Traditional coupled land-atmosphere 4-D data assimilation systems (4DDA) often yield significant errors and drift in soil moisture and surface energy/water fluxes owing to biases in precipitation, surface radiation and air temperature in the surface forcing from the parent atmospheric models. The GEWEX initiatives of ISLSCP GSWP, PILPS 2c and 2e, and Rhone/GLASS demonstrated the viability of executing distributed, uncoupled, macro-scale land-surface models over large spatial domains, provided moderately dense precipitation observations are available. Hence, as an uncoupled alternative to coupled 4DDA, we have undertaken the collaborative development, execution, and evaluation of an uncoupled national-scale Land Data Assimilation System (N-LDAS) -- a realtime, hourly, distributed, uncoupled, land-surface system on a U.S. CONUS domain. This N-LDAS partnership includes NCEP (EMC and CPC), NASA/GSFC, NESDIS/ORA, NWS/HRL, Princeton University, Rutgers University, University of Washington, and University of Maryland. In the N-LDAS, we are executing in tandem the four land-surface models (LSMs) of NOAH, MOSAIC, VIC and SAC on a common 1/8th-degree grid with common land/sea mask, driven by common surface forcing anchored by model-independent, observation-based precipitation and solar insolation fields. Also, a common stream-connectivity network and streamflow routing model is applied to each LSM's gridded runoff to provide simulated streamflow from each LSM.

The goals of the LDAS project are to 1) provide land-state initial conditions (e.g. soil moisture and snowpack) for coupled land/atmosphere regional models for a) realtime predictions of seasonal climate and near-term weather and b) retrospective land-memory predictability studies, 2) improve LSM physics by sharing methodologies and data sources, 3) identify and reduce the causes of the spread in surface water fluxes and surface water storage typically seen in LSM intercomparisons, 4) compare land states of the uncoupled LDAS with traditional coupled 4DDA (e.g. Global and Regional Reanalysis), 5) explore how to best validate grid-scale LSM soil moisture and surface fluxes against in-situ point-wise measurements, and 6) demonstrate how to assimilate land-state related satellite retrievals (e.g. snowpack, skin temperature) using such techniques as adjoint models and Kalman filtering.

In this paper we will present an overview of the approach, results, and lessons from the N-LDAS project to date. We will illustrate N-LDAS results from both 1) realtime executions during the period of Apr 99 to the present, and 2) retrospective executions for the period Oct 96 - Sep 99. In particular, we will focus on how the N-LDAS project has A) assembled together a number of GAPP PIs and their support scientists in an ongoing collaboration on a large-scale realtime demonstration project and B) leveraged together an impressive suite of products and capabilities that emerged from different components of the GCIP Program (e.g., gage and radar precipitation analyses, satellite-derived surface insolation, soil moisture observations, N.H. snow cover analysis, retrospective LDAS, and advancements to several land models).