signatures in Weather Surveillance Radar-1988 Doppler (WSR-88D) radar data and quantify their characteristics. This paper will discuss the development of the algorithm, demonstrate its performance through comparisons with manually-generated time series of ZDR arc and KDP-ZDR separation signature characteristics, and briefly explore potential uses of this algorithm in research and operations.

From mess to mass: a methodology for calculating storm event pollutant loads with their uncertainties, from continuous raw data time series

2011 ◽  
Vol 63 (3) ◽  
pp. 369-376 ◽  
Author(s):  
M. Métadier ◽  
J. -L. Bertrand-Krajewski
Keyword(s):  
Time Series ◽  
Oxygen Demand ◽  
Software Tool ◽  
Large Data ◽  
Sensor Calibration ◽  
Storm Event ◽  
Storm Events ◽  
Time Step ◽  
Raw Data ◽  
Sewer Systems

With the increasing implementation of continuous monitoring of both discharge and water quality in sewer systems, large data bases are now available. In order to manage large amounts of data and calculate various variables and indicators of interest it is necessary to apply automated methods for data processing. This paper deals with the processing of short time step turbidity time series to estimate TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) event loads in sewer systems during storm events and their associated uncertainties. The following steps are described: (i) sensor calibration, (ii) estimation of data uncertainties, (iii) correction of raw data, (iv) data pre-validation tests, (v) final validation, and (vi) calculation of TSS and COD event loads and estimation of their uncertainties. These steps have been implemented in an integrated software tool. Examples of results are given for a set of 33 storm events monitored in a stormwater separate sewer system.

scholarly journals Delphi Method Supported by Forecasting Software

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Marcin Lawnik ◽  
Arkadiusz Banasik
Keyword(s):  
Time Series ◽  
Programming Languages ◽  
Delphi Method ◽  
Software Tool ◽  
Mathematical Tool ◽  
Key Factors ◽  
Programming Library ◽  
Multi Stage ◽  
Virtual Expert ◽  
R Programming

The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multi-stage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computer-enhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet library—a Facebook tool that can be used in Python or R programming languages.

scholarly journals Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

2019 ◽  
Vol 11 (3) ◽  
pp. 334 ◽  
Author(s):  
Cecília Lira Melo de Oliveira Santos ◽  
Rubens Augusto Camargo Lamparelli ◽  
Gleyce Kelly Dantas Araújo Figueiredo ◽  
Stéphane Dupuy ◽  
Julie Boury ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Cover ◽  
Radar Data ◽  
Agricultural Landscapes ◽  
Gap Filling ◽  
Use Of Time ◽  
High Quality Image ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
Heterogeneous Region

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.

A multi-frequency framework for soil moisture retrieval from time series radar data

2019 ◽  
Vol 235 ◽  
pp. 111433 ◽  
Author(s):  
Liujun Zhu ◽  
Jeffrey P. Walker ◽  
Leung Tsang ◽  
Huanting Huang ◽  
Nan Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Radar Data
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