Seasonal global mean sea level change from satellite altimeter, GRACE, and geophysical models

Chen, Jianli; Wilson, Clark R.; Tapley, Byron D; Famiglietti, J. S.; Rodell, Matthew

doi:10.1007/s00190-005-0005-9

Cited by 73 publications

(51 citation statements)

References 28 publications

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“…(4) Despite the previous correction and updates, the freshwater budget is far from balanced. In order to avoid any mean sea-surface-height drift due to the poor water budget closure, the surface freshwater budget is set to zero in IRG DEV at each time step with a superimposed seasonal cycle (Chen et al, 2005). It helps to reduce errors in SLA assimilation.…”

Section: Updates For Forthcoming Myocean Systemsmentioning

confidence: 99%

Evaluation of global monitoring and forecasting systems at Mercator Océan

et al. 2013

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The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters) and data sources (satellite sea surface temperature) have as great an impact as the system design (model physics and assimilation parameters). The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007-2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean dynamic topography corrects local biases in the Indonesian Throughflow and in the western tropical Pacific. This improves also the subsurface currents at the Equator. The global systems give an accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 • C and 0.2 psu. The assimilation of an improved sea surface temperature product aims to better represent the sea ice concentration and the sea ice edge. The systems under development are still suffering from a drift which can only be detected by means of a 5-yr hindcast, preventing us from upgrading them in real time. This emphasizes the need to pursue research while building future systems for MyOcean2 forecasting.

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Section: Updates For Forthcoming Myocean Systemsmentioning

confidence: 99%

Evaluation of global monitoring and forecasting systems at Mercator Océan

et al. 2013

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“…For filling of missing months, linear interpolation was used according to Landerer and Swenson [28]. Specific gridded files include a monthly, [36]. The degree-1 coefficients (Geocenter) are estimated using the method from Swenson, Chambers, and Whar.…”

Section: Tws From Gracementioning

confidence: 99%

Evaluation of Groundwater Storage Variations in Northern China Using GRACE Data

Yin

Jiao

2017

Geofluids

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Dynamic change of groundwater storage is one of the most important topics in the sustainable management of groundwater resources. Groundwater storage variations are firstly isolated from the terrestrial water storage change using the Global Land Data Assimilation System (GLDAS). Two datasets are used: (1) annual groundwater resources and (2) groundwater storage changes estimated from point-based groundwater level data in observation wells. Results show that the match between the GRACE-derived groundwater storage variations and annual water resources variation is not good in six river basins of Northern China. However, it is relatively good between yearly GRACE-derived groundwater storage data and groundwater storage change dataset in Huang-HuaiHai Plain and the Song-Liao Plain. The mean annual depletion rate of groundwater storage in the Northern China was approximately 1.70 billion m 3 yr −1 from 2003 to 2012. In terms of provinces, the yearly depletion rate is higher in Jing-Jin-Ji (Beijing, Tianjin, and Hebei province) and lowest in Henan province from 2003 to 2012, with the rate of 0.70 and 0.21 cm yr −1 Equivalent Water Height (EWH), respectively. Different land surface models suggest that the patterns from different models almost remain the same, and soil moisture variations are generally bigger than snow water equivalent variations.

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“…Error sources not foreseen before launch have impacted the effective resolution, so that based on the analysis of Swenson and Wahr (2006b), the figure may be closer to 500,000 km 2 , if an optimized data filtering and smoothing technique is used. Many studies are now demonstrating the value of GRACE to hydrological research and applications (e.g., Rodell et al 2004b;Chen et al 2005b;Syed et al 2005;Velicogna et al 2005;Swenson and Wahr 2006a). This paper presents a case study of the application of GRACE to the estimation of groundwater storage variability in the Mississippi River basin, USA.…”

Section: Introductionmentioning

confidence: 99%