scholarly journals Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4144 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Wen ◽  
Zhong ◽  
...  
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Better Than

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

scholarly journals Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

2019 ◽  
Vol 11 (6) ◽  
pp. 679 ◽  
Author(s):  
Vagner Ferreira ◽  
Christopher Ndehedehe ◽  
Henry Montecino ◽  
Bin Yong ◽  
Peng Yuan ◽  
...  
Keyword(s):  
South America ◽  
Amazon Basin ◽  
Mean Squared Error ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Resolution Analysis ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Better Than

Few studies have used crustal displacements sensed by the Global Positioning System (GPS) to assess the terrestrial water storage (TWS), which causes loadings. Furthermore, no study has investigated the feasibility of using GPS to image TWS over South America (SA), which contains the world’s driest (Atacama Desert) and wettest (Amazon Basin) regions. This work presents a resolution analysis of an inversion of GPS data over SA. Firstly, synthetic experiments were used to verify the spatial resolutions of GPS-imaged TWS and examine the resolving accuracies of the inversion based on checkerboard tests and closed-loop simulations using “TWS” from the Noah-driven Global Land Data Assimilation System (GLDAS-Noah). Secondly, observed radial displacements were used to image daily TWS. The inverted results of TWS at a resolution of 300 km present negligible errors, as shown by synthetic experiments involving 397 GPS stations across SA. However, as a result of missing daily observations, the actual daily number of available stations varied from 60–353, and only 6% of the daily GPS-imaged TWS agree with GLDAS-Noah TWS, which indicates a root-mean-squared error (RMSE) of less than 100 kg/m 2 . Nevertheless, the inversion shows agreement that is better than 0.50 and 61.58 kg/m 2 in terms of the correlation coefficient (Pearson) and RMSE, respectively, albeit at each GPS site.

scholarly journals Wind Speed Seasonality in a Brazilian Amazon-Savanna Region from the Global Land Data Assimilation System

2020 ◽  
Vol 42 ◽  
pp. e12
Author(s):  
Leonardo Henrique De Sá Rodrigues ◽  
Marcos Aurélio Alves Freitas ◽  
Luan Victor Soares Pereira ◽  
Brunna Caroline Correia Dias ◽  
Vicente Marques Silvino ◽  
...  
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Wind Energy ◽  
Linear Regression Analysis ◽  
The State ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

The objective of this study was to develop a methodology for the use of remote sensing data for the planning of wind energy projects in Maranhão. Monthly wind speed and precipitation data from 2000 to 2016 were used. Initially, wind velocity data were processed using the principal component analysis (PCA) technique. Next, the grouping technique known as k-means was used. Finally, a linear regression analysis was performed with the objective of identifying the parameters to be used in the validation of the data estimated by the Global Land Data Assimilation System (GLDAS) base against the data measured by the meteorological stations. Four homogeneous zones were identified; the zone with the highest values of monthly average wind speeds is in the northern region of the state on the coast. The period of greatest intensity of the winds was identified to be in the months of October and November. The lowest values of precipitation were observed during these months. The analyses carried out by this study show a favorable scenario for the production of wind energy in the state of Maranhão.

From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States

2020 ◽  
Author(s):  
Anthony Mucia ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

<p>LDAS-Monde is a global Land Data Assimilation System developed in the research department of Météo-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.</p><p>In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2<sup>o</sup> x 0.2<sup>o</sup> spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.</p>

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