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Presenter: Rolf Reichle

Seminar Title: Soil moisture data assimilation: Error modeling, adaptive filtering, and the contribution of soil moisture retrievals to land data assimilation products

In this presentation, we describe the development of an adaptive filtering module in the GMAO land assimilation system (Part 1) and its application to the assimilation of soil moisture retrievals (Part 2).

Part 1: In a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface soil moisture retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters affect the quality of the assimilation products. In particular, soil moisture estimates from data assimilation are sensitive to observation and model error variances and, for very poor input error parameters, may even be worse than model estimates without data assimilation. Estimates of surface heat fluxes and runoff are at best marginally improved through the assimilation of surface soil moisture and tend to have large errors when the assimilation system operates with poor input error parameters. We present a computationally affordable, adaptive assimilation system that continually adjusts model and observation error parameters in response to internal diagnostics. The adaptive filter can identify model and observation error variances and provide generally improved assimilation estimates when compared to the non- adaptive system.

Part 2: Satellite measurements (retrievals) of surface soil moisture are subject to errors and cannot provide complete space-time coverage. Data assimilation systems merge available retrievals with information from land surface models and antecedent meteorological data, information that is spatio-temporally complete but likewise uncertain. For the design of new satellite missions it is critical to understand just how uncertain retrievals can be and still be useful. Here, we present a synthetic data assimilation experiment that determines the contribution of retrievals to the skill of land assimilation products (soil moisture and evapotranspiration) as a function of retrieval and land model skill. As expected, the skill of the assimilation products increases with the skill of the model and that of the retrievals. The skill of the soil moisture assimilation products always exceeds that of the model acting alone; even retrievals of low quality contribute information to the assimilation product, particularly if model skill is modest.

>> Presentation Slides (PDF)

References:

Reichle, R. H., W. T. Crow, and C. L. Keppenne: An adaptive ensemble Kalman filter for soil moisture data assimilation, Water Resources Research, in press, 2008.

Reichle, R. H., W. T. Crow, R. D. Koster, H. Sharif, and S. P. P. Mahanama: Contribution of soil moisture retrievals to land data assimilation products, Geophysical Research Letters, 35, L01404, doi: 10.1029/2007GL031986, 2008.

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