HR: 0800h
AN: A31B-02    [Abstracts]
TI: A data assimilation OSSE for assessing uncertainty and utility of soil moisture retrievals
AU: * Reichle, R
EM: reichle@gmao.gsfc.nasa.gov
AF: NASA Global Modeling and Assimilation Office, Code 610.1 Greenbelt Rd, Greenbelt, MD 20771, United States
AU: * Reichle, R
EM: reichle@gmao.gsfc.nasa.gov
AF: UMBC/GEST, 1000 Hilltop Circle, Baltimore, MD 21250, United States
AU: Crow, W T
EM: wcrow@hydrolab.arsusda.gov
AF: ARS Hydrology and Remote Sensing Lab, USDA, Beltsville, MD 20705, United States
AU: Koster, R D
EM: randal.d.koster@nasa.gov
AF: NASA Global Modeling and Assimilation Office, Code 610.1 Greenbelt Rd, Greenbelt, MD 20771, United States
AU: Sharif, H
EM: Hatim.Sharif@utsa.edu
AF: Civil Engineering Department, University of Texas, San Antonio, TX 78249, United States
AU: Mahanama, S P
EM: sarith@gmao.gsfc.nasa.gov
AF: NASA Global Modeling and Assimilation Office, Code 610.1 Greenbelt Rd, Greenbelt, MD 20771, United States
AU: Mahanama, S P
EM: sarith@gmao.gsfc.nasa.gov
AF: UMBC/GEST, 1000 Hilltop Circle, Baltimore, MD 21250, United States
AB: Soil moisture is of interest for a variety of reasons, including water cycle studies and the initialization of weather and climate forecasts, but it is also difficult to observe at the global scale. Satellite retrievals of surface soil moisture are subject to large uncertainties because the physical processes that relate brightness temperature to soil moisture are difficult to parameterize, and because the necessary parameters are difficult to obtain on the global scale. An important question for the design of new satellite sensors is just how uncertain satellite retrievals can be and still add useful information to a land data assimilation system. In this paper, we address this question with an Observing System Simulation Experiment (OSSE) that is based on high-resolution (1 km) "true" soil moisture fields and associated passive microwave brightness temperatures from a long-term integration of the TOPLATS land surface model over the Red-Arkansas river basin. From the true fields, we simulate many different retrieval data sets at a typical satellite footprint scale (36 km). The various retrieval data sets reflect various realistic sources of uncertainty with different error structure and magnitude. We use the NASA Catchment land surface model (CLSM) to set up different land model scenarios that reflect varying degrees of uncertainty in model parameter and forcing data. The various simulated retrieval data sets are then assimilated into the various model scenarios with an Ensemble Kalman filter (EnKF) in a suite of data assimilation experiments. The EnKF is fitted with a simple and effective method of bias removal (cumulative distribution function matching) and an adaptive component for on-line model error estimation. The adaptive feature is critical because correct assessment of the utility of the retrievals depends on the near-optimal performance of the assimilation system for each data assimilation experiment. Earlier research has shown that off-line estimation of model error parameters outside of the cycling assimilation system is not a viable strategy. Calibration of the model parameters by repeating the each data assimilation experiment until acceptable model error parameters are found is not computationally feasible. Finally, the quality of the assimilation estimates (with respect to the synthetic truth) is compared with that of a baseline integration of the Catchment model without assimilation. This procedure permits us to quantify the maximum level of uncertainty in the satellite retrievals for which information is still added in the assimilation, depending on the application. Performance measures include the traditional absolute (RMS) error, which is important for water cycle studies, and the time series correlation coefficient. The latter measures how well (scaled) anomalies are estimated, which contain the key information for forecast initialization.
DE: 1816 Estimation and forecasting
DE: 1843 Land/atmosphere interactions (1218, 1631, 3322)
DE: 1873 Uncertainty assessment (3275)
DE: 1878 Water/energy interactions (0495)
DE: 3315 Data assimilation
SC: Atmospheric Sciences [A]
MN: 2007 Joint Assembly