Search Results
November 2005 was chosen for this study because it contained a series of rainfall events with which to test the observation operators through wetting and drying cycles. 2) Land surface temperature The most common way of estimating land surface temperature from satellite brightness temperature observations is via a split-window algorithm (SWA; Yu et al. 2008 ). LST retrievals via SWAs (e.g., Becker and Li 1990 ; Sobrino et al. 1994; Wan and Dozier 1996 ) exploit the differential absorption of
November 2005 was chosen for this study because it contained a series of rainfall events with which to test the observation operators through wetting and drying cycles. 2) Land surface temperature The most common way of estimating land surface temperature from satellite brightness temperature observations is via a split-window algorithm (SWA; Yu et al. 2008 ). LST retrievals via SWAs (e.g., Becker and Li 1990 ; Sobrino et al. 1994; Wan and Dozier 1996 ) exploit the differential absorption of
the interaction of microwaves with the earth’s surface, retrieval methods have been mostly experimental and limited to certain climatic regions. One of the long-term global remotely sensed soil moisture datasets available today is the dataset derived from European Remote Sensing Satellites 1 and 2 ( ERS-1 ) and ( ERS-2 ) scatterometers (SCATs; coarse-resolution radar instruments with superior radiometric accuracy), using a soil moisture retrieval algorithm developed at the Vienna University of
the interaction of microwaves with the earth’s surface, retrieval methods have been mostly experimental and limited to certain climatic regions. One of the long-term global remotely sensed soil moisture datasets available today is the dataset derived from European Remote Sensing Satellites 1 and 2 ( ERS-1 ) and ( ERS-2 ) scatterometers (SCATs; coarse-resolution radar instruments with superior radiometric accuracy), using a soil moisture retrieval algorithm developed at the Vienna University of
-parameter lumped conceptual daily rainfall–runoff model that simulates daily runoff using daily precipitation and Priestley–Taylor potential evapotranspiration as input data ( Chiew et al. 2002 ). The structure of SIMHYD and the algorithms that describe water movement into and out of the storages are shown in Fig. 1 and Table 1 . SIMHYD has been extensively used for various applications across Australia ( Chiew and Siriwardena 2005 ; Chiew et al. 2002 ; Siriwardena et al. 2006 ; Viney et al. 2008
-parameter lumped conceptual daily rainfall–runoff model that simulates daily runoff using daily precipitation and Priestley–Taylor potential evapotranspiration as input data ( Chiew et al. 2002 ). The structure of SIMHYD and the algorithms that describe water movement into and out of the storages are shown in Fig. 1 and Table 1 . SIMHYD has been extensively used for various applications across Australia ( Chiew and Siriwardena 2005 ; Chiew et al. 2002 ; Siriwardena et al. 2006 ; Viney et al. 2008
moisture assimilation in a synthetic experiment. The multiscale Kalman filter ( Parada and Liang 2004 ; Kumar 1999 ) efficiently considers the spatial dependence and scaling properties of soil moisture. Parada and Liang (2004) applied a multiscale Kalman filter in conjunction with an expectation maximization algorithm to estimate temporally evolving statistical observation and model error parameters inherent to the Kalman filter to assimilate surface soil moisture in an LSM. Besides interest in the
moisture assimilation in a synthetic experiment. The multiscale Kalman filter ( Parada and Liang 2004 ; Kumar 1999 ) efficiently considers the spatial dependence and scaling properties of soil moisture. Parada and Liang (2004) applied a multiscale Kalman filter in conjunction with an expectation maximization algorithm to estimate temporally evolving statistical observation and model error parameters inherent to the Kalman filter to assimilate surface soil moisture in an LSM. Besides interest in the
river basins, and it can be reduced through a suitable bias-correction algorithm. For medium and small river basins, the medium-range forecasts suffer from considerable underdispersion, and improvement of the EPS strategy and downscaling of the atmospheric forcing may lead to improvement in ensemble streamflow forecasting. The grouping of the grid points into different categories of river basins based on the mean streamflow over the 2-month experimental period is somehow arbitrary, and the inferred
river basins, and it can be reduced through a suitable bias-correction algorithm. For medium and small river basins, the medium-range forecasts suffer from considerable underdispersion, and improvement of the EPS strategy and downscaling of the atmospheric forcing may lead to improvement in ensemble streamflow forecasting. The grouping of the grid points into different categories of river basins based on the mean streamflow over the 2-month experimental period is somehow arbitrary, and the inferred
such error include infiltration, the vertical percolation of soil water, and evapotraspiration. It should be stressed that the perturbation bias explained above will likely constitute only a fraction of the total model versus observation bias encountered in a land surface data assimilation problem. In general, the magnitude of other bias components—originating from measurement instrument features, soil moisture retrieval algorithm, and the bias in the land surface model itself—are difficult to
such error include infiltration, the vertical percolation of soil water, and evapotraspiration. It should be stressed that the perturbation bias explained above will likely constitute only a fraction of the total model versus observation bias encountered in a land surface data assimilation problem. In general, the magnitude of other bias components—originating from measurement instrument features, soil moisture retrieval algorithm, and the bias in the land surface model itself—are difficult to
physics, the skill will decrease rapidly with lead time. NWPs, on the other hand, capture the physics of large systems very well but lack local detail because of their limited spatial resolution and have imperfect assimilation algorithms. Therefore, their skill is not so high for small lead times but decreases only gradually with increasing lead time ( Golding 1998 ). Lin et al. (2005) investigated the crossover point in time where NWPs start to have more skill than nowcast methods and found this to
physics, the skill will decrease rapidly with lead time. NWPs, on the other hand, capture the physics of large systems very well but lack local detail because of their limited spatial resolution and have imperfect assimilation algorithms. Therefore, their skill is not so high for small lead times but decreases only gradually with increasing lead time ( Golding 1998 ). Lin et al. (2005) investigated the crossover point in time where NWPs start to have more skill than nowcast methods and found this to
