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members were combined with perturbed forcing data to determine the state ensemble in Eq. (3) : where is a vector of forecasted states at time t and is a vector of updated states for the previous time. The forcing data were perturbed by adding the noise γ t with covariance at each time step to generate its ensemble according to Eq. (4) : The JULES model was run forward in time to determine the ensemble predictions in Eq. (5) : where f 2 represents the JULES prediction. The observation
members were combined with perturbed forcing data to determine the state ensemble in Eq. (3) : where is a vector of forecasted states at time t and is a vector of updated states for the previous time. The forcing data were perturbed by adding the noise γ t with covariance at each time step to generate its ensemble according to Eq. (4) : The JULES model was run forward in time to determine the ensemble predictions in Eq. (5) : where f 2 represents the JULES prediction. The observation
. The complementary use of assimilation techniques can reduce uncertainty in the model forecast, considering that field data for additional snow variables are available. There are many examples of snow simulation using different assimilation techniques, from simple methods such as direct insertion (DI; Liston et al. 1999 ; Malik et al. 2012 ) to more complex methods such as those derived from applications of Kalman filter ( Kalman 1960 ). In this group, a wide range of methodologies are found: the
. The complementary use of assimilation techniques can reduce uncertainty in the model forecast, considering that field data for additional snow variables are available. There are many examples of snow simulation using different assimilation techniques, from simple methods such as direct insertion (DI; Liston et al. 1999 ; Malik et al. 2012 ) to more complex methods such as those derived from applications of Kalman filter ( Kalman 1960 ). In this group, a wide range of methodologies are found: the
. , and Coauthors , 2002 : Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data . Remote Sens. Environ. , 83 , 214 – 231 , doi:10.1016/S0034-4257(02)00074-3 . Nash, J. E. , and Sutcliffe J. V. , 1970 : River flow forecasting through the conceptual models part I—A discussion of principles . J. Hydrol. , 10 , 282 – 290 , doi:10.1016/0022-1694(70)90255-6 . Noilhan, J. , and Planton S. , 1989 : A simple parameterization of land surface
. , and Coauthors , 2002 : Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data . Remote Sens. Environ. , 83 , 214 – 231 , doi:10.1016/S0034-4257(02)00074-3 . Nash, J. E. , and Sutcliffe J. V. , 1970 : River flow forecasting through the conceptual models part I—A discussion of principles . J. Hydrol. , 10 , 282 – 290 , doi:10.1016/0022-1694(70)90255-6 . Noilhan, J. , and Planton S. , 1989 : A simple parameterization of land surface
; Saleh et al. 2007 ); and Lit3 is similar to Lit2, but with Nr p = 0 and with the soil roughness h as used in SMOS Tb monitoring with CMEM ( Sabater et al. 2011 ) at the European Centre for Medium-Range Weather Forecasts (ECMWF). These three sets of literature values are used in two ways. First, we simulate Tb using the literature values for the microwave RTM parameters and compare the results against SMOS observations. Second, the literature values are used as prior constraints in the parameter
; Saleh et al. 2007 ); and Lit3 is similar to Lit2, but with Nr p = 0 and with the soil roughness h as used in SMOS Tb monitoring with CMEM ( Sabater et al. 2011 ) at the European Centre for Medium-Range Weather Forecasts (ECMWF). These three sets of literature values are used in two ways. First, we simulate Tb using the literature values for the microwave RTM parameters and compare the results against SMOS observations. Second, the literature values are used as prior constraints in the parameter
and z 0h to calculate u * , θ * , and H cal from Eqs. (2a) – (2e) , 4) use u * and θ * to calculate kB −1 from kB −1 = ln( z 0m / z 0h ) according to each of the four z 0h schemes, and 5) repeat steps 2–4 until the cost function is minimized. c. Noah LSM The Noah LSM is widely used and forms the land component of the regional and global weather forecasting models at the National Centers for Environmental Prediction (NCEP) and of the Weather Research and Forecasting model (WRF
and z 0h to calculate u * , θ * , and H cal from Eqs. (2a) – (2e) , 4) use u * and θ * to calculate kB −1 from kB −1 = ln( z 0m / z 0h ) according to each of the four z 0h schemes, and 5) repeat steps 2–4 until the cost function is minimized. c. Noah LSM The Noah LSM is widely used and forms the land component of the regional and global weather forecasting models at the National Centers for Environmental Prediction (NCEP) and of the Weather Research and Forecasting model (WRF
respect to instantaneous and daily air temperature, the local lapse rates were calculated for the mountainous areas and integrated (based on a DEM) in the interpolation of air temperature data. Finally, the downwelling shortwave and longwave radiation flux ( R swd and R lwd ), boundary layer height, and dewpoint temperature at 2-m height were retrieved from the high-resolution gridded European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) dataset ( http
respect to instantaneous and daily air temperature, the local lapse rates were calculated for the mountainous areas and integrated (based on a DEM) in the interpolation of air temperature data. Finally, the downwelling shortwave and longwave radiation flux ( R swd and R lwd ), boundary layer height, and dewpoint temperature at 2-m height were retrieved from the high-resolution gridded European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) dataset ( http
