scholarly journals Measurement and interpretation of subtle deformation signals at Unimak Island from 2003 to 2010 using weather model‐assisted time series InSAR

2015 ◽  
Vol 120 (2) ◽  
pp. 1175-1194 ◽  
Author(s):  
W. Gong ◽  
F. J. Meyer ◽  
C.‐W. Lee ◽  
Z. Lu ◽  
J. Freymueller
Keyword(s):  
Time Series ◽  
Weather Model ◽  
Unimak Island

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

scholarly journals Assessing marine operations with a Markov-switching autoregressive metocean model

2020 ◽  
Vol 234 (4) ◽  
pp. 785-802
Author(s):  
Jack Paterson ◽  
Philipp R Thies ◽  
Roman Sueur ◽  
Jérôme Lonchampt ◽  
Federico D’Amico
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wave Height ◽  
Selection Process ◽  
Markov Switching ◽  
Transfer Task ◽  
Weather Conditions ◽  
Weather Model ◽  
Wide Range ◽  
Modelling Methodology

This article presents a metocean modelling methodology using a Markov-switching autoregressive model to produce stochastic wind speed and wave height time series, for inclusion in marine risk planning software tools. By generating a large number of stochastic weather series that resemble the variability in key metocean parameters, probabilistic outcomes can be obtained to predict the occurrence of weather windows, delays and subsequent operational durations for specific tasks or offshore construction phases. To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to seasonal and inter-monthly fluctuations at each site, generating realistic time series to support weather risk assessments. A model selection process is presented for both weather parameters across three locations, and a personnel transfer task is used to contextualise a realistic weather window analysis. Summarising plots demonstrate the validity of the presented methodology and that a small extension improves the adaptability of the approach for sites with strong correlations between wind speed and wave height. It is concluded that the overall methodology can produce suitable wind speed and wave time series for the assessment of marine operations, yet it is recommended that the methodology is applied to other sites and operations, to determine the method’s adaptability to a wide range of offshore locations.

scholarly journals Tropospheric products of the second GOP European GNSS reprocessing (1996–2014)

2017 ◽  
Vol 10 (9) ◽  
pp. 3589-3607 ◽  
Author(s):  
Jan Dousa ◽  
Pavel Vaclavovic ◽  
Michal Elias
Keyword(s):  
Time Series ◽  
International Association ◽  
A Priori ◽  
Mapping Function ◽  
Satellite System ◽  
Weather Data ◽  
Horizontal Gradients ◽  
Atmospheric Loading ◽  
Weather Model ◽  
The Impact

Abstract. In this paper, we present results of the second reprocessing of all data from 1996 to 2014 from all stations in International Association of Geodesy (IAG) Reference Frame Sub-Commission for Europe (EUREF) Permanent Network (EPN) as performed at the Geodetic Observatory Pecný (GOP). While the original goal of this research was to ultimately contribute to the realization of a new European Terrestrial Reference System (ETRS), we also aim to provide a new set of GNSS (Global Navigation Satellite System) tropospheric parameter time series with possible applications to climate research. To achieve these goals, we improved a strategy to guarantee the continuity of these tropospheric parameters and we prepared several variants of troposphere modelling. We then assessed all solutions in terms of the repeatability of coordinates as an internal evaluation of applied models and strategies and in terms of zenith tropospheric delays (ZTDs) and horizontal gradients with those of the ERA-Interim numerical weather model (NWM) reanalysis. When compared to the GOP Repro1 (first EUREF reprocessing) solution, the results of the GOP Repro2 (second EUREF reprocessing) yielded improvements of approximately 50 and 25 % in the repeatability of the horizontal and vertical components, respectively, and of approximately 9 % in tropospheric parameters. Vertical repeatability was reduced from 4.14 to 3.73 mm when using the VMF1 mapping function, a priori ZHD (zenith hydrostatic delay), and non-tidal atmospheric loading corrections from actual weather data. Raising the elevation cut-off angle from 3 to 7° and then to 10° increased RMS from coordinates' repeatability, which was then confirmed by independently comparing GNSS tropospheric parameters with the NWM reanalysis. The assessment of tropospheric horizontal gradients with respect to the ERA-Interim revealed a strong sensitivity of estimated gradients to the quality of GNSS antenna tracking performance. This impact was demonstrated at the Mallorca station, where gradients systematically grew up to 5 mm during the period between 2003 and 2008, before this behaviour disappeared when the antenna at the station was changed. The impact of processing variants on long-term ZTD trend estimates was assessed at 172 EUREF stations with time series longer than 10 years. The most significant site-specific impact was due to the non-tidal atmospheric loading followed by the impact of changing the elevation cut-off angle from 3 to 10°. The other processing strategy had a very small or negligible impact on estimated trends.

Seasonal active landsliding and hillslope activity in the southern Central Andes of NW Argentina

2020 ◽  
Author(s):  
Mohammad M.Aref ◽  
Bodo Bookhagen ◽  
Taylor T. Smith ◽  
Manfred R. Strecker
Keyword(s):  
Time Series ◽  
Driving Forces ◽  
Central Andes ◽  
Temporal Variations ◽  
High Elevation ◽  
Optical Data ◽  
Tropospheric Delay ◽  
Active Layer Thickness ◽  
Weather Model ◽  
3D Deformation

<p>The eastern Central Andes of northwestern Argentina is characterized by a steep topographic gradient with elevations ranging from 1000m in the foreland to more than 6000m in the eastern Andean Cordillera. This setting furthermore shows high topographic relief with deeply incised river valleys that are frequently impacted by strong rainfall events driven by the South American monsoon. Additionally, a strong vegetation cover contrast from dense coverage in the low elevation foreland to sparse coverage at high elevation defines the environmental gradient in this area. This area is impacted by several types of hillslope instabilities and landsliding: at some high elevations above 5000m hillslope instability are related to solifluction processes, whereas shallow and deep seated landsliding affect geologically preconditioned areas.</p><p>Here we use a combination of different radar sensors and wavelengths to describe the 3D deformation signal of instable hillslopes: TerraSAR-X, Sentinel-1, and ALOS2. To mitigate the tropospheric delay from InSAR measurements, phase-based and weather model approaches are applied to improve the spatial and temporal variations of displacement signals.  We use persistent and small baseline subsets (SBAS) category of distributed scatterer approaches to derive deformation fields and we invert for 3D deformation fields using several look angles in combination with GNSS data under different assumptions including that the horizontal component has a motion parallel to the downhill slope. We analyze Line-of-sight (LOS) time series and combine deformation fields with temperature and rainfall measurements to better understand driving forces of high-elevation hillslope instabilities We describe two deep-seated landslides with downslope velocities exceeding 5-10 cm/yr and we exploit image-cross correlation techniques of optical data to monitor seasonal and inter-annual changes. The periodic changes of InSAR deformation and temperature time series show freeze-thaw processes of the active layer thickness of the permafrost areas at elevations exceeding 5000m. We document that deep-seated, fast moving landslides are related to geologic preconditioning. The combination of SAR and optical approaches helps to describe hillslope regimes in steep and difficult to access terrain.</p>

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.

scholarly journals Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Hassan A. N. Hejase ◽  
Ali H. Assi
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Bias Error ◽  
Time Series Regression ◽  
Weather Model ◽  
Al Ain ◽  
Using Data ◽  
Long Term Prediction

The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.

Towards a new generation of Generic Atmospheric Correction Online Service for InSAR (GACOS 2.0)

2020 ◽  
Author(s):  
Chen Yu ◽  
Zhenhong Li
Keyword(s):  
Time Series ◽  
Atmospheric Correction ◽  
Tropospheric Delay ◽  
Online Service ◽  
Atmospheric Effect ◽  
Automatic Data ◽  
Gps Network ◽  
Weather Model ◽  
New Generation ◽  
Globally Distributed

<div>The tremendous development of InSAR missions (e.g., Sentinel-1A/1B, ALOS-2, TerraSAR-X/TanDEM-X, COSMO-SkyMED, RADARSAT-2, and Gaofen-3) in recent years facilitates the study of smaller amplitude ground deformation using longer time series and over greater spatial scales. This poses new challenges for correcting interferograms for atmospheric (tropospheric) effects especially the dominant long wavelength effect and the spatial-temporal correlated topographic related effect, resulting the atmospheric effect being distance-dependent with larger interferograms experiencing greater contamination and preventing deformation mapping of large scales deformation phenomena such as inter-seismic tectonic strain accumulation, post-seismic relaxation of fault systems and Glacial Isostatic Adjustment (GIA). </div><div> </div><div>To overcome this, we have released the Generic Atmospheric Correction Online Service (GACOS) whose notable features comprise: (i) global coverage, (ii) all-weather, all-time usability, (iii) correction maps available in near real-time, and (iv) indicators to assess the correction performance and feasibility. The model applies operational high resolution ECMWF data (0.125-degree grid, 137 vertical levels, 6-hour interval) using an iterative tropospheric decomposition model and its performance for InSAR atmospheric correction was tested using globally-distributed interferograms, encompassing both flat and mountainous topographies, mid-latitude and near-polar regions, monsoon and oceanic climate systems, achieving a phase precision and displacement accuracy of approximately 1 cm for the corrected interferograms. Indicators describing the model’s performance including (i) ECMWF cross-RMS, (ii) phase-delay correlations, (iii) ECMWF time differences, and (iv) topography variations, were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied. </div><div> </div><div>To further improve the performance of GACOS to better serve the InSAR community, a new generation (GACOS 2.0) is being developed by: (i) improving the temporal resolution by integrating the newly published 1-hour ERA-5 weather model and the 5-minute GPS tropospheric delay estimates; (ii) developing an API system to facilitate automatic data processing; and (iii) enhancing GACOS based on regional/local datasets (such as national weather model and regional GPS network). The ERA-5 product and global GPS tropospheric delay estimates are carefully validated in order to achieve a robust integration. Based on the globally distributed GPS network and the MODIS PWV product, the performance of GACOS 2.0 in different regions of the world is evaluated with its elevation and latitude dependency being concluded which could be served as another performance indicator. All these features will contribute to a simplified time series analysis method (i.e. relying less on spatial-temporal filters) to reduce the computational burden, provided that the majority of the atmospheric error has been mitigated by GACOS 2.0. </div><div> </div>

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