scholarly journals Investigation of GPS draconitic year effect on GPS time series of eliminated eclipsing GPS satellite data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
A. Allahverdi-zadeh ◽  
J. Asgari ◽  
A.R. Amiri-Simkooei
Keyword(s):  
Time Series ◽  
Original Position ◽  
Year Effect ◽  
The Earth ◽  
Gps Satellites ◽  
Position Time Series ◽  
The Difference ◽  
Original Time ◽  
Almost All ◽  
New Time

AbstractGPS draconitic signal (351.6 ± 0.2 days) and its higher harmonics are observed at almost all IGS products such as position time series of IGS permanent stations. Orbital error and multipath are known as two possible sources of these signals. The effect of Earth shadow crossing of GPS satellites is another suspect for this signal. Up to now there is no serious attempt to investigate this dependence. AMATLAB toolbox is developed and used to determine the satellites located at the earth shadow. RINEX observation files and precise ephemeris are imported to the toolbox and a cylindrical model is used to detect the shadow regions. Data of these satellites were removed from the RINEX observation files of three IGS permanent stations (GRAZ,ONSAandWSRT) and new RINEX observation fileswere created. The time span of these data is about 11 years. The new and original fileswere then processed using precise point positioning (PPP) method to determine position time series, for further analysis. Both the original and new time series were analyzed using the least squares harmonic estimation (LS-HE) in the following steps. The 1st step is the validation of the draconitic harmonics signature in the original position time series of the three stations. The 2nd step does the same for the new time series. It confirms that the power spectrum at the draconitic signals decreases to some extent for the new time series. The difference between the original and new time series (difference between all three position quantity (X, Y and Z)) is then analyzed in the 3rd step. Signature of the draconitic harmonics is also observed to the differences. The results represent that all eight harmonics of GPS draconitic period do exist at the residuals and mainly they decrease. All of the three stations were then processed together using the multivariate LS-HE method. At the 4th step, the difference of the spectral values between the original time series and new times serieswere analyzed. Decreasing of the spectral values at most harmonics (e.g. 1st, 2nd, 4th, 6th, 7th and 8th) represents the effect of removing satellite observations at shadow of the earth on draconitic harmonics. At least, five harmonics among seven shows the amelioration of results (draconitic error reduction) after removing the earth shadowed data from RINEX raw data. The results show that the draconitic year’s component of data is in part due to eclipsing satellites.

Data Fusion of Total Solar Irradiance Composite Time Series Using 40 years of Satellite Measurements: First Results 

2021 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Wolfgang Finsterle ◽  
Werner Schmutz ◽  
Margit Haberreiter ◽  
Rok Sikonja
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Solar Irradiance ◽  
Total Solar Irradiance ◽  
Maunder Minimum ◽  
The Sun ◽  
Climate Reconstructions ◽  
The Earth ◽  
First Results ◽  
The Difference

<p><span>Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements are important to estimate the Earth’s energy imbalance, </span><span>i.e. the difference of energy absorbed and emitted by our planet. Climate modelers need the solar forcing time series in their models in order to study the influence of the Sun on the Earth’s climate. With this amount of TSI data, solar irradiance reconstruction models  can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). V</span><span>arious algorithms have been proposed in the last decade to merge the various TSI measurements over the 40 years of recording period. We have developed a new statistical algorithm based on data fusion.  The stochastic noise processes of the measurements are modeled via a dual kernel including white and coloured noise.  We show our first results and compare it with previous releases (PMOD,ACRIM, ... ). </span></p>

scholarly journals Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

2022 ◽  
Author(s):  
Zekai Sen
Keyword(s):  
Time Series ◽  
White Noise ◽  
Trend Analysis ◽  
Serial Correlation ◽  
Original Time Series ◽  
Trend Component ◽  
The Difference ◽  
Temperature Records ◽  
Original Time ◽  
Trend Identification

Abstract To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

scholarly journals Optimal Strategy of a GPS Position Time Series Analysis for Post-Glacial Rebound Investigation in Europe

2019 ◽  
Vol 11 (10) ◽  
pp. 1209 ◽  
Author(s):  
Janusz Bogusz ◽  
Anna Klos ◽  
Krzysztof Pokonieczny
Keyword(s):  
Time Series ◽  
Western Europe ◽  
Statistical Significance ◽  
Vertical Position ◽  
Isostatic Adjustment ◽  
Glacial Rebound ◽  
Position Time Series ◽  
Terrestrial Hydrology ◽  
The Difference ◽  
The Baltic

We describe a comprehensive analysis of the 469 European Global Positioning System (GPS) vertical position time series. The assumptions we present should be employed to perform the post-glacial rebound (PGR)-oriented comparison. We prove that the proper treatment of either deterministic or stochastic components of the time series is indispensable to obtain reliable vertical velocities along with their uncertainties. The statistical significance of the vertical velocities is examined; due to their small vertical rates, 172 velocities from central and western Europe are found to fall below their uncertainties and excluded from analyses. The GPS vertical velocities reach the maximum values for Scandinavia with the maximal uplift equal to 11.0 mm/yr. Moreover, a comparison between the GPS-derived rates and the present-day motion predicted by the newest Glacial Isostatic Adjustment (GIA) ICE-6G_C (VM5a) model is provided. We prove that these rates agree at a 0.5 mm/yr level on average; the Sweden area with the most significant uplift observed agrees within 0.2 mm/yr. The largest discrepancies between GIA-predicted uplift and the GPS vertical rates are found for Svalbard; the difference is equal to 6.7 mm/yr and arises mainly from the present-day ice melting. The GPS-derived vertical rates estimated for the southern coast of the Baltic Sea are systematically underestimated by the GIA prediction by up to 2 mm/yr. The northern British Isles vertical rates are overestimated by the GIA model by about 0.5 mm/yr. The area of the Netherlands and the coastal area of Belgium are both subsiding faster than it is predicted by the GIA model of around 1 mm/yr. The inland part of Belgium, Luxemburg and the western part of Germany show strong positive velocities when compared to the GIA model. Most of these stations uplift of more than 1 mm/yr. It may be caused by present-day elastic deformation due to terrestrial hydrology, especially for Rhein basin, or non-tidal atmospheric loading, for Belgium and Luxembourg.

scholarly journals Deep Learning-based Damage Mapping with InSAR Coherence Time Series

2021 ◽  
Author(s):  
Oliver Stephenson ◽  
Tobias Köhne ◽  
Eric Zhan ◽  
Brent Cahill ◽  
Sang-Ho Yun ◽  
...  
Keyword(s):  
Time Series ◽  
Detection Threshold ◽  
Time History ◽  
Weather Conditions ◽  
Coherence Time ◽  
Full Time ◽  
The Earth ◽  
The Difference ◽  
Damage Mapping ◽  
Interferometric Sar

<p>Satellite remote sensing is playing an increasing role in rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth’s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth’s surface. In this study, we propose to map damage using the full time history of SAR observations of an impacted region from a single satellite constellation in order to detect anomalous variations in the Earth’s surface properties due to a natural disaster. We quantify Earth surface change using time series of sequential interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local temporal behavior before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with measured damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.</p>

Faithfulness of Recurrence Plots: A Mathematical Proof

2015 ◽  
Vol 25 (12) ◽  
pp. 1550168 ◽  
Author(s):  
Yoshito Hirata ◽  
Motomasa Komuro ◽  
Shunsuke Horai ◽  
Kazuyuki Aihara
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mathematical Proof ◽  
Recurrence Plot ◽  
Series Data ◽  
Recurrence Plots ◽  
Original Time Series ◽  
Mild Conditions ◽  
Original Time ◽  
Almost All

It is practically known that a recurrence plot, a two-dimensional visualization of time series data, can contain almost all information related to the underlying dynamics except for its spatial scale because we can recover a rough shape for the original time series from the recurrence plot even if the original time series is multivariate. We here provide a mathematical proof that the metric defined by a recurrence plot [Hirata et al., 2008] is equivalent to the Euclidean metric under mild conditions.

The Origin of the Moon

1962 ◽  
Vol 14 ◽  
pp. 149-155 ◽  
Author(s):  
E. L. Ruskol
Keyword(s):  
Chemical Composition ◽  
Solar System ◽  
The Moon ◽  
The Earth ◽  
The Difference ◽  
Origin Of The Moon

The difference between average densities of the Moon and Earth was interpreted in the preceding report by Professor H. Urey as indicating a difference in their chemical composition. Therefore, Urey assumes the Moon's formation to have taken place far away from the Earth, under conditions differing substantially from the conditions of Earth's formation. In such a case, the Earth should have captured the Moon. As is admitted by Professor Urey himself, such a capture is a very improbable event. In addition, an assumption that the “lunar” dimensions were representative of protoplanetary bodies in the entire solar system encounters great difficulties.

EVALUASI PROYEKSI DEMAND BARANG DAN PENUMPANG PELABUHAN DI PROVINSI MALUKU

2019 ◽  
Vol 19 (2) ◽  
pp. 101-110
Author(s):  
Adrian Firdaus ◽  
M. Dwi Yoga Sutanto ◽  
Rajin Sihombing ◽  
M. Weldy Hermawan
Keyword(s):  
Time Series ◽  
Projection Method ◽  
Development Plan ◽  
Master Plan ◽  
Port Development ◽  
Loading And Unloading ◽  
The Difference ◽  
Existing Data

Abstract Every port in Indonesia must have a Port Master Plan that contains an integrated port development plan. This study discusses one important aspect in the preparation of the Port Master Plan, namely the projected movement of goods and passengers, which can be used as a reference in determining the need for facilities at each stage of port development. The case study was conducted at a port located in a district in Maluku Province and aims to evaluate the analysis of projected demand for goods and passengers occurring at the port. The projection method used is time series and econometric projection. The projection results are then compared with the existing data in 2018. The results of this study show that the econometric projection gives adequate results in predicting loading and unloading activities as well as the number of passenger arrival and departure in 2018. This is indicated by the difference in the percentage of projection results towards the existing data, which is smaller than 10%. Whereas for loading and unloading activities, time series projections with logarithmic trends give better results than econometric projections. Keywords: port, port master plan, port development, unloading activities  Abstrak Setiap pelabuhan di Indonesia harus memiliki sebuah Rencana Induk Pelabuhan yang memuat rencana pengem-bangan pelabuhan secara terpadu. Studi ini membahas salah satu aspek penting dalam penyusunan Rencana Induk Pelabuhan, yaitu proyeksi pergerakan barang dan penumpang, yang dapat dipakai sebagai acuan dalam penentuan kebutuhan fasilitas di setiap tahap pengembangan pelabuhan. Studi kasus dilakukan pada sebuah pelabuhan yang terletak di sebuah kabupaten di Provinsi Maluku dan bertujuan untuk melakukan evaluasi ter-hadap analisis proyeksi demand barang dan penumpang yang terjadi di pelabuhan tersebut. Metode proyeksi yang dipakai adalah proyeksi deret waktu dan ekonometrik. Hasil proyeksi selanjutnya dibandingkan dengan data eksisting tahun 2018. Hasil studi ini menunjukkan bahwa proyeksi ekonometrik memberikan hasil yang cukup baik dalam memprediksi aktivitas bongkar barang serta jumlah penumpang naik dan turun di tahun 2018. Hal ini diindikasikan dengan selisih persentase hasil proyeksi terhadap data eksisting yang lebih kecil dari 10%. Sedangkan untuk aktivitas muat barang, proyeksi deret waktu dengan tren logaritmik memberikan hasil yang lebih baik daripada proyeksi ekonometrik. Kata-kata kunci: pelabuhan, rencana induk pelabuhan, pengembangan pelauhan, aktivitas bongkar barang

scholarly journals Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID-19 in China

2020 ◽  
Vol 9 (s1) ◽  
Author(s):  
Babak Jamshidi ◽  
Shahriar Jamshidi Zargaran ◽  
Mansour Rezaei
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Communicable Disease ◽  
Time Series Models ◽  
Cumulative Number ◽  
The Novel ◽  
New Family ◽  
Two Samples ◽  
Almost All ◽  
Number Of Individuals

AbstractIntroductionTime series models are one of the frequently used methods to describe the pattern of spreading an epidemic.MethodsWe presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples.ResultsWe estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China.DiscussionOur model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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