Calibration in the MBW of simulated GOCE gradients aided by ground data M. Veicherts, C. C. Tscherning, Niels Bohr Institute, University of Copenhagen,

Slides:



Advertisements
Similar presentations
Shapelets Correlated with Surface Normals Produce Surfaces Peter Kovesi School of Computer Science & Software Engineering The University of Western Australia.
Advertisements

Running a model's adjoint to obtain derivatives, while more efficient and accurate than other methods, such as the finite difference method, is a computationally.
11/11/02 IDR Workshop Dealing With Location Uncertainty in Images Hasan F. Ates Princeton University 11/11/02.
Experimental Design, Response Surface Analysis, and Optimization
Design and Simulation of a Novel MEMS Dual Axis Accelerometer Zijun He, Advisor: Prof. Xingguo Xiong Department of Electrical and Computer Engineering,
Advantages of Decorrugation of Aeromagnetic Data using the Naudy-Fuller Space Domain Filter Saad Mogren (King Saud University, Saudi Arabia) and Derek.
« هو اللطیف » By : Atefe Malek. khatabi Spring 90.
IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010.
GG450 April 22, 2008 Seismic Processing.
Master thesis by H.C Achterberg
Motion Detection And Analysis Michael Knowles Tuesday 13 th January 2004.
Digital Voice Communication Link EE 413 – TEAM 2 April 21 st, 2005.
Optimized Numerical Mapping Scheme for Filter-Based Exon Location in DNA Using a Quasi-Newton Algorithm P. Ramachandran, W.-S. Lu, and A. Antoniou Department.
Principles of Sea Level Measurement Long-term tide gauge records  What is a tide station?  How is sea level measured relative to the land?  What types.
RESEARCH POSTER PRESENTATION DESIGN © This research is based on the estimation of the spherical harmonic geopotential.
1 Seventh Lecture Error Analysis Instrumentation and Product Testing.
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
ECE 480 Wireless Systems Lecture 14 Problem Session 26 Apr 2006.
Two and a half problems in homogenization of climate series concluding remarks to Daily Stew Ralf Lindau.
EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Error Analysis of the NGS Gravity Database Jarir Saleh, Xiaopeng Li, Yan Ming Wang, Dan Roman and Dru Smith, NOAA/NGS/ERT Paper: G , 04 July 2011,
AN ITERATIVE METHOD FOR MODEL PARAMETER IDENTIFICATION 4. DIFFERENTIAL EQUATION MODELS E.Dimitrova, Chr. Boyadjiev E.Dimitrova, Chr. Boyadjiev BULGARIAN.
1 Hybrid methods for solving large-scale parameter estimation problems Carlos A. Quintero 1 Miguel Argáez 1 Hector Klie 2 Leticia Velázquez 1 Mary Wheeler.
Institut für Erdmessung (IfE), Leibniz Universität Hannover, Germany Quality Assessment of GOCE Gradients Phillip Brieden, Jürgen Müller living planet.
Measures of Variability In addition to knowing where the center of the distribution is, it is often helpful to know the degree to which individual values.
Secular variation in Germany from repeat station data and a recent global field model Monika Korte and Vincent Lesur Helmholtz Centre Potsdam, German Research.
Baseband Demodulation/Detection
Effect of Noise on Angle Modulation
Modern Navigation Thomas Herring MW 11:00-12:30 Room
Geo479/579: Geostatistics Ch4. Spatial Description.
On the reliability of using the maximum explained variance as criterion for optimum segmentations Ralf Lindau & Victor Venema University of Bonn Germany.
Evapotranspiration Estimates over Canada based on Observed, GR2 and NARR forcings Korolevich, V., Fernandes, R., Wang, S., Simic, A., Gong, F. Natural.
3.7 Adaptive filtering Joonas Vanninen Antonio Palomino Alarcos.
Regional Enhancement of the Mean Dynamic Topography using GOCE Gravity Gradients Matija Herceg 1 and Per Knudsen 1 1 DTU Space, National Space Institute,
Higher National Certificate in Engineering Unit 36 –Lesson 4 – Parameters used to Describe the Normal Distribution.
International Symposium on Gravity, Geoid and Height Systems GGHS 2012, Venice, Italy 1 GOCE data for local geoid enhancement Matija Herceg Per Knudsen.
IAG Scientific Assembly – Cairns, Australia, August 2005 The GOCE Mission GOCE (Gravity field and steady-state Ocean Circulation Explorer) will be.
Full Resolution Geoid from GOCE Gradients for Ocean Modeling Matija Herceg & Per Knudsen Department of Geodesy DTU Space living planet symposium 28 June.
C.C.Tscherning, Niels Bohr Institute, University of Copenhagen. Improvement of Least-Squares Collocation error estimates using local GOCE Tzz signal standard.
Bouman et al, GOCE Gravity Gradients, ESA Living Planet Symposium 2010 GOCE Gravity Gradients in Instrument and Terrestrial Frames J. Bouman, Th. Gruber,
CHAPTER 2.3 PROBABILITY DISTRIBUTIONS. 2.3 GAUSSIAN OR NORMAL ERROR DISTRIBUTION  The Gaussian distribution is an approximation to the binomial distribution.
1 1 Chapter 6 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing.
Electron density profile retrieval from RO data Xin’an Yue, Bill Schreiner  Abel inversion error of Ne  Data Assimilation test.
The joint influence of break and noise variance on break detection Ralf Lindau & Victor Venema University of Bonn Germany.
Inverse Barometer correction comparison for Envisat mission between Corrections based on JRA-55 and ERA-Interim atmospheric reanalyses The JRA-55 correction.
Comparison of filters for burst detection M.-A. Bizouard on behalf of the LAL-Orsay group GWDAW 7 th IIAS-Kyoto 2002/12/19.
ESA Living Planet Symposium 28 June - 2 July 2010, Bergen, Norway A. Albertella, R. Rummel, R. Savcenko, W. Bosch, T. Janjic, J.Schroeter, T. Gruber, J.
Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation Dah-Chung Chang* and Wen-Rong Wu, Member, IEEE IEEE TRANSACTIONS.
4.Results (1)Potential coefficients comparisons Fig.3 FIR filtering(Passband:0.005~0.1HZ) Fig.4 Comparison with ESA’s models (filter passband:0.015~0.1HZ)
The OC in GOCE: A review The Gravity field and Steady-state Ocean Circulation Experiment Marie-Hélène RIO.
Bouman et al, GOCE Calibration, ESA Living Planet Symposium 2010, Bergen, Norway Overview of GOCE Gradiometer Cal/Val Activities J. Bouman, P. Brieden,
Date of download: 6/22/2016 Copyright © 2016 SPIE. All rights reserved. Schematic representation of the near-infrared (NIR) structured illumination instrument,
ESA Living Planet Symposium, 29 June 2010, Bergen (Norway) GOCE data analysis: the space-wise approach and the space-wise approach and the first space-wise.
WLTP-DHC Analysis of in-use driving behaviour data, influence of different parameters By Heinz Steven
Yun, Hyuk Jin. Theory A.Nonuniformity Model where at location x, v is the measured signal, u is the true signal emitted by the tissue, is an unknown.
High Resolution Weather Radar Through Pulse Compression
Aerodynamic Attitude Control for CubeSats
Digital Control Systems Waseem Gulsher
FLUCTUATIONS OF MUON ENERGY LOSSES
D. Rieser *, R. Pail, A. I. Sharov
6.2 Grid Search of Chi-Square Space
EE513 Audio Signals and Systems
Virtual University of Pakistan
Paige Thielen, ME535 Spring 2018
9.4 Enhancing the SNR of Digitized Signals
Inferential Statistics
CHAPTER – 1.1 UNCERTAINTIES IN MEASUREMENTS.
Volume 86, Issue 3, Pages (March 2004)
Erika J Mancini, Felix de Haas, Stephen D Fuller  Structure 
Department of Physics and Astronomy, University of Louisville, KY, USA
Presentation transcript:

Calibration in the MBW of simulated GOCE gradients aided by ground data M. Veicherts, C. C. Tscherning, Niels Bohr Institute, University of Copenhagen, Denmark J. Bouman, SRON National Institute for Space Research, Utrecht The Netherlands Data Processing Flowchart Abstract. The GOCE gravity gradients will be calibrated using external gravity field data. While the GOCE gradients will have the minimal noise level within the Measurement Band-Width (MBW) from 5 to 100 mHz, they suffer from a 1/f error below the MBW. It is therefore required to band-pass filter the gradients to suppress the 1/f error on the one hand, while keeping the signal in and possibly below the MBW. The gradients are initially calibrated using a global gravity field model (GGM). Both time series of GOCE gradients and GGM derived gradients are identically filtered and scale factors for each component are determined. In order to verify this calibration result, the GGM has been enhanced using gradients computed by combining the model with ground gravity in four regions with good quality gravity data. Scale factors were determined for the gradients on each track passing through the area, and it could be concluded that the scale factors could be determined track-wise with results not significantly different from those obtained using the GGM only. Using 1 month of 1 s simulated data we have tested different procedures for extracting data in the MBW. Gradient Examples The accuracy of the SF determination naturally depends on the number of GOCE measurement points or maybe rather the ‘length’ of the crossing. In the figure below the scale factors are plotted against the number of points in the crossing and improvement is obvious as the number of points increase untill a certain point where the ‘minimum’ conditions for the estimation are fulfilled. However it is equally obvious that the quality of the scale factor estimates decrease significantly with only a small decrease of the number of points compared to the number of points in a ‘full’ crossing. Gradient Validation Areas Estimated Scale factors Figure 1: Time series of "true" and measured VXX GG. Figure 2: PSD of VXX error and signal. The areas used for validation of the gradient calibration are selected on the basis of gravity smoohtness, - although one area is from a more ‘ruggy’ region (Norway), for the geographical distribution, and naturally for the data availability. The gravity anomalies in the areas used have an estimated error below 5 mE (1 mE=10-12 s-2), and extend of maximally 10 degrees in latitude and 18 in longitude, corresponding to an approximate square with side lengths of 1100 km. The areas are shown below. Scale Factor Estimation Uncertainty The result of the scale factor estimation depends on several ‘configurable parameters’ such as filter type (we have tried using Butterworth, Chebyshev, Elliptic, Squarish filter with spline interpolation, - all with comparatively equal and in general good results), varying bandpass limits, varying the length of the data series, and more. All the results from these investigations are not presented in this poster, but below the results from using the latter filter are shown. Another factor of uncertainty is the influence of the a priori error of the gravity gradients. The plot below shows the direct connection between the gravity gradients a priori error and the scale factor error estimates. The procedure with the use of the data in the MWB has been successfully used despite the obvious non-stationarity of the long-periodic “noise”. This ‘MWB method’ requires data to be received in time-series without gaps, or alternatively the gradient data must be split and scale factor estimation must be derived from separate series. Considering the quick degradation of the scale factor estimates when the length of the area crossing track becomes shorter, it will be examined to extend the size of the areas used. Conclusion A challenge in this comparison is the 1/f behavior of the GOCE GG errors for low frequencies. As an example, see Figure 1, where the time series of 1 day are plotted for the measured VXX and the error free VXX. Clearly a large, slowly varying error is superimposed on the true GG. The PSD of this error as well as of the true signal are plotted in Figure 2. The error shows a 1/f behaviour for low frequencies. The signal has most of its power at 1 and 2 CPR (cycles per revolution) and is above the error power for these frequencies. The error starts to dominate the signal for frequencies below 1 CPR. Figure 9: Flowchart of the processing chain. Figure 4: The Australian area with GOCE crossing tracks and measurement points Figure 3: Global map showing the selected cal/val areas Figure 10: SF estimate dependency of number of cross track points Figure 11: The amount of GG outliers as function of GG a priori error. Figure 5: Scale factor estimates from Australian region, Vxx Figure 6: Scale factor estimates from Central Scandinavian region, Vxx Figure 7: Scale factor estimates from Canadian area, Vxx Figure 8: Scale factor estimates from Norwegian area, Vxx