Comparative analysis of different environmental loading methods and their impacts on the GPS height time series

Jiang, Weiping; Li, Zhao; Dam, Tonie van; Ding, Wenwu

doi:10.1007/s00190-013-0642-3

Cited by 100 publications

(88 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quite different from the MERRA and GLDAS model, the NCEP-R-2 model yields very small STD value at most of places around the globe, while the STD of the NCEP-R-1 model is very large, especially for the central North America and Eurasia. Since our results on the NCEP-R-1 model is quite similar as that from (Jiang et al 2013), we only show the results from the NCEP-R-2 model only hereafter. The Fig.…”

Section: Comparison Of the Weekly Loading Time Series Between Modelsmentioning

confidence: 65%

“…To remove this environmental signal from the GPS data, the CWS mass models are required to predict surface displacements. However, these CWS model induced vertical surface displacement are often not consistent with one another and with the seasonal changes in the position of the GPS markers (Jiang et al 2013). Currently, the most frequent used CWS mass models are the soil moisture (SM) plus snow water equivalent (SWE) from the monthly Global Land Data Assimilation System (GLDAS) 1 (Rui 2011) and the six-hourly National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) global reanalysis datasets (R-1) (Kalnay et al 1996), with spatial resolutions as 1 ı 1 ı and 1:875 ı .1:8889 1:9048/ ı respectively.…”

Section: Introductionmentioning

confidence: 90%

“…To evaluate the quality of the CWS induced weekly vertical loading displacement, we calculate the Weighted Root Mean Square (WRMS) reduction of the GPS height time The white dots in the figure indicate that the STD value for the station is larger than the maximum value on the scale series using the CWS models (Jiang et al 2013) and the correlation coefficients between each CWS model and the GPS height. Some statistics of the WRMS and correlation results are shown in Table 2.…”

Section: Comparison Between Loading and Gps Height Time Seriesmentioning

confidence: 99%

“…Currently, the most frequent used CWS mass models are the soil moisture (SM) plus snow water equivalent (SWE) from the monthly Global Land Data Assimilation System (GLDAS) 1 (Rui 2011) and the six-hourly National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) global reanalysis datasets (R-1) (Kalnay et al 1996), with spatial resolutions as 1 ı 1 ı and 1:875 ı .1:8889 1:9048/ ı respectively. It is well known that the CWS model in the R-1 are inaccurate (Kanamitsu et al 2002), and (Jiang et al 2013) found that the vertical loading time series from the R-1 CWS model were not fit well with the GPS height. To fix the known errors and also update the parameterizations of physical processes in R-1,the NCEP-Department of Energy (NCEP-DOE) global reanalysis products (NCEP-R-2) came into being 2 (Kanamitsu et al 2002).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

Dam

2015

Refag 2014

Self Cite

View full text Add to dashboard Cite

To remove continental water storage (CWS) signals from the GPS data, CWS mass models are needed to obtain predicted surface displacements. We compared weekly GPS height time series with five CWS models: (1) the monthly and (2) three-hourly Global Land Data Assimilation System (GLDAS); (3) the monthly and (4) one-hourly ModernEra Retrospective Analysis for Research and Applications (MERRA); (5) the six-hourly National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) global reanalysis products (NCEP-R-2). We find that of the 344 selected global IGS stations, more than 77% of stations have their weighted root mean square (WRMS) reduced in the weekly GPS height by using both the GLDAS and MERRA CWS products to model the surface displacement, and the best improvement concentrate mainly in North America and Eurasia. We find that the one-hourly MERRA-Land dataset is the most appropriate product for modeling weekly vertical surface displacement caused by CWS variations. The threehourly GLDAS data ranks the second, while the GLDAS and MERRA monthly products rank the third. The higher spatial resolution MERRA product improves the performance of the CWS model in reducing the scatter of the GPS height by about 2-6% compared with the GLDAS. Under the same spatial resolution, the higher temporal resolution could also improve the performance by almost the same magnitude. We also confirm that removing the ATML and NTOL effects from the weekly GPS height would remarkably improve the performance of CWS model in correcting the GPS height by at least 10%, especially for coastal and island stations. Since the GLDAS product has a much greater latency than the MERRA product, MERRA would be a better choice to model surface displacements from CWS. Finally, we find that the NCEP-R-2 data is not sufficiently precise to be used for this application. Further work is still required to determine the reason.

show abstract

Section: Comparison Of the Weekly Loading Time Series Between Modelsmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 90%

Section: Comparison Between Loading and Gps Height Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

Dam

2015

Refag 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…4 We did not include the SWE data above the latitude of 60.5N. This area includes Greenland and most Arctic regions; GLDAS does not model snow dynamics well in these regions (Rui 2011;Jiang et al 2013).…”

Section: Data Descriptionmentioning

confidence: 99%

Quality Evaluation of the Weekly Vertical Loading Effects Induced from Continental Water Storage Models

Dam

Collilieux

et al. 2015

International Association of Geodesy Symposia

View full text Add to dashboard Cite

show abstract

Cause Analysis of the Non‐Linear Variations of the IGS Reference Station Coordinate Time Series in China

Jiang

Liu

et al. 2013

Chinese J of Geophysics

View full text Add to dashboard Cite

GPS position time series exhibit dramatic seasonal variations. It is generally considered that atmospheric pressure loading, nontidal ocean loading snow depth and soil moisture loading account for these seasonal changes. Focusing on the 11 IGS reference stations in China, this paper calculates station displacements caused by the above four kinds of surface mass loadings to correct the coordinate time series of these 11 stations during period of 1995–2010 under ITRF2005. We establish a preliminary numerical link among certain geophysical phenomena, seasonal variations and noise characteristics, finding that the calculated four kinds of surface mass loadings could produce variations in station's noise features, mainly exhibiting characteristics of band pass noise and random walk noise, and could reduce the annual amplitude of station's Up component. However, it could not well interpret station's semi‐annual and horizontal annual movement. We investigate the possible factors that could cause non‐linear variations of IGS stations, taking emphasis on the contributions of diurnal (S1) and semi‐diurnal (S2) atmospheric tides to the annual amplitude of the 11 stations, and propose that the S1–S2 atmospheric tides could be one of the main factors for annual motion of IGS stations in China, especially for the vertical annual motion of stations in central and southern regions.

show abstract

Comparative analysis of different environmental loading methods and their impacts on the GPS height time series

Cited by 100 publications

References 39 publications

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

The Phase 2 North America Land Data Assimilation System (NLDAS-2) Products for Modeling Water Storage Displacements for Plate Boundary Observatory GPS Stations

Quality Evaluation of the Weekly Vertical Loading Effects Induced from Continental Water Storage Models

Cause Analysis of the Non‐Linear Variations of the IGS Reference Station Coordinate Time Series in China

Contact Info

Product

Resources

About