LDAS | Land Data Assimilation Systems

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GLDAS Publications

For reprints of any article listed on this page, contact Matthew Rodell.

Primary Reference:

Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3), 381-394, 2004. Abstract

GLDAS validation publications are marked with a "V".

Non-exhaustive list of GLDAS-related references:

Zaitchik, B.F., M. Rodell, and F. Olivera, Evaluation of the Global Land Data Assimilation System using global river discharge data and a source to sink routing scheme, Water Resour. Res., doi:10.1029/2009WR007811, in press, 2010. Abstract - V

Syed, T.H., J.S. Famiglietti, M. Rodell, J.L. Chen, and C.R. Wilson, Analysis of terrestrial water storage changes from GRACE and GLDAS, Wat. Resour. Res., 44, W02433, doi:10.1029/2006WR005779, 2008. Abstract - V

Rodell, M., and H. Kato, GLDAS output supports CEOP studies, CEOP Newsletter, 10, 2006. Full Text

Goncalves, L., J. Shuttleworth, S.C. Chou, Y. Xue, P. Houser, D. Toll, J. Marengo, and M. Rodell, Impact of different initial soil moisture fields on Eta model weather forecasts for South America, J. Geophys. Res., 111, D17102, doi:10.1029/2005JD006309, 2006. Abstract

Goncalves, L., J. Shuttleworth, E. Burke, P. Houser, D. Toll, M. Rodell, and K. Arsenault, Towards a South America land data assimilation system (SALDAS): aspects of land surface model spin up using the Simplified Simple Biosphere (SSiB), J. Geophys. Res., 111, D17110, doi:10.1029/2005JD006297, 2006. Abstract

Kato, H., M. Rodell, F. Beyrich, H. Cleugh, E. van Gorsel, H. Liu, and T.P. Meyers, Sensitivity of Land Surface Simulations to Model Physics, Parameters, and Forcings, at Four CEOP Sites, J. Meteor. Soc. Japan, 85A, 187-204, 2007. Abstract - V

Rodell, M., P.R. Houser, A.A. Berg, and J.S. Famiglietti, Evaluation of ten methods for initializing a land surface model, J. Hydromet., 6(2), 146-155, 2005. Abstract

Berg A.A., J.S. Famiglietti, M. Rodell, R.H. Reichle, U. Jambor, S.L. Holl, and P.R. Houser, Development of a hydrometeorological forcing data set for global soil moisture estimation, Int. J. Climatol., 25(13), 1697-1714, 2005. Abstract

Gottschalck, J., J. Meng, M. Rodell, and P. Houser, Analysis of multiple precipitation products and preliminary assessment of their impact on Global Land Data Assimilation System (GLDAS) land surface states, J. Hydromet., 6(5), 573-598, 2005. Abstract

Rodell, M., and P.R. Houser, Updating a land surface model with MODIS derived snow cover, J. Hydromet., 5(6), 1064-1075, 2004. Abstract

Rodell, M., J.S. Famiglietti, J. Chen, S. Seneviratne, P. Viterbo, S. Holl, and C. R. Wilson, Basin scale estimates of evapotranspiration using GRACE and other observations, Geophys. Res. Lett., 31, L20504, doi:10.1029/2004GL020873, 2004. Abstract - V

Koster, R.D., M.J. Suarez, P. Liu, U. Jambor, M. Kistler, A. Berg, R. Reichle, M. Rodell, and J. Famiglietti, Realistic initialization of land surface states: impacts on subseasonal forecast skill, J. Hydromet., 5(6), 1049-1063, 2004. Abstract

ABSTRACTS

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation based data, runs globally at high resolution (0.25°), and produces results in near-real time (typically within 48 hours of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based "tiling" approach is used to simulate sub-grid scale variability, with a 1 km global vegetation dataset as its basis. Soil and elevation parameters are based on high resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The 2001-forward GLDAS archive of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.

Syed, T.H., J.S. Famiglietti, M. Rodell, J.L. Chen, and C.R. Wilson, Analysis of terrestrial water storage changes from GRACE and GLDAS, Wat. Resour. Res., 44, W02433, doi:10.1029/2006WR005779, 2008.

Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided first estimates of land water storage variations by monitoring the time-variable component of Earth's gravity field. Here we characterize spatial-temporal variations in terrestrial water storage changes (TWSC) from GRACE and compare them to those simulated with the Global Land Data Assimilation System (GLDAS). Additionally, we use GLDAS simulations to infer how TWSC is partitioned into snow, canopy water and soil water components, and to understand how variations in the hydrologic fluxes act to enhance or dissipate the stores. Results quantify the range of GRACE-derived storage changes during the studied period and place them in the context of seasonal variations in global climate and hydrologic extremes including drought and flood, by impacting land memory processes. The role of the largest continental river basins as major locations for freshwater redistribution is highlighted. GRACE-based storage changes are in good agreement with those obtained from GLDAS simulations. Analysis of GLDAS-simulated TWSC illustrates several key characteristics of spatial and temporal land water storage variations. Global averages of TWSC were partitioned nearly equally between soil moisture and snow water equivalent, while zonal averages of TWSC revealed the importance of soil moisture storage at low latitudes and snow storage at high latitudes. Evapotranspiration plays a key role in dissipating globally averaged terrestrial water storage. Latitudinal averages showed how precipitation dominates TWSC variations in the tropics, evapotranspiration is most effective in the midlatitudes, and snowmelt runoff is a key dissipating flux at high latitudes. Results have implications for monitoring water storage response to climate variability and change, and for constraining land model hydrology simulations.

Two 7-day weather simulations were made for South America in July 2003 and January 2004 (in the Southern Hemisphere summer and winter) to investigate the impacts of using different soil moisture initialization fields in the Eta model coupled to the Simplified Simple Biosphere (SSiB) land surface model. The alternative initial soil moisture fields were (1) the soil moisture climatology used operationally by the Centro de Previsão do Tempo e Estudos Climáticos in Brazil and (2) the soil moisture fields generated by a South American Land Data Assimilation System (SALDAS) based on SSiB. When the SALDAS soil moisture fields were used, there was an increase in the model performance relative to climatology in the equitable threat score calculated with respect to observed surface precipitation fields and a decrease (up to 53%) in the root-mean-square error relative to the NCEP analysis of the modeled geopotential height at 500 hPa and mean sea level pressure. However, there was small change in the model skill in positioning the primary South American weather systems because of a change in the upper troposphere circulation caused by SALDAS initialization, most noticeably in the South Atlantic Convergence Zone.

This paper describes a spin-up experiment conducted over South America using the Simplified Simple Biosphere (SSiB) land surface model to study the process of model adjustment to atmospheric forcing data. The experiment was carried out as a precursor to the use of SSiB in a South American Land Data Assimilation System (SALDAS). The results from an 11 year long recursive simulation using three different initial conditions of soil wetness (control, wet and dry) are examined. The control run was initiated by interpolation of the NCEP/DOE Global Reanalysis-2 (NCEP/DOE R-2) soil moisture data set. In each case the time required for the model to reach equilibrium was calculated. The wet initialization leads to a faster adjustment of the soil moisture field, followed by the control and then the dry initialization. Overall, the final spin-up states using the SSiB-based SALDAS are generally wetter than both the NCEP/DOE R-2 and the Centro de Previsao do Tempo e Estudos Climaticos (CPTEC-Brazilian Center for Weather Forecast and Climate Studies) operational initial soil moisture states, consequently modeled latent heat is higher and sensible heat lower in the final year of simulation when compared with the first year. Selected regions, i.e., in semiarid northeastern Brazil, the transition zone to the south of the Amazon tropical forest, and the central Andes were studied in more detail because they took longer to spin up (up to 56 months) when compared with other areas (less than 24 months). It is shown that there is a rapid change in the soil moisture in all layers in the first 2 months of simulation followed by a subsequent slow and steady adjustment: This could imply there are increasing errors in medium range simulations. Spin-up is longest where frozen soil is present for long periods such as in the central Andes.

Numerical land surface models (LSMs) are abundant and in many cases highly sophisticated, yet their output has not converged towards a consensus depiction of reality. Addressing this matter is complicated by the huge number of possible combinations of input land characteristics, forcings, and physics packages available. The Global Land Data Assimilation System (GLDAS) and its sister project the Land Information System (LIS) have made it straightforward to test a variety of configurations with multiple LSMs. In order to compare the impacts of the choice of LSM, land cover, soil, and elevation information, and precipitation and downward radiation forcing datasets on simulated evapotranspiration, sensible heat flux, and top layer soil moisture, a set of experiments was designed which made use of high quality, physically coherent, 1-year datasets from four reference sites of the Coordinated Enhanced Observing Period (CEOP) initiative. As in previous studies, it was shown that the LSM itself is generally the most important factor governing output. Beyond that, evapotranspiration seems to be most sensitive to precipitation, land cover, and radiation (in that order); sensible heat flux is most sensitive to radiation, precipitation, and land cover; and soil moisture is most sensitive to precipitation, soil, and land cover. Various seasonal and model specific dependencies and other caveats are discussed. Output fields were also compared with observations in order to test whether the LSMs are capable of simulating an observed reality given a plausible set of inputs. In general, that potential was fair for evapotranspiration, good for sensible heat flux but problematic given its strong sensitivity to the inputs, and poor for soil moisture. The results emphasize that improving the LSMs themselves, and not just the inputs, will be essential if we hope to model land surface water and energy processes accurately.

Rodell, M., P.R. Houser, A.A. Berg, and J.S. Famiglietti, Evaluation of ten methods for initializing a land surface model, J. Hydromet., 6(2), 146-155, 2005.

Improper initialization of numerical models can cause spurious trends in the output, inviting erroneous interpretations of the Earth system processes that one wishes to study. In particular, soil moisture memory is considerable, so that accurate initialization of this variable in land surface models (LSMs) is critical. The most commonly employed method for initializing a LSM is to spin-up by looping through a single year repeatedly until a predefined equilibrium is achieved. The downside to this technique, when applied to continental to global scale simulations, is that regional annual anomalies in the meteorological forcing accumulate as artificial anomalies in the land surface states, including soil moisture. Nine alternative approaches were tested and compared using the Mosaic LSM and 15 years of global meteorological forcing. Results indicate that the most efficient way to initialize a LSM, if possible and given that multiple years of preceding forcing are not available, is to use climatological average states from the same model for the precise time of year of initialization. Three other approaches were also determined to be preferable to the single year spin-up method. In addition, low resolution spin-up scenarios were devised and tested, and based on the results an effective yet computationally economical technique is proposed.

Off-line land surface modeling simulations require accurate meteorological forcing with consistent spatial and temporal resolutions. Although reanalysis products present an attractive data source for these types of applications, bias to many of the reanalysis fields limits their use for hydrological modeling. In this study, we develop a global 0.5° forcing data sets for the time period 1979-1993 on a 6-hourly time step through application of a bias correction scheme to reanalysis products. We then use this forcing data to drive a land surface model for global estimation of soil moisture and other hydrological states and fluxes. The simulated soil moisture estimates are compared to in situ measurements, satellite observations and to a modeled data set of root zone soil moisture produced within a separate land surface model, using a different data set of hydrometeorological forcing. In general, there is good agreement between anomalies in modeled and observed (in situ) root zone soil moisture. Similarly, for the surface soil wetness state, modeled estimates and satellite observations are in general statistical agreement; however, correlations decline with increasing vegetation amount. Comparisons to a modeled data set of soil moisture also demonstrates that both simulations present estimates that are well correlated for the soil moisture in the anomaly time series, despite being derived from different land surface models, using different data sources for meteorological forcing, and with different specifications of the land surfaces properties.

Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM).

A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies most notably an overestimation of summertime precipitation in the central United States (200-400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from -75% to +100%. The percent differences in SWC are generally 25-75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally "damp" precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States.

Rodell, M., and P.R. Houser, Updating a land surface model with MODIS derived snow cover, J. Hydromet., 5(6), 1064-1075, 2004.

A simple scheme for updating snow water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites. Simulated snow water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101 day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground based observation networks over the continental U.S. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.

Evapotranspiration is integral to studies of the Earth system, yet it is difficult to measure on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling closure of this equation by providing the terrestrial water storage change. Equations are presented here for estimating evapotranspiration using observation based information, taking into account the unique nature of GRACE observations. GRACE water storage changes are first substantiated by comparing with results from a land surface model and a combined atmospheric-terrestrial water budget approach. Evapotranspiration is then estimated for 14 time periods over the Mississippi River basin and compared with output from three modeling systems. The GRACE estimates generally lay in the middle of the models and may provide skill in evaluating modeled evapotranspiration.

Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and nearsurface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979-1993) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.