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derivative operator, and λ stands for a set of parameters. In most cases of interest these parameters are entering through phenomenological relations complementing (1) and providing information on quantities not expressible directly in terms of X such as dissipation rates, the effect of unresolved scales, and so forth. It is well known that for Eqs. (1) to constitute a well-posed problem it is necessary to prescribe appropriate initial and boundary conditions: where ∂Ω denotes the boundary of Ω
derivative operator, and λ stands for a set of parameters. In most cases of interest these parameters are entering through phenomenological relations complementing (1) and providing information on quantities not expressible directly in terms of X such as dissipation rates, the effect of unresolved scales, and so forth. It is well known that for Eqs. (1) to constitute a well-posed problem it is necessary to prescribe appropriate initial and boundary conditions: where ∂Ω denotes the boundary of Ω
initial and lateral boundary conditions (more discussion later) for the NFS. A version of NFS was also developed for the prediction of typhoons with a different domain and the inclusion of synthetic data to better represent the initial structure of a typhoon. This special-purpose forecast system runs only when a typhoon is in the vicinity of Taiwan. The TC forecast system using the NFS framework also improves the forecast track skill relative to the CWB typhoon forecast system based on the older
initial and lateral boundary conditions (more discussion later) for the NFS. A version of NFS was also developed for the prediction of typhoons with a different domain and the inclusion of synthetic data to better represent the initial structure of a typhoon. This special-purpose forecast system runs only when a typhoon is in the vicinity of Taiwan. The TC forecast system using the NFS framework also improves the forecast track skill relative to the CWB typhoon forecast system based on the older
International (ALADIN) model ( Bubnová et al. 1995 ), and the so-called spectral version of the HIRLAM model ( Haugen and Machenhauer 1993 ). But, because of the contradiction between a local specification of LBCs and the global character of the Fourier spectral discretization, designing stable and accurate well-posed boundary conditions is a daunting task. In Fourier spectral limited-area models, one of the most difficult questions is to combine the periodicity constraint for the Fourier expansion with
International (ALADIN) model ( Bubnová et al. 1995 ), and the so-called spectral version of the HIRLAM model ( Haugen and Machenhauer 1993 ). But, because of the contradiction between a local specification of LBCs and the global character of the Fourier spectral discretization, designing stable and accurate well-posed boundary conditions is a daunting task. In Fourier spectral limited-area models, one of the most difficult questions is to combine the periodicity constraint for the Fourier expansion with
the atmospheric model remains global, although it is focused on the region of interest. The required boundary conditions that differ between the present and future model runs are then essentially the atmospheric composition (in particular greenhouse gas concentrations) and sea surface conditions (SSC), that is, sea surface temperature (SST) and sea ice concentration (SIC). There are two basic methods of prescribing present and future sea surface conditions. The first method consists in using
the atmospheric model remains global, although it is focused on the region of interest. The required boundary conditions that differ between the present and future model runs are then essentially the atmospheric composition (in particular greenhouse gas concentrations) and sea surface conditions (SSC), that is, sea surface temperature (SST) and sea ice concentration (SIC). There are two basic methods of prescribing present and future sea surface conditions. The first method consists in using
insight into the climate response to different forcing can be gained from time-slice experiments using constant boundary conditions ( Kutzbach and Otto-Bliesner 1982 ; Broccoli and Manabe 1987 ; Kutzbach and Liu 1997 ; Joussaume et al. 1999 ; Pinot et al. 1999 ; Hostetler and Mix 1999 ; Shin et al. 2003 ; Kim et al. 2003 ; Timmermann et al. 2004 ; Peltier and Solheim 2004 ; Renssen et al. 2004 ; Otto-Bliesner et al. 2006 ), the nonequilibrated (transient) climate trajectories are difficult
insight into the climate response to different forcing can be gained from time-slice experiments using constant boundary conditions ( Kutzbach and Otto-Bliesner 1982 ; Broccoli and Manabe 1987 ; Kutzbach and Liu 1997 ; Joussaume et al. 1999 ; Pinot et al. 1999 ; Hostetler and Mix 1999 ; Shin et al. 2003 ; Kim et al. 2003 ; Timmermann et al. 2004 ; Peltier and Solheim 2004 ; Renssen et al. 2004 ; Otto-Bliesner et al. 2006 ), the nonequilibrated (transient) climate trajectories are difficult
1. Introduction In coastal ocean modeling, open boundary conditions (OBCs) have a crucial impact on the inner domain solution. This is largely due to the fact that time scales associated with the propagation of waves throughout the coastal area are comparable to the length of the simulation itself, when they are not much shorter. A barotropic wave can, for instance, cross a 100-m-deep and 100-km-long shelf in about 1 h and a 1 m s −1 internal wave can cross it in about 1 day. This is much
1. Introduction In coastal ocean modeling, open boundary conditions (OBCs) have a crucial impact on the inner domain solution. This is largely due to the fact that time scales associated with the propagation of waves throughout the coastal area are comparable to the length of the simulation itself, when they are not much shorter. A barotropic wave can, for instance, cross a 100-m-deep and 100-km-long shelf in about 1 h and a 1 m s −1 internal wave can cross it in about 1 day. This is much
Inversion of Tidal Open Boundary Conditions of the M2 Constituent in the Bohai and Yellow Seas). Treatment of open boundary conditions (OBCs), one of the most important control parameters in the tidal model, remains a great challenge for numerical simulation of tides and tidal currents ( Lardner et al. 1993 ). Since the 1990s, the adjoint method has been widely implemented in the inversion of OBCs. Lardner et al. (1993) achieved effective control of the OBCs in a depth-averaged numerical tidal model by data assimilation. Seiler (1993) successfully estimated the OBCs for a quasigeostrophic ocean
). Treatment of open boundary conditions (OBCs), one of the most important control parameters in the tidal model, remains a great challenge for numerical simulation of tides and tidal currents ( Lardner et al. 1993 ). Since the 1990s, the adjoint method has been widely implemented in the inversion of OBCs. Lardner et al. (1993) achieved effective control of the OBCs in a depth-averaged numerical tidal model by data assimilation. Seiler (1993) successfully estimated the OBCs for a quasigeostrophic ocean
. As physical parameterizations at smaller scales show stronger nonlinearities, convective-scale perturbations grow much faster and even impact the large-scale predictability. The sensitivity to ICs ( Ducrocq et al. 2002 ) is also different between parameterized and resolved convection. Moreover, in addition to the uncertainties on ICs and the model errors, cloud-resolving ensembles must also consider the uncertainty due to lateral boundary conditions (LBCs), since they are run over a limited area
. As physical parameterizations at smaller scales show stronger nonlinearities, convective-scale perturbations grow much faster and even impact the large-scale predictability. The sensitivity to ICs ( Ducrocq et al. 2002 ) is also different between parameterized and resolved convection. Moreover, in addition to the uncertainties on ICs and the model errors, cloud-resolving ensembles must also consider the uncertainty due to lateral boundary conditions (LBCs), since they are run over a limited area
to achieve operationally adequate forecasts of propagation conditions. Atkinson et al. (2001) then simulated the propagation environment in the atmosphere boundary layer over the Persian Gulf using a mesoscale model from the Met Office ( Golding 1987 , 1990 ) with a horizontal resolution of 6 km and examined the effects of initializing the model in varying degrees of idealization with the observations taken during the SHAREM-115 experiment ( Brooks and Rogers 2000 ). The existence, location
to achieve operationally adequate forecasts of propagation conditions. Atkinson et al. (2001) then simulated the propagation environment in the atmosphere boundary layer over the Persian Gulf using a mesoscale model from the Met Office ( Golding 1987 , 1990 ) with a horizontal resolution of 6 km and examined the effects of initializing the model in varying degrees of idealization with the observations taken during the SHAREM-115 experiment ( Brooks and Rogers 2000 ). The existence, location
Effect of Boundary Conditions on Adjoint-Based Forecast Sensitivity Observation Impact in a Regional Model, the boundary conditions can affect the forecast results in the regional model. Wang et al. (2010) investigated the effect of lateral boundary-condition configurations (i.e., nudging coefficient and buffer zone size) on the track forecasts of Typhoon Winnie (1997) in the regional climate model. Kumar et al. (2011) demonstrated that the model domain size affected the intensity and track forecast of Tropical Cyclone Sidr (2007) in the Bay of Bengal, in the high-resolution regional model. To
, the boundary conditions can affect the forecast results in the regional model. Wang et al. (2010) investigated the effect of lateral boundary-condition configurations (i.e., nudging coefficient and buffer zone size) on the track forecasts of Typhoon Winnie (1997) in the regional climate model. Kumar et al. (2011) demonstrated that the model domain size affected the intensity and track forecast of Tropical Cyclone Sidr (2007) in the Bay of Bengal, in the high-resolution regional model. To
