Optimal averaging of soil moisture predictions from ensemble land surface model simulations

Improving Predictions Using Ensemble Bayesian Model Averaging

We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated v...

Towards Land Surface Model Validation from Using Satellite Retrieved Soil Moisture

Land surface model validation at distributed scales is important for model improvements. Recent advances in satellite technology provide an opportunity for distributed calibration and validation of land surface models. In the past years, a number of active and passive microwave soil moisture products have become available. While passive microwave soil moisture is the preferred approach for soil...

Joint assimilation of surface soil moisture and LAI observations into a land surface model

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Soil moisture initialization for climate prediction: Assimilation of scanning multifrequency microwave radiometer soil moisture data into a land surface model

[1] Climate model prediction skill is currently limited in response to poor land surface soil moisture state initialization. However, initial soil moisture state prediction skill can potentially be enhanced by the assimilation of remotely sensed near-surface soil moisture data in off-line simulation. This study is one of the first to evaluate such potential using actual remote sensing data toge...

On the Nature of Soil Moisture in Land Surface Models