scholarly journals Improved Estimation of Regional Surface Mass Variations from GRACE Intersatellite Geopotential Differences Using a Priori Constraints

2020 ◽  
Vol 12 (16) ◽  
pp. 2553
Author(s):  
Bo Zhong ◽  
Qiong Li ◽  
Jianli Chen ◽  
Zhicai Luo ◽  
Hao Zhou
Keyword(s):  
A Priori ◽  
Energy Balance Equation ◽  
Hydrological Models ◽  
A Priori Information ◽  
Improved Method ◽  
Surface Mass ◽  
Range Rate ◽  
Priori Information ◽  
Gravity Recovery

We presented an improved method for estimation of regional surface mass variations from the Gravity Recovery and Climate Experiment (GRACE)-derived precise intersatellite geopotential differences using a priori constraints. An alternative analytic formula was proposed to incorporate the K-band ranging (KBR) range rate into the improved energy balance equation, and precise geopotential differences were estimated from GRACE Level-1B data based on the remove-compute-restore (RCR) technique, which avoids the long-wavelength gravity signals being absorbed by empirical parameters. To reduce the ill condition for inversion of regional mass variations from geopotential differences, a priori information from hydrological models was used to construct the constraint equations, and the optimal regularization parameters were adaptively determined based on iterative least-squares estimation. To assess our improved method, a case study of regional mass variations’ inversion was carried out over South America on 2° × 2° grids at monthly intervals from January 2005 to December 2010. The results show that regional mascon solutions inverted from geopotential differences estimated by the RCR technique using hydrological models as a priori constraints can retain more signal energy and enhance regional mass variation inversion. The spatial distributions and annual amplitudes of geopotential difference-based regional mascon solutions agree well with the official GRACE mascon solutions, although notable differences exist in spatial patterns and trends, especially in small basins. In addition, our improved method can robustly estimate the mascon solutions, which are less affected by the a priori information. The results from the case study have clearly demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals The bias in GRACE estimates of continental water storage variations

2006 ◽  
Vol 3 (6) ◽  
pp. 3557-3594 ◽  
Author(s):  
R. Klees ◽  
E. A. Zapreeva ◽  
H. C. Winsemius ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Southern Africa ◽  
A Priori ◽  
Water Storage ◽  
Target Area ◽  
Hydrological Models ◽  
A Priori Information ◽  
Basin Scale ◽  
Priori Information ◽  
Gravity Field Models

Abstract. The estimation of terrestrial water storage variations at river basin scale is among the best documented applications of the GRACE (Gravity and Climate Experiment) satellite gravity mission. In particular, it is expected that GRACE closes the water balance at river basin scale and allows the verification, improvement and modeling of the related hydrological processes by combining GRACE amplitude estimates with hydrological models' output and in-situ data. When computing monthly mean storage variations from GRACE gravity field models, spatial filtering is mandatory to reduce GRACE errors, but at the same time yields biased amplitude estimates. The objective of this paper is three-fold. Firstly, we want to compute and analyze amplitude and time behaviour of the bias in GRACE estimates of monthly mean water storage variations for several target areas in Southern Africa. In particular, we want to know the relation between bias and the choice of the filter correlation length, the size of the target area, and the amplitude of mass variations inside and outside the target area. Secondly, we want to know to what extent the bias can be corrected for using a priori information about mass variations. Thirdly, we want to quantify errors in the estimated bias due to uncertainties in the a priori information about mass variations that are used to compute the bias. The target areas are located in Southern Africa around the Zambezi river basin. The latest release of monthly GRACE gravity field models have been used for the period from January 2003 until March 2006. An accurate and properly calibrated regional hydrological model has been developed for this area and its surroundings and provides the necessary a priori information about mass variations inside and outside the target areas. The main conclusion of the study is that spatial smoothing significantly biases GRACE estimates of the amplitude of annual and monthly mean water storage variations. For most of the practical applications, the bias will be positive, which implies that GRACE underestimates the amplitudes. The bias is mainly determined by the filter correlation length; in the case of 1000 km smoothing, which is shown to be an appropriate choice for the target areas, the annual bias attains values up to 50% of the annual storage; the monthly bias is even larger with a maximum value of 75% of the monthly storage. A priori information about mass variations can provide reasonably accurate estimates of the bias, which significantly improves the quality of GRACE water storage amplitudes. For the target areas in Southern Africa, we show that after bias correction, GRACE annual amplitudes differ between 0 and 30 mm from the output of a regional hydrological model, which is between 0% and 25% of the storage. Annual phase shifts are small, not exceeding 0.25 months, i.e. 7.5 deg. Our analysis suggests that bias correction of GRACE water storage amplitudes is indispensable if GRACE is used to calibrate hydrological models.

scholarly journals An improved method using adaptive smoothing for GNSS tomographic imaging of ionosphere

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250613
Author(s):  
Rushang Jia ◽  
Xumin Yu ◽  
Jianping Xing ◽  
Yafei Ning ◽  
Hecheng Sun
Keyword(s):  
A Priori ◽  
Simulated Data ◽  
Reconstruction Technique ◽  
Electron Content ◽  
Observation Data ◽  
A Priori Information ◽  
Improved Method ◽  
Adaptive Smoothing ◽  
Total Electron ◽  
Priori Information

Global navigation satellite system (GNSS) is a well-established sensors in the recent ionosphere research. By comparing with classical meteorological equipments, the GNSS application can obtain more reliable and precious ionospheric total electron content (TEC) result. However, the most used GNSS ionospheric tomography technique is sensitive to a priori information due to the sparse and non-uniform distribution of GNSS stations. In this paper, we propose an improved method based on adaptive Laplacian smoothing and algebraic reconstruction technique (ALS-ART). Compared with traditional constant constraints, this method is less dependent on a priori information and adaptive smoothing constraints is closer to the actual situation. Tomography experiments using simulated data show that reconstruction accuracy of ionospheric electron density using ALS-ART method is significantly improved. We also use the method to do the analysis of real observation data and compare the tomography results with ionosonde observation data. The results demonstrate the superiority and reliability of the proposed method compared to traditional constant constraints method which will further improve the capability of obtaining precious ionosphere TEC by using GNSS.

BLIND SOURCE SEPARATION: AN APPLICATION TO THE MT. MERAPI VOLCANO, INDONESIA

2008 ◽  
Vol 08 (03n04) ◽  
pp. L249-L260 ◽  
Author(s):  
GIUSEPPE CABRAS ◽  
ROBERTO CARNIEL ◽  
JOACHIM WASSERMANN
Keyword(s):  
A Priori ◽  
Volcano Seismology ◽  
A Priori Information ◽  
Merapi Volcano ◽  
Volcanic Source ◽  
Study Support ◽  
Priori Information ◽  
Non Gaussian ◽  
Suitable Technique

Independent Component Analysis (ICA) is an emerging new technique in the blind identification of signals recorded in a variety of different fields. ICA tries to find the most statistically independent sources from an observable random vector, with the only restriction that all sources but at most one are non-Gaussian; no other a priori information on sources and mixing dynamic system are needed. The applications of these techniques to the analysis of volcanic time series are relatively few to date. In this paper we show that ICA is a suitable technique to separate a volcanic source component from ocean microseisms background noise in a seismic dataset recorded at the Mt. Merapi volcano, Indonesia. The encouraging results obtained with this methodology in the presented case study support their wider applicability in volcano seismology.

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