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The accuracy of forcing data greatly impacts the ability of land surface models (LSMs) to produce realistic simulations of land surface processes. With this in mind, the multi‐institutional North American Land Data Assimilation System (NLDAS) project has produced retrospective (1996–2002) and real‐time (1999–present) data sets to support its LSM modeling activities. Featuring 0.125° spatial resolution, hourly temporal resolution, nine primary forcing fields, and six secondary validation/model development fields, each data set is based on a backbone of Eta Data Assimilation System/Eta data and is supplemented with observation‐based precipitation and radiation data. Hourly observation‐based precipitation data are derived from a combination of daily National Center for Environmental Prediction Climate Prediction Center (CPC) gauge‐based precipitation analyses and hourly National Weather Service Doppler radar‐based (WSR‐88D) precipitation analyses, wherein the hourly radar‐based analyses are used to temporally disaggregate the daily CPC analyses. NLDAS observation‐based shortwave values are derived from Geostationary Operational Environmental Satellite radiation data processed at the University of Maryland and at the National Environmental Satellite Data and Information Service. Extensive quality control and validation efforts have been conducted on the NLDAS forcing data sets, and favorable comparisons have taken place with Oklahoma Mesonet, Atmospheric Radiation Measurement Program/cloud and radiation test bed, and Surface Radiation observation data. The real‐time forcing data set is constantly evolving to make use of the latest advances in forcing‐related data sets, and all of the real‐time and retrospective data are available online at http://ldas.gsfc.nasa.govfor visualization and downloading in both full and subset forms.

This article discusses the water and surface energy budgets over the Mississippi River basin and subbasins using NCEP's Eta model forecasts. It also discusses the relation between surface states (soil moisture) and other variables that affect the surface energy balance, potentially interacting with precipitation processes. The Eta model is NCEP's operational mesoscale model and therefore has been subject to changes and upgrades over the period of this evaluation. While the atmospheric water cycle is analyzed here using a 7‐year long (June 1995 to May 2002) data set, the research focusing on surface processes is based on a 4‐year period (June 1998 to May 2002) after substantial upgrades to the land surface component were performed. On the 7‐year average the Eta model 12–36 h forecast precipitation averaged over the Mississippi basin differs from the observed precipitation by 2%, while the estimate of evaporation computed as a residual of the water balance equation differs by 5% from the evaporation estimate resulting from a data set of land surface fluxes prepared with the macroscale hydrologic variable infiltration model (VIC). However, the difference between the model parameterized evaporation and VIC's is about 17% due to excessive bare soil evaporation in the Eta model. Notably, the long‐term average of moisture flux convergence also estimated from the 12–36 h forecasts is 0.54 mm day−1over the Mississippi basin, while streamflow observations at Vicksburg average 0.50 mm day−1. This agreement within 10% is a strict test of the quality of the hydrologic cycle estimates; therefore these are promising results for estimates of the water cycle from regional analysis or future regional reanalysis. Subbasins of the Mississippi have diverse land surface‐atmosphere interactions at monthly timescales. In the western half of the Mississippi basin, feedbacks can be described as follows: increased soil moisture is associated with a slight increase of net radiation at the surface; latent heat also increases with soil moisture while sensible heat decreases, resulting in an almost linear increase of the evaporative fraction. Increased soil moisture is also associated with a lower lifting condensation level and an increase of observed precipitation (though not statistically significant). The overall results support the concept of a positive feedback in which increased soil moisture affects surface fluxes in such a manner that increased precipitation results. However, toward the east (e.g., the Ohio basin), there are no well‐defined land surface‐atmosphere interactions, suggesting that other effects, like the advection of moisture, may be more relevant for precipitation processes.

Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three‐year (September 1996–September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station‐observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state‐of‐the‐art data set for land surface modeling, at least over the southern Great Plains region.

This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's Snowpack Telemetry (SNOTEL) network for a 3‐year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic low bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000 mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000 mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.

In support of the World Climate Research Program GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), real‐time estimates of shortwave radiative fluxes, both at the surface and at the top of the atmosphere, are being produced operationally by the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite Data and Information Service using observations from GOES images. The inference scheme has been developed at the Department of Meteorology, University of Maryland, and the atmospheric and surface model input parameters are produced and provided by the NOAA/National Centers for Environmental Prediction. The radiative fluxes are being evaluated on hourly, daily, and monthly timescales using observations at about 50 stations. The satellite estimates have been found to be within acceptable limits during snow‐free periods, but the difficulty in detecting clouds over snow affects the accuracy during the winter season. In what follows, this activity is discussed, and evaluation results of the derived fluxes against ground observations for time periods of 1–2 years are presented.

This study evaluates the cold season process modeling in the North American Land Data Assimilation System (NLDAS) and consists of two parts: (1) assessment of land surface model simulations of snow cover extent and (2) evaluation of snow water equivalent. In this first part, simulations of snow cover extent from the four land surface models (Noah, MOSAIC, Sacramento land surface model (SAC), and variable infiltration capacity land surface model (VIC)) in the NLDAS were compared with observational data from the Interactive Multisensor Snow and Ice Mapping System for a 3 year retrospective period over the conterminous United States. In general, all models simulate reasonably well the regional‐scale spatial and seasonal dynamics of snow cover. Systematic biases are seen in the model simulations, with consistent underestimation of snow cover extent by MOSAIC (−19.8% average bias) and Noah (−22.5%), and overestimation by VIC (22.3%), with SAC being essentially unbiased on average. However, the level of bias at the regional scale varies with geographic location and elevation variability. Larger discrepancies are seen over higher elevation regions of the northwest of the United States that may be due, in part, to errors in the meteorological forcings and also at the snow line boundary, where most temporal and spatial variability in snow cover extent is likely to occur. The spread between model simulations is fairly low and generally envelopes the observed data at the mean regional scale, indicating that the models are quite capable of simulating the general behavior of snow processes at these scales. Intermodel differences can be explained to some extent by differences in the model representations of subgrid variability and parameterizations of snow cover extent.

North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May–September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model‐specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow‐free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.

The process of a model adjusting to its forcing (model spin‐up) can severely bias land surface simulations, and result in questionable land surface model (LSM) output during the spin‐up process. To gain a better understanding of how spin‐up processes affect complex spatial and temporal land surface modeling situations in general, and the Retrospective North American Land Data Assimilation System (NLDAS) simulations in particular, a two‐phase study was conducted. The first phase examined results from Control, Wet, and Dry 11 year‐long Mosaic simulations, while the second phase attempted to explain spin‐up behavior in NLDAS Retrospective simulations from the Mosaic, Noah, VIC and Sacramento LSMs based in part on the results from phase 1. Total column and root zone soil moisture spin up slowly, while evaporation and deep soil temperature spin up more quickly. Mosaic soil moisture initialization with NCEP/DOE Global Reanalysis 2 (NCEP/DOE R‐2) data (Control run) leads to a faster spin‐up time than saturated (Wet run) or dry (Dry run) initialization, with the Control run reaching equilibrium 1 to 2 years sooner than the Wet run and 3 to 4 years more quickly than the Dry run. Overall, practical drift of land surface stores and output ceased in the Control run within approximately 1 year, and fine‐scale equilibrium was reached within 5.5 years. Spin‐up times exhibited large spatial variability, and although no single causal factor could be determined, they were correlated most strongly with precipitation and temperature forcing. In general, NLDAS models reach a state of rough equilibrium within the first 1 to 2 years of the 3‐year Retrospective simulation. The Sacramento LSM has the shortest spin‐up phase, followed by the Mosaic, VIC, and Noah LSMs. Initial NCEP/DOE R‐2 conditions were too dry in general for the VIC and Noah LSMs, and too moist for the Mosaic and Sacramento LSMs. These results indicate that in most cases, the 1‐year spin‐up time used in the Retrospective NLDAS simulations eliminated spin‐up problems from the subsequent period that was used for analysis.