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Wunsch C. , 2007 : Global hydrographic variability and the data weights in oceanic state estimates . J. Phys. Oceanogr. , 37 , 1997 – 2008 , doi: 10.1175/JPO3072.1 . Hallberg, R. , 2013 : Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects . Ocean Modell. , 72 , 92 – 103 , doi: 10.1016/j.ocemod.2013.08.007 . Halpern, D. , Fukumori I. , Menemenlis D. , and Wang X. , 2013 : Long range Kelvin wave propagation of transport variations in the
Wunsch C. , 2007 : Global hydrographic variability and the data weights in oceanic state estimates . J. Phys. Oceanogr. , 37 , 1997 – 2008 , doi: 10.1175/JPO3072.1 . Hallberg, R. , 2013 : Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects . Ocean Modell. , 72 , 92 – 103 , doi: 10.1016/j.ocemod.2013.08.007 . Halpern, D. , Fukumori I. , Menemenlis D. , and Wang X. , 2013 : Long range Kelvin wave propagation of transport variations in the
1. Introduction Forecasting systems for short-range weather and ocean prediction have been run separately at the Met Office for many years with the weather forecasts using prescribed ocean surface temperatures and sea ice fields, and with the ocean forecasts using atmospheric forcing fields from the Met Office’s numerical weather prediction (NWP) system. It has long been known that coupling between the various earth system components (the ocean, atmosphere, sea ice, and land) produces improved
1. Introduction Forecasting systems for short-range weather and ocean prediction have been run separately at the Met Office for many years with the weather forecasts using prescribed ocean surface temperatures and sea ice fields, and with the ocean forecasts using atmospheric forcing fields from the Met Office’s numerical weather prediction (NWP) system. It has long been known that coupling between the various earth system components (the ocean, atmosphere, sea ice, and land) produces improved
1. Introduction Ocean data assimilation, which synthesizes observations and numerical models to obtain the statistically best estimate of the ocean state, has been widely used for various purposes such as ocean monitoring, forecast, and reanalysis (e.g., Bennett 1992 , 2002 ; Ghil and Malanotte-Rizzoli 1991 ; Wunsch 1996 ; Talagrand 1997 ; Lewis et al. 2006 ; Evensen 2007 ). The variational method is one of the major approaches in data assimilation. Based on the maximum likelihood
1. Introduction Ocean data assimilation, which synthesizes observations and numerical models to obtain the statistically best estimate of the ocean state, has been widely used for various purposes such as ocean monitoring, forecast, and reanalysis (e.g., Bennett 1992 , 2002 ; Ghil and Malanotte-Rizzoli 1991 ; Wunsch 1996 ; Talagrand 1997 ; Lewis et al. 2006 ; Evensen 2007 ). The variational method is one of the major approaches in data assimilation. Based on the maximum likelihood
integration, we use an element-based Galerkin approach, the spectral element method. Based on the interpolation rule using CSGEA in appendix B , we can discretize Eq. (5) as This is the discretized formulation of STCS based on the spectral element method. 3. Characteristics of STCS a. Dependency of the wave representation of a CSGEA on the element and the polynomial numbers The formulation of STCS, Eq. (6) , normally works when the SHFs discretized on a CSGEA , represent the analytic SHF well. To
integration, we use an element-based Galerkin approach, the spectral element method. Based on the interpolation rule using CSGEA in appendix B , we can discretize Eq. (5) as This is the discretized formulation of STCS based on the spectral element method. 3. Characteristics of STCS a. Dependency of the wave representation of a CSGEA on the element and the polynomial numbers The formulation of STCS, Eq. (6) , normally works when the SHFs discretized on a CSGEA , represent the analytic SHF well. To
numerical model. For example, initializing the model close to the observations and outside the model attractor can lead to a quasi-systematic excitation of model climate dynamics (i.e., the drift is projected onto the main climate internal modes, which is dissociated from the actual nature evolution intended to be captured). This is observed in experiments with Earth system models in which initialization of the Pacific Ocean nudged to reanalysis leads to an artificial sequence of El Niño/La Niña events
numerical model. For example, initializing the model close to the observations and outside the model attractor can lead to a quasi-systematic excitation of model climate dynamics (i.e., the drift is projected onto the main climate internal modes, which is dissociated from the actual nature evolution intended to be captured). This is observed in experiments with Earth system models in which initialization of the Pacific Ocean nudged to reanalysis leads to an artificial sequence of El Niño/La Niña events
, 2010 : Reconstruction of Hurricane Katrina’s wind fields for storm surge and wave hindcasting . Ocean Eng. , 37 , 26 – 36 , doi: 10.1016/j.oceaneng.2009.08.014 . Pu , Z. , and L. Zhang , 2010 : Validation of AIRS temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones . J. Geophys. Res. , 115 , D24114 , doi: 10.1029/2010JD014258 . Pu , Z. , W.-K. Tao , S. Braun , J. Simpson , Y. Jia , J. Halverson , W
, 2010 : Reconstruction of Hurricane Katrina’s wind fields for storm surge and wave hindcasting . Ocean Eng. , 37 , 26 – 36 , doi: 10.1016/j.oceaneng.2009.08.014 . Pu , Z. , and L. Zhang , 2010 : Validation of AIRS temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones . J. Geophys. Res. , 115 , D24114 , doi: 10.1029/2010JD014258 . Pu , Z. , W.-K. Tao , S. Braun , J. Simpson , Y. Jia , J. Halverson , W
front existed south of the Japanese islands. A short wave in the baiu front just south of the Kanto Plain (partially overlapping the experimental domain in Fig. 1 ) moved eastward while the baiu front moved southward until evening (not shown). However, these large-scale systems did not directly affect rainfall on the Kanto Plain. Upper sounding data observed at Tateno ( Fig. 2 ) show that the atmosphere below 700 hPa was very humid and that wind directions below 850 hPa were southerly to south
front existed south of the Japanese islands. A short wave in the baiu front just south of the Kanto Plain (partially overlapping the experimental domain in Fig. 1 ) moved eastward while the baiu front moved southward until evening (not shown). However, these large-scale systems did not directly affect rainfall on the Kanto Plain. Upper sounding data observed at Tateno ( Fig. 2 ) show that the atmosphere below 700 hPa was very humid and that wind directions below 850 hPa were southerly to south
and Wang 1999 ). A clear wave-2 structure appears in the mean difference between 24-h forecasts of 1000-hPa geopotential height from the two experiments ( Fig. 6a ), with centers of action in the Pacific and Indian Oceans that are in phase with the semidiurnal tide at the valid times of the forecasts (0000 and 1200 UTC). Geopotential heights from the experiment using 4DIAU are in better agreement with ERA-Interim than those from the digital filter–based experiment (cf. Figs. 6b and 6c ). Although
and Wang 1999 ). A clear wave-2 structure appears in the mean difference between 24-h forecasts of 1000-hPa geopotential height from the two experiments ( Fig. 6a ), with centers of action in the Pacific and Indian Oceans that are in phase with the semidiurnal tide at the valid times of the forecasts (0000 and 1200 UTC). Geopotential heights from the experiment using 4DIAU are in better agreement with ERA-Interim than those from the digital filter–based experiment (cf. Figs. 6b and 6c ). Although
1024 × 769, 70 levels rather than 640 × 481, 70 levels. For the first case we chose a region in the polar jet stream, located in the North Atlantic Ocean about 1500 km southeast of Newfoundland, Canada. The observation is located at level 29, which corresponds to a height of 5796 m and a pressure of about 450 hPa. The wind speed for this example is about 60 m s −1 . Figure 2 shows the background wind fields for the jet stream at 0300 ( Fig. 2a ) and 0900 UTC ( Fig. 2b ) 1 November 2011. The
1024 × 769, 70 levels rather than 640 × 481, 70 levels. For the first case we chose a region in the polar jet stream, located in the North Atlantic Ocean about 1500 km southeast of Newfoundland, Canada. The observation is located at level 29, which corresponds to a height of 5796 m and a pressure of about 450 hPa. The wind speed for this example is about 60 m s −1 . Figure 2 shows the background wind fields for the jet stream at 0300 ( Fig. 2a ) and 0900 UTC ( Fig. 2b ) 1 November 2011. The
, where the mean RMS error of the EnSRF jumped to a value of 8.0, an unrealistically large amplitude of the wave is visible for the EnSRF in the part of the domain, where no observations have been assimilated yet. The behavior is similar for the EnSRF bulk, but the RMS error remains smaller than for the serial EnSRF. In contrast, the LETKF estimates a wave of realistic amplitude. When the number of observations is further increased, the EnSRF and EnSRF bulk continue to estimate a state with a large
, where the mean RMS error of the EnSRF jumped to a value of 8.0, an unrealistically large amplitude of the wave is visible for the EnSRF in the part of the domain, where no observations have been assimilated yet. The behavior is similar for the EnSRF bulk, but the RMS error remains smaller than for the serial EnSRF. In contrast, the LETKF estimates a wave of realistic amplitude. When the number of observations is further increased, the EnSRF and EnSRF bulk continue to estimate a state with a large
