RL05 monthly and 10-day gravity field solutions from CNES/GRGS

2020 ◽  
Author(s):  
Jean-Michel Lemoine ◽  
Stéphane Bourgogne
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Time Span ◽  
Full Time ◽  
Accelerometer Data ◽  
Times Series ◽  
Grace Data ◽  
Range Rate ◽  
Combined Time Series ◽  
Recent Version

<p>In February 2020 CNES/GRGS published its 5th reprocessing (called "RL05") of the GRACE data, from August 2002 to August 2016. The extension of this series covering the span of the GRACE-FO data, 2018-now, will be released in October. This new times series comes, as for the previous releases, in a monthly and a 10-day time resolution. </p> <p>The main differences of the new release with respect to the previous one are :<br />- the use of the most recent version of the JPL KBR data (version 3),<br />- the use of the IGS orbits and clocks in replacement of the GRGS ones,<br />- a completely homogenous processing over the full time span,<br />- the use of AOD1B-RL06 for the dealiasing data in replacement of the ERA-Interim+TUGO products.</p> <p>For the processing of the GRACE-FO data we have used the TUGRAZ transplant accelerometer data instead of the JPL one, resulting in a great stabilization of the accelerometer scale factors, now close to 1 over the full time span, in a reduction of the range-rate and GPS residuals and in an improvement of the gravity field solutions.</p> <p>This presentation will focus on the processing details and on the comparison of this new series with the Release 06 from JPL / GFZ / CSR, the ITSG-Grace2018 time series from TUGRAZ, and the combined time series from the new international combination service COST-G.</p>

scholarly journals GRACETOOLS—GRACE Gravity Field Recovery Tools

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 350 ◽  
Author(s):  
Neda Darbeheshti ◽  
Florian Wöske ◽  
Matthias Weigelt ◽  
Christopher Mccullough ◽  
Hu Wu
Keyword(s):  
Open Source ◽  
Gravity Field ◽  
Transition Matrix ◽  
State Transition ◽  
Satellite Observations ◽  
Gravity Field Recovery ◽  
Grace Data ◽  
Range Rate ◽  
Analysis Platform ◽  
Recovery Tools

This paper introduces GRACETOOLS, the first open source gravity field recovery tool using GRACE type satellite observations. Our aim is to initiate an open source GRACE data analysis platform, where the existing algorithms and codes for working with GRACE data are shared and improved. We describe the first release of GRACETOOLS that includes solving variational equations for gravity field recovery using GRACE range rate observations. All mathematical models are presented in a matrix format, with emphasis on state transition matrix, followed by details of the batch least squares algorithm. At the end, we demonstrate how GRACETOOLS works with simulated GRACE type observations. The first release of GRACETOOLS consist of all MATLAB M-files and is publicly available at Supplementary Materials.

scholarly journals Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

2017 ◽  
Author(s):  
Christina Lück ◽  
Jürgen Kusche ◽  
Roelof Rietbroek ◽  
Anno Löcher
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Sea Level ◽  
Time Variable ◽  
Field Analysis ◽  
Time Variable Gravity ◽  
Ocean Mass ◽  
Grace Data ◽  
Swarm Satellites ◽  
Variable Gravity

Abstract. Measuring the spatiotemporal variation of ocean mass allows one to partition volumetric sea level change, sampled by radar altimeters, into a mass-driven and a steric part, the latter being related to ocean heat change and the current Earth’s energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission provides estimates of the Earth’s time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites; i.e. extending the GRACE time series. Here we utilize data from the European Space Agency’s (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. We investigate the potential to bridge the gap between the GRACE missions and to substitute missing monthly solutions. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating trend, annual and semiannual signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, for 80.0 % of all investigated cases of an 18-months-gap, Swarm ocean mass was found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modelling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including non-dedicated satellites.

Can daily GRACE gravity field models be used to evaluate short-term hydro-meteorological signals over the continents?

2020 ◽  
Author(s):  
Annette Eicker ◽  
Laura Jensen ◽  
Viviana Wöhnke ◽  
Andreas Kvas ◽  
Henryk Dobslaw ◽  
...  
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Time Scales ◽  
Water Flux ◽  
Water Storage ◽  
Numerical Differentiation ◽  
Data Set ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gravity Field Models

<p>Over the recent years, the computation of temporally high-resolution (daily) GRACE gravity field solutions has advanced as an alternative to the processing of monthly models. In this presentation we will show that recent processing improvements incorporated in the latest version of daily gravity field models (ITSG-Grace2018) now allow for the investigation of water flux signals on the continents down to time scales of a few days.</p><p>Time variations in terrestrial water storage derived from GRACE can be related to atmospheric net-fluxes of precipitation (P), evapotranspiration (E) and lateral runoff (R) via the terrestrial water balance equation, which makes GRACE a new and completely independent data set for constraining hydro-meteorological observations and the output of atmospheric reanalyses.</p><p>In our study, band-pass filtered water fluxes are derived from the daily GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days. We can show that on these time scales GRACE is able to identify quality differences between different global reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim.</p><p>We can further demonstrate that only the very recent progress in GRACE data processing has enabled the use of daily GRACE time series for such an evaluation of high-frequency atmospheric fluxes. The accuracy of the previous daily GRACE time series ITSG-Grace2016 would not have been sufficient to carry out such an assessment.</p>

scholarly journals Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

Solid Earth ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 323-339 ◽  
Author(s):  
Christina Lück ◽  
Jürgen Kusche ◽  
Roelof Rietbroek ◽  
Anno Löcher
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Sea Level ◽  
Time Variable ◽  
Time Variable Gravity ◽  
Ocean Mass ◽  
Grace Data ◽  
Swarm Satellites ◽  
Gravity Fields ◽  
Variable Gravity

Abstract. Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including satellites not dedicated to gravity field studies.

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

An essay on ‘combined’ time series processes

2000 ◽  
Vol 19 (1) ◽  
pp. 77-93 ◽  
Author(s):  
Christopher Wlezien
Keyword(s):  
Time Series ◽  
Combined Time Series

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

Correlating geologic and seismic data with unconventional resource production curves using machine learning

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. O39-O47 ◽  
Author(s):  
Ryan Smith ◽  
Tapan Mukerji ◽  
Tony Lupo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Oil And Gas ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Full Time ◽  
Data Types ◽  
Production Time ◽  
Well Production ◽  
Unconventional Resource

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data. We are then able to predict the well-production time series, with 65%–76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.

scholarly journals Pengaruh Analisis Faktor Fundamental Terhadap Return Saham

2011 ◽  
Vol 2 (2) ◽  
pp. 85
Author(s):  
Perwito Perwito
Keyword(s):  
Time Series ◽  
Cross Sectional ◽  
Dividend Payout ◽  
Data Pooling ◽  
Return On Equity ◽  
Book Value ◽  
Times Series ◽  
Ex Post ◽  
Ratio Size ◽  
Ex Post Facto

Krisis yang terjadi pada tahun 2008 sangat mempengaruhi kinerja perusahaan-perusahaan yang terdaftar di Bursa Efek Indonesia, hal ini terlihat dengan menurunnya harga saham. Menurunnya harga saham tersebut tentunya akan berimplikasi pada return yang didapatkan oleh investor. Penelitian mengkaji dan menganalisis faktor-faktor fundamental terhadap return saham. Jenis dan sifat penelitian ini adalah ex post facto dan survey explanatory, adapun metode penelitian yang digunakan adalah metode yang bersifat deskriptif, komparatif, asosiatif, dan juga verifikatif. Variabel yang dianalisis terdiri dari; Variabel terikat (Y), dalam hal ini adalah return saham, sedangkan variabel bebas yang terdiri dari return on equity (ROE), earning per share (EPS), price earning ratio (PER), price book value (PBV), dan tingkat suku bunga. Populasi dalam penelitian ini terdiri dari perusahaan kelompok Industri Barang Konsumsi dan Keuangan yang terdaftar di Bursa Efek Indonesia periode 2002 s.d 2009 yang terdiri dari 31 perusahaan untuk kelompok industri barang konsumsi, dan 44 perusahaan pada kelompok keuangan. Data yang dianalisis merupakan gabungan antara data time series dan cross sectional, atau biasa disebut data pooling atau pooled times series, dengan 429 data sampel penelitian. Hasil penelitian menunjukkan, pertama; terdapat perbedaan return saham antara kelompok Industri Barang Konsumsi dan Keuangan, rata-rata total return saham yang dihasilkan oleh kelompok Keuangan relatif lebih besar jika dibandingkan dengan rata-rata return saham dari kelompok Industri Barang Konsumsi, hal tersebut mengindikasikan bahwa masing-masing kelompok industri memiliki return dan pertumbuhan yang berbeda-beda. Kedua; hasil penelitian ini menjelaskan bahwa nilai r sebesar 0,387 dan R² sebesar 0,1498, hal ini berarti pengaruh faktor fundamental terhadap return saham sebesar 14,98%, dan sisanya sebesar 85,02% dipengaruhi oleh faktor lain yang tidak dijelaskan dalam penelitian ini seperti return on asset, dividend dan dividend payout ratio, size, serta beta fundamental. Sehingga dapat disimpulkan secara simultan atau secara bersama-sama bahwa analisis faktor fundamental dapat digunakan untuk memprediksikan return saham pada perusahaan kelompok Industri Barang Konsumsi dan Keuangan. Sedangkan secara parsial hanya EPS berkontribusi paling kuat yakni 9,12%.

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