Modeling Non-Maturing Deposits: A Procedure for the Determination of the Minimal Time Series Length Required for Model Calibration and Back-Testing

2021 ◽  
Author(s):  
Sophie Döpp ◽  
Andre Horovitz ◽  
Alexander Szimayer
Keyword(s):  
Time Series ◽  
Model Calibration ◽  
Series Length ◽  
Minimal Time ◽  
Back Testing

Satellite altimetry for hydraulic model calibration: potential of single and multi-mission series

2020 ◽  
Author(s):  
Giada Molari ◽  
Alessio Domeneghetti ◽  
Mohammad Tourian ◽  
Angelica Tarpanelli ◽  
Tommaso Moramarco ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Model Calibration ◽  
Hydraulic Model ◽  
Data Availability ◽  
Data Uncertainty ◽  
Calibration Error ◽  
Series Length ◽  
Po River ◽  
Short Period

<p>The recent improvement of satellite products has provided an increasing data availability with an unprecedented coverage, stimulating their usage in hydraulic and hydrological fields. Notwithstanding, regarding the satellite water level monitoring, the limited temporal resolution (i.e. revisit time varying from 10 to 35 days) and decimeter accuracy of  altimetry satellites strongly restrict their applications. Recently proposed multi-mission (MM) densified time series might represent a possible alternative to ensure higher spatial and temporal coverage. However, an assessment of the potential of different altimetry products, including MM series, for hydrodynamic model calibration is still missing. The goal of this study is the assessment of remotely sensed water surface elevations usefulness for the calibration of a hydraulic model implemented for a 140-km stretch of the Po River (Italy). In particular this study presents: i) a comparison of altimetry satellite data collected from different missions (ENVISAT (E), ENVISAT extended (EX), TOPEX/Poseidon (TP), SARAL/AltiKa (SA), Jason-2 (J2) and Jason-3 (J3); ii) insights to the effects of satellite series length on hydraulic model calibration; iii) the analysis of how data uncertainty influences model accuracy; iv) the potential of multi-mission (MM) densified time series as possible alternative to overcome spatial and temporal limitations of single mission. The results highlight a good agreement among satellite and in-situ observations for all the series, excluding EX series. J2 provides the best outcome in terms of calibration error (about 30 cm) and number of measurements required to achieve a reliable calibration (less than 1 year of data). In case of frequent and accurate satellite data (i.e. J2 and TP), the MM series seem unable to provide additional benefits in calibrating the hydraulic model. On the other hand, MM series outperform low frequency products (i.e. E and SA) when the latter are available only for short period. This research offers a wide overview of the potential of altimetry products, providing a general comparison of different satellite missions series and showing the potential, as well as limitations, offered by multi-mission series.</p>

New determinations of tides on the north-western Ross Ice Shelf

2020 ◽  
pp. 1-14
Author(s):  
Richard D. Ray ◽  
Kristine M. Larson ◽  
Bruce J. Haines
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
High Rate ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
The North ◽  
Global Positioning ◽  
North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

scholarly journals Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements

Landslides ◽  
2021 ◽  
Author(s):  
Chuang Song ◽  
Chen Yu ◽  
Zhenhong Li ◽  
Veronica Pazzi ◽  
Matteo Del Soldato ◽  
...  
Keyword(s):  
Time Series ◽  
Seismic Noise ◽  
Slip Surface ◽  
Inversion Method ◽  
Subsurface Structures ◽  
Slip Surfaces ◽  
Landslide Volume ◽  
Noise Measurements ◽  
Landslide Body

AbstractInterferometric Synthetic Aperture Radar (InSAR) enables detailed investigation of surface landslide movements, but it cannot provide information about subsurface structures. In this work, InSAR measurements were integrated with seismic noise in situ measurements to analyse both the surface and subsurface characteristics of a complex slow-moving landslide exhibiting multiple failure surfaces. The landslide body involves a town of around 6000 inhabitants, Villa de la Independencia (Bolivia), where extensive damages to buildings have been observed. To investigate the spatial-temporal characteristics of the landslide motion, Sentinel-1 displacement time series from October 2014 to December 2019 were produced. A new geometric inversion method is proposed to determine the best-fit sliding direction and inclination of the landslide. Our results indicate that the landslide is featured by a compound movement where three different blocks slide. This is further evidenced by seismic noise measurements which identified that the different dynamic characteristics of the three sub-blocks were possibly due to the different properties of shallow and deep slip surfaces. Determination of the slip surface depths allows for estimating the overall landslide volume (9.18 · 107 m3). Furthermore, Sentinel-1 time series show that the landslide movements manifest substantial accelerations in early 2018 and 2019, coinciding with increased precipitations in the late rainy season which are identified as the most likely triggers of the observed accelerations. This study showcases  the potential of integrating InSAR and seismic noise techniques to understand the landslide mechanism from ground to subsurface.

scholarly journals How to correctly apply Gaussian statistics in a non-stationary climate?

2021 ◽  
Author(s):  
Reinhold Steinacker
Keyword(s):  
Time Series ◽  
Actual Data ◽  
Temperature Record ◽  
Positive Trend ◽  
Positive Temperature ◽  
Climatic Trend ◽  
Filter Methods ◽  
Periodic Time Series ◽  
Past Data

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals Demand forecasting in Small and Medium Enterprises (SMEs) ED Aluminium Yogyakarta using causal, time series, and combined causal-time series approaches

2018 ◽  
Vol 204 ◽  
pp. 01004 ◽  
Author(s):  
Wildanul Isnaini ◽  
Andi Sudiarso
Keyword(s):  
Time Series ◽  
Small And Medium Enterprises ◽  
Demand Forecasting ◽  
Actual Condition ◽  
Inventory Data ◽  
Inventory Cost ◽  
Forecasting Method ◽  
Medium Enterprises ◽  
Better Than

ED Aluminium is the biggest Small and Medium Enterprises (SMEs) in Daerah Istimewa Yogyakarta (DIY) with 90 number of workers and 1,5 ton ingot capacity for production (Isnaini, 2014). Inventory data in December 2015 indicates that some products are overstocked (9%) and stockout (83%). This condition can happend because that SMEs still using intuition to predict the number of demand. Inventory fluctuation causes the inventory cost increases while overstock happend and lost the opportunity cost during stockout. To avoid overstock and stockout, the determination of demand with exact method is needed and one of them can be solved by forecasting method. This study aims to find the best forecasting methods of demand in 2015 using causal, time series, and combined causal-time series approces that better than the actual condition. The results of this research is the best forecasting method used to predict the number of sales in January-November 2015, that are SARIMA (3,1,1)(0,1,1)12 for WB, SARIMA (1,1,1)(1,0,1)6 for WSD, SARIMA (1,1,1)(1,1,0)6 for DE, SARIMA (2,1,1)(1,1,0)6 for PE, and SARIMA (2,1,3)(0,1,0)12 for PT.

Analysis of Stall Precursors and Determination of Optimum Signal Processing Methods for an LM-2500 Gas Turbine

2008 ◽  
Author(s):  
Knox T. Millsaps ◽  
Gustave C. Dahl ◽  
Daniel E. Caguiat ◽  
Jeffrey S. Patterson
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Gas Turbine ◽  
Rotor Speed ◽  
Processing Methods ◽  
Turbine Engine ◽  
Pressure Signal ◽  
Time Series Analyses ◽  
Signal Processing Methods

This paper presents an analysis of data taken from several stall initiation events on a GE LM-2500 gas turbine engine. Specifically, the time series of three separate pressure signals located at compressor stages 3, 6, and 15 were analyzed utilizing various signal processing methods to determine the most reliable indicator of incipient stall for this engine. The spectral analyses performed showed that rotating precursor waves traveling around the annulus at approximately half of the rotor speed were the best indicators. Non-linear chaotic time series analyses were also used to predict stall, but it was not as reliable an indicator. Several algorithms were used and it was determined that stall wave perturbations can be reliably identified about 900 revolutions prior to the stall. This work indicates that a single pressure signal located at stage 3 on an LM-2500 gas turbine is sufficient to provide advance warning of more than 2 seconds prior to the fully developed stall event.

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