Paul R. Houser

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AUGUST 2003 AMERICAN METEOROLOGICAL SOCIETY | C limate and weather forecasting models require the energy, water, and momentum fluxes across the land–atmosphere interface to be specified. Various land surface parameterizations (LSPs), ranging from the simple bucket-type LSP in the 1960s to the current soil–vegetation–atmosphere interactive LSP, have been(More)
[1] 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(More)
Because of its long-term persistence, accurate initialization of land surface soil moisture in fully coupled global climate models has the potential to greatly increase the accuracy of climatological and hydrological prediction. To improve the initialization of soil moisture in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a onedimensional(More)
Knowledge of land surface water, energy, and carbon conditions are of critical importance due to their impact on many real world applications such as agricultural production, water resource management, and flood, weather, and climate prediction. Land Information System (LIS) is a software framework that integrates the use of satellite and ground-based(More)
Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and(More)
State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size(More)
Soil moisture satellite mission accuracy, repeat time and spatial resolution requirements are addressed through a numerical twin data assimilation study. Simulated soil moisture profile retrievals were made by assimilating near-surface soil moisture observations with various accuracy (0, 1, 2, 3, 4, 5 and 10%v/v standard deviation) repeat time (1, 2, 3, 5,(More)
The performance of the extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation. In a twin experiment for the southeastern United States synthetic observations of near-surface soil moisture are assimilated once every 3 days, neglecting horizontal error correlations and treating catchments independently.(More)
[1] Simulating land surface hydrological states and fluxes requires a comprehensive set of atmospheric forcing data at consistent temporal and spatial scales. At the continental-toglobal scale, such data are not available except in weather reanalysis products. Unfortunately, reanalysis products are often biased due to errors in the host weather forecast(More)
International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) Project Rachel T. Pinker (1), J. Dan Tarpley (2), Istvan Laszlo (2), Kenneth E. Mitchell (3), Paul R. Houser (4), Eric F. Wood (5), John C. Schaake (6), Alan Robock (7), Dag Lohmann (3), Brian A. Cosgrove (8),(More)