scholarly journals Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

2021 ◽  
Vol 13 (22) ◽  
pp. 4670
Author(s):  
Fangjia Dou ◽  
Xiaolei Lv ◽  
Huiming Chai
Keyword(s):  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Deformation Rate ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Numerical Weather ◽  
Atmospheric Delay ◽  
Aperture Radar

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

scholarly journals Manual and Semiautomated Wind Direction Editing for Use in the Generation of Synthetic Aperture Radar Wind Speed Imagery

2007 ◽  
Vol 46 (6) ◽  
pp. 776-790 ◽  
Author(s):  
George S. Young ◽  
Todd D. Sikora ◽  
Nathaniel S. Winstead
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Wind Direction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Synthetic Aperture ◽  
Numerical Weather ◽  
Sar Imagery ◽  
Aperture Radar

Abstract Previous studies have demonstrated that satellite synthetic aperture radar (SAR) can be used as an accurate scatterometer, yielding wind speed fields with subkilometer resolution. This wind speed generation is only possible, however, if a corresponding accurate wind direction field is available. The potential sources of this wind direction information include satellite scatterometers, numerical weather prediction models, and SAR itself through analysis of the spatial patterns caused by boundary layer wind structures. Each of these wind direction sources has shortcomings that can lead to wind speed errors in the SAR-derived field. Manual and semiautomated methods are presented for identifying and correcting numerical weather prediction model wind direction errors. The utility of this approach is demonstrated for a set of cases in which the first-guess wind direction data did not adequately portray the features seen in the SAR imagery. These situations include poorly resolved mesoscale phenomena and misplaced synoptic-scale fronts and cyclones.

scholarly journals Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

2021 ◽  
Author(s):  
Matthias Aichinger-Rosenberger ◽  
Natalia Hanna ◽  
Robert Weber
Keyword(s):  
Numerical Weather Prediction ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Global Navigation Satellite Systems ◽  
Single Frequency ◽  
Tropospheric Delay ◽  
Global Networks ◽  
Numerical Weather ◽  
Station Coordinates ◽  
Nwp Model

<p>Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth’s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).</p><p>This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting. </p>

scholarly journals Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

2021 ◽  
Vol 13 (12) ◽  
pp. 2258
Author(s):  
Nikolaos Roukounakis ◽  
Panagiotis Elias ◽  
Pierre Briole ◽  
Dimitris Katsanos ◽  
Ioannis Kioutsioukis ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Weather Prediction ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Phase Gradient ◽  
Gulf Of Corinth ◽  
Spatial Coverage ◽  
Gnss Measurements ◽  
Aperture Radar

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.

scholarly journals Developing new multi-image InSAR methods for atmospheric delay estimation

2017 ◽  
Author(s):  
Αρλίντα Σακελλάρη
Keyword(s):  
Synthetic Aperture Radar ◽  
Digital Elevation Model ◽  
Delay Estimation ◽  
Synthetic Aperture ◽  
Atmospheric Delay ◽  
Digital Elevation ◽  
Elevation Model ◽  
Aperture Radar

Η διατριβή έχει ως στόχο να καθορίσει τίς βέλτιστες μεθόδους επεξεργασίας των απεικονίσεων Ραντάρ Συνθετικού Ανοίγματος (Synthetic Aperture Radar) (SAR) με σκοπό να παράγονται κατά το δυνατόν αξιόπιστα συμβολομετρικά προϊόντα. Η βέλτιστη επεξεργασία περιλαμβάνει τόσο βελτίωση της ποιότητας των απεικονίσεων SAR καθώς και των ενδιάμεσων προϊόντων τα οποία παράγονται κατά την συμβολομετρική διαδιακασία, όσο και την ανάπτυξη μεθοδολογιών για την ακριβή εκτίμηση όλων των συνιστωσών της συμβολομετρικής φάσης, θεωρώντας και την καθυστέρηση λόγω ατμόσφαιρας ως μια συνιστώσα της συμβολομετρικής φάσης. Ιδιαίτερη έμφαση έχει δοθεί στην εκτίμηση αυτής της συνιστώσας και ιδιαίτερα των επιδράσεων της τροπόσφαιρας, θεωρώντας ότι η συνιστώσα της μετατόπισης παρουσιάζεται σε περιορισμένο αριθμό εφαρμογών σε σχέση με αυτήν της ατμόσφαιρας η οποία είναι μία από τις κύριες αιτίες σφάλματος κατά την εκτίμηση του υψομέτρου με συμβολομετρία και διαφορική συμβολομετρία. Γι αυτό το λόγο, καθώς και για το γεγονός ότι οι μέθοδοι που έχουν αναπτυχθεί δεν βασίζονται στην αφαίρεση γνωστού Ψηφιακού Μοντέλου Εδάφους (Digital Elevation model) (DEM), οι μέθοδοι αυτές περιγράφονται καλύτερα από τον όρο «μέθοδοι Συμβολομετρίας πολλαπλών απεικονίσεων SAR» σε σχέση με τον ήδη υπάρχοντα όρο της διαφορικής Συμβολομετρίας.

scholarly journals Applied Research on InSAR and GPS Data Fusion in Deformation Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ziwen Zhang ◽  
Xuelian Wang ◽  
Yongdong Wu ◽  
Zengpeng Zhao ◽  
Yang E
Keyword(s):  
Data Fusion ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Inverse Distance Weighting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Gps Data ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Aperture Radar

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

scholarly journals Beating the Uncertainties: Ensemble Forecasting and Ensemble-Based Data Assimilation in Modern Numerical Weather Prediction

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Atmospheric Flow ◽  
Numerical Weather ◽  
Recent Developments ◽  
Numerical Weather Forecasting ◽  
Chaotic Nature

Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble-based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble-based data assimilation. The usefulness of these methods in improving high-impact weather forecasting is also discussed.

Forming of the products of non-hydrostatic modelling systems for short-range weather prediction COSMO-RuBy (Hydrometcentre of Russia) и WRF-ARW (Belhydromet)

2021 ◽  
pp. 6-29
Author(s):  
I.А. Rozinkina ◽  
◽  
G.S . Rivin ◽  
R.N. Burak ◽  
Е.D. Аstakhova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Numerical Weather Prediction ◽  
Web Site ◽  
Short Range ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Grid Spacing ◽  
Numerical Weather ◽  
Prediction Systems

The paper considers the results of activities on the development of output products for the non-hydrostatic short-range numerical weather prediction systems: COSMO-RuBy with a grid spacing of 2.2 km at the Hydrometcentre of Russia and WRF-ARW with a grid spacing of 3 km in Belhydromet. The important results of the activities are the organization of the exchange of unified products between the countries and the development at the Hydrometcentre of Russia of two technologies for obtaining the unified products: the multi-model lagged ensemble system and the system for the complex correction based on machine learning of model results. A specialized web-site providing convenient work of forecasters with the COSMO-RuBy results and unified products was created at the Hydrometcentre of Russia based on the feedback from forecasters. The systems of common visualization and verification of COSMO-RuBy and WRF-ARW results are implemented in Belhydromet. Keywords: numerical weather prediction, ensemble forecasting, visualization, machine learning

scholarly journals Background Tropospheric Delay in Geosynchronous Synthetic Aperture Radar

2020 ◽  
Vol 12 (18) ◽  
pp. 3081
Author(s):  
Dexin Li ◽  
Xiaoxiang Zhu ◽  
Zhen Dong ◽  
Anxi Yu ◽  
Yongsheng Zhang
Keyword(s):  
Synthetic Aperture Radar ◽  
Meteorological Parameters ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Imaging Method ◽  
Imaging Geometry ◽  
Long Time ◽  
Spatially Variant ◽  
To Receive ◽  
Aperture Radar

Spaceborne synthetic aperture radar (SAR) has been treated as a weather independent system for a long time. However, with the development of advanced SAR configurations, e.g., high resolution, bistatic, geosynchronous (GEO), the influence of tropospheric propagation error, which strongly depends on the weather, has begun to receive attention. In this paper, we focus on the effect of deterministic background tropospheric delay (BTD) during the image formation of GEO SAR. First, the decorrelation problems caused by the spatial variation and BTD are presented. Second, by combining with the SAR imaging geometry, the BTD error is decomposed as constant error, spatially variant error, and time variant error, the influences of which are analyzed under different circumstances. Third, an imaging method starting from the meteorological parameters and the GEO SAR systematic parameters is proposed to deal with the decorrelation problems. Finally, simulations with the dot-matrix targets are performed to validate the imaging method.

scholarly journals Feasibility of Replacing the Range Doppler Equation of Spaceborne Synthetic Aperture Radar Considering Atmospheric Propagation Delay with a Rational Polynomial Coefficient Model

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 553
Author(s):  
Shasha Hou ◽  
Yuancheng Huang ◽  
Guo Zhang ◽  
Ruishan Zhao ◽  
Peng Jia
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Polynomial Coefficient ◽  
Positioning Accuracy ◽  
Atmospheric Delay ◽  
Atmospheric Propagation ◽  
Rational Polynomial ◽  
Sar Imaging ◽  
Delay Correction ◽  
Aperture Radar

Usually, the rational polynomial coefficient (RPC) model of spaceborne synthetic aperture radar (SAR) is fitted by the original range Doppler (RD) model. However, the radar signal is affected by two-way atmospheric delay, which causes measurement error in the slant range term of the RD model. In this paper, two atmospheric delay correction methods are proposed for use in terrain-independent RPC fitting: single-scene SAR imaging with a unique atmospheric delay correction parameter (plan 1) and single-scene SAR imaging with spatially varying atmospheric delay correction parameters (plan 2). The feasibility of the two methods was verified by conducting fitting experiments and geometric positioning accuracy verification of the RPC model. The experiments for the GF-3 satellite were performed by using global meteorological data, a global digital elevation model, and ground control data from several regions in China. The experimental results show that it is feasible to use plan 1 or plan 2 to correct the atmospheric delay error, no matter whether in plain, mountainous, or plateau areas. Moreover, the geometric positioning accuracy of the RPC model after correcting the atmospheric delay was improved to better than 3 m. This is of great significance for the efficient and high-precision geometric processing of spaceborne SAR images.

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