scholarly journals Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

2016 ◽  
Vol 17 (7) ◽  
pp. 1951-1972 ◽  
Author(s):  
Sujay V. Kumar ◽  
Benjamin F. Zaitchik ◽  
Christa D. Peters-Lidard ◽  
Matthew Rodell ◽  
Rolf Reichle ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Eastern United States ◽  
Terrestrial Water Storage ◽  
The North ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System

Abstract The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

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

2020 ◽  
Vol 12 (12) ◽  
pp. 2020 ◽  
Author(s):  
Anthony Mucia ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Clément Albergel ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Model ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) 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 ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

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.

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>

scholarly journals Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

2017 ◽  
Vol 53 (11) ◽  
pp. 8941-8965 ◽  
Author(s):  
Sujay V. Kumar ◽  
Shugong Wang ◽  
David M. Mocko ◽  
Christa D. Peters-Lidard ◽  
Youlong Xia
Keyword(s):  
Data Assimilation ◽  
North American ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
The North ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Surface Solar Radiation in North America: A Comparison of Observations, Reanalyses, Satellite, and Derived Products*

2015 ◽  
Vol 17 (1) ◽  
pp. 401-420 ◽  
Author(s):  
Andrew G. Slater
Keyword(s):  
United States ◽  
Solar Radiation ◽  
Data Assimilation ◽  
Bias Correction ◽  
Surface Solar Radiation ◽  
Clear Sky ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Observations of daily surface solar or shortwave radiation data from over 4000 stations have been gathered, covering much of the continental United States as well as portions of Alberta and British Columbia, Canada. The quantity of data increases almost linearly from 1998, when only several hundred stations had data. A quality-control procedure utilizing threshold values along with computing the clear-sky radiation envelope for individual stations was implemented to both screen bad data and rescue informative data. Over two-thirds of the observations are seen as acceptable. There are 15 different surface solar radiation products assessed relative to observations, including reanalyses [Twentieth-Century Reanalysis (20CR), CFS Reanalysis and Reforecast (CFSRR), ERA-Interim, Japanese 55-year Reanalysis Project (JRA-55), MERRA, NARR, and NCEP–NCAR Reanalysis 1 (NCEP-1)], derived products [observations from the CRU and NCEP-1 (CRU–NCEP); Daily Surface Weather and Climatological Summaries (Daymet); Global Land Data Assimilation System, version 1 (GLDAS-1); Global Soil Wetness Project Phase 3 (GSWP3); Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP); and phase 2 of the North American Land Data Assimilation System (NLDAS-2)], and two satellite products (CERES and GOES). All except the CERES product have daily or finer temporal resolution. The RMSE of spatial biases is greater than 18 W m−2 for 13 of the 15 products over the summer season (June–August). None of the daily resolution products fulfill all three desirable criteria of low (<5%) annual or seasonal bias, high correlation with observed cloudiness, and correct distribution of clear-sky radiation. Some products display vestiges of underlying algorithm issues [e.g., from the Mountain Microclimate Simulation Model, version 4.3 (MTCLIM 4.3)] or bias-correction methods. A new bias-correction method is introduced that preserves clear-sky radiation values and better replicates cloudiness statistics. The current quantity of data over the continental United States suggests that a solar radiation product based on, or enhanced with, observations is feasible.

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