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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
. Grant, J. , Saleh-Contell K. , Wigneron J. , Guglielmetti M. , Kerr Y. H. , Schwank M. , Skou S. , and Van de Griend A. A. , 2008 : Calibration of the L-MEB model over a coniferous and deciduous forest . IEEE Trans. Geosci. Remote Sens. , 46 , 808 – 818 . Holmes, T. R. H. , Drusch M. , Wigneron J. P. , and Jeu R. A. M. D. , 2008 : A global simulation of microwave emission: Error structures based on output from ECMWF’s operational integrated forecast system . IEEE Trans
. Grant, J. , Saleh-Contell K. , Wigneron J. , Guglielmetti M. , Kerr Y. H. , Schwank M. , Skou S. , and Van de Griend A. A. , 2008 : Calibration of the L-MEB model over a coniferous and deciduous forest . IEEE Trans. Geosci. Remote Sens. , 46 , 808 – 818 . Holmes, T. R. H. , Drusch M. , Wigneron J. P. , and Jeu R. A. M. D. , 2008 : A global simulation of microwave emission: Error structures based on output from ECMWF’s operational integrated forecast system . IEEE Trans
.1139/l88-109 . Pianosi, F. , and Ravazzani G. , 2010 : Assessing rainfall-runoff models for the management of Lake Verbano . Hydrol. Processes , 24 , 3195 – 3205 , doi:10.1002/hyp.7745 . Rabuffetti, D. , Ravazzani G. , Corbari C. , and Mancini M. , 2008 : Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system—The AMPHORE case studies in the upper Po River . Nat. Hazards Earth Syst. Sci. , 8 , 161 – 173 , doi:10.5194/nhess-8
.1139/l88-109 . Pianosi, F. , and Ravazzani G. , 2010 : Assessing rainfall-runoff models for the management of Lake Verbano . Hydrol. Processes , 24 , 3195 – 3205 , doi:10.1002/hyp.7745 . Rabuffetti, D. , Ravazzani G. , Corbari C. , and Mancini M. , 2008 : Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system—The AMPHORE case studies in the upper Po River . Nat. Hazards Earth Syst. Sci. , 8 , 161 – 173 , doi:10.5194/nhess-8
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
