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approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models ( Jones et al. 2004 ; Huang et al. 2008 ), improved energy partitioning between latent and sensible heat fluxes ( Pipunic et al. 2008 ), and a concomitant higher skill in quantitative precipitation forecasts ( Koster et al. 2000 ). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at
approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models ( Jones et al. 2004 ; Huang et al. 2008 ), improved energy partitioning between latent and sensible heat fluxes ( Pipunic et al. 2008 ), and a concomitant higher skill in quantitative precipitation forecasts ( Koster et al. 2000 ). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at
1. Introduction Microwave sensors on board remote sensing satellites offer an attractive and relatively direct way of measuring soil moisture, thanks to the strong relationship between soil moisture content and the soil dielectric constant. In contrast to pointwise in situ measurements, satellite-borne instruments delivering measurements integrated over larger areas are better suited for hydrological studies of entire catchments or geographical regions. Yet to date, because of the complexity of
1. Introduction Microwave sensors on board remote sensing satellites offer an attractive and relatively direct way of measuring soil moisture, thanks to the strong relationship between soil moisture content and the soil dielectric constant. In contrast to pointwise in situ measurements, satellite-borne instruments delivering measurements integrated over larger areas are better suited for hydrological studies of entire catchments or geographical regions. Yet to date, because of the complexity of
1. Introduction Recent advances in hydrologic data assimilation have demonstrated the value of remotely sensed surface soil moisture for improving the prediction of key hydrologic variables such as root-zone soil moisture ( Walker et al. 2001 ; Reichle et al. 2007 ; Kumar et al. 2008 ) and surface runoff ( Crow et al. 2005 ). In hydrologic data assimilation, the Kalman filter provides a statistical framework to optimally update model predictions using observations based on the uncertainties
1. Introduction Recent advances in hydrologic data assimilation have demonstrated the value of remotely sensed surface soil moisture for improving the prediction of key hydrologic variables such as root-zone soil moisture ( Walker et al. 2001 ; Reichle et al. 2007 ; Kumar et al. 2008 ) and surface runoff ( Crow et al. 2005 ). In hydrologic data assimilation, the Kalman filter provides a statistical framework to optimally update model predictions using observations based on the uncertainties
products and hydrometeors for high-frequency microwave products), or propagation of in situ profile information to locations where sensors are temporarily out of service. In satellite data assimilation schemes, the modeled grid cells are typically covered by the observations (after reprojecting the data). This allows an update of all the a priori error correlations at each time observations are available and use them, whenever part of the observations falls out and information needs to be propagated to
products and hydrometeors for high-frequency microwave products), or propagation of in situ profile information to locations where sensors are temporarily out of service. In satellite data assimilation schemes, the modeled grid cells are typically covered by the observations (after reprojecting the data). This allows an update of all the a priori error correlations at each time observations are available and use them, whenever part of the observations falls out and information needs to be propagated to
the high sensitivity of simulated states and fluxes to these parameters can cause spurious drifts in the soil moisture state. To address this issue in model-based soil moisture products, different land data assimilation systems have been developed to constrain simulated soil moisture to observations of screen-level temperature and humidity (e.g., Bouttier et al. 1993 ; Rhodin et al. 1999 ; Douville et al. 2000 ), surface soil moisture or surface emissivity (e.g., Heathman et al. 2003
the high sensitivity of simulated states and fluxes to these parameters can cause spurious drifts in the soil moisture state. To address this issue in model-based soil moisture products, different land data assimilation systems have been developed to constrain simulated soil moisture to observations of screen-level temperature and humidity (e.g., Bouttier et al. 1993 ; Rhodin et al. 1999 ; Douville et al. 2000 ), surface soil moisture or surface emissivity (e.g., Heathman et al. 2003
2002 ; Yildiz and Barros 2007 ). Dynamic remote sensing vegetation data can be used to calculate not only actual evapotranspiration but also other water balance and energy balance components. The current study included only MODIS LAI time series data in the modified SIMHYD for estimating actual evapotranspiration. Another potential application is to incorporate LAI observations to estimate canopy interception, which depends strongly on LAI ( Zhang and Wegehenkel 2006 ). This study used only the
2002 ; Yildiz and Barros 2007 ). Dynamic remote sensing vegetation data can be used to calculate not only actual evapotranspiration but also other water balance and energy balance components. The current study included only MODIS LAI time series data in the modified SIMHYD for estimating actual evapotranspiration. Another potential application is to incorporate LAI observations to estimate canopy interception, which depends strongly on LAI ( Zhang and Wegehenkel 2006 ). This study used only the
