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), 381394, 2004. Abstract
Non-exhaustive list
of GLDAS-related references:
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., in press, 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.
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
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 -Y´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.
Two 7-day weather simulations were made for South America in
July 2003 and January 2004 (in the south 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 (a) the soil moisture climatology used
operationally by the Centro de Previsão do Tempo e Estudos Climáticos in
This paper describes a spin-up experiment conducted over
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.
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
19791993 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 deficienciesmost notably an
overestimation of summertime precipitation in the central United States
(200400 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 -Y´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
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
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, 197993) 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.