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- Author or Editor: David Hall x
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Abstract
A discontinuous Galerkin (DG) transport scheme is presented that employs the Yin–Yang grid on the sphere. The Yin–Yang grid is a quasi-uniform overset mesh comprising two notched latitude–longitude meshes placed at right angles to each other. Surface fluxes of conserved scalars are obtained at the overset boundaries by interpolation from the interior of the elements on the complimentary grid, using high-order polynomial interpolation intrinsic to the DG technique. A series of standard tests are applied to evaluate its performance, revealing it to be robust and its accuracy to be competitive with other global advection schemes at equivalent resolutions. Under p-type grid refinement, the DG Yin–Yang method exhibits spectral error convergence for smooth initial conditions and third-order geometric convergence for C 1 continuous functions. In comparison with finite-volume implementations of the Yin–Yang mesh, the DG implementation is less complex, as it does not require a wide halo region of elements for accurate boundary value interpolation. With respect to DG cubed-sphere implementations, the Yin–Yang grid exhibits similar accuracy and appears to be a viable alternative suitable for global advective transport. A variant called the Yin–Yang polar (YY-P) mesh is also examined and is shown to have properties similar to the original Yin–Yang mesh while performing better on tests with strictly zonal flow.
Abstract
A discontinuous Galerkin (DG) transport scheme is presented that employs the Yin–Yang grid on the sphere. The Yin–Yang grid is a quasi-uniform overset mesh comprising two notched latitude–longitude meshes placed at right angles to each other. Surface fluxes of conserved scalars are obtained at the overset boundaries by interpolation from the interior of the elements on the complimentary grid, using high-order polynomial interpolation intrinsic to the DG technique. A series of standard tests are applied to evaluate its performance, revealing it to be robust and its accuracy to be competitive with other global advection schemes at equivalent resolutions. Under p-type grid refinement, the DG Yin–Yang method exhibits spectral error convergence for smooth initial conditions and third-order geometric convergence for C 1 continuous functions. In comparison with finite-volume implementations of the Yin–Yang mesh, the DG implementation is less complex, as it does not require a wide halo region of elements for accurate boundary value interpolation. With respect to DG cubed-sphere implementations, the Yin–Yang grid exhibits similar accuracy and appears to be a viable alternative suitable for global advective transport. A variant called the Yin–Yang polar (YY-P) mesh is also examined and is shown to have properties similar to the original Yin–Yang mesh while performing better on tests with strictly zonal flow.
Abstract
The observed relationship between tropical cyclone activity in the Australian region and the Madden–Julian oscillation (MJO) has been examined using 20 yr of outgoing longwave radiation, NCEP–NCAR reanalysis, and best track tropical cyclone data. The MJO strongly modulates the climatological pattern of cyclogenesis in the Australian region, where significantly more (fewer) cyclones form in the active (inactive) phase of the MJO. This modulation is more pronounced to the northwest of Australia. The relationship between tropical cyclone activity and the MJO was strengthened during El Niño periods. Variations of the large-scale dynamical conditions necessary for cyclogenesis were explored, and it was found that MJO-induced perturbations of these parameters correspond with the observed variation in cyclone activity. In particular, 850-hPa relative vorticity anomalies attributable to the MJO were found to be an excellent diagnostic of the changes in the large-scale cyclogenesis patterns.
Abstract
The observed relationship between tropical cyclone activity in the Australian region and the Madden–Julian oscillation (MJO) has been examined using 20 yr of outgoing longwave radiation, NCEP–NCAR reanalysis, and best track tropical cyclone data. The MJO strongly modulates the climatological pattern of cyclogenesis in the Australian region, where significantly more (fewer) cyclones form in the active (inactive) phase of the MJO. This modulation is more pronounced to the northwest of Australia. The relationship between tropical cyclone activity and the MJO was strengthened during El Niño periods. Variations of the large-scale dynamical conditions necessary for cyclogenesis were explored, and it was found that MJO-induced perturbations of these parameters correspond with the observed variation in cyclone activity. In particular, 850-hPa relative vorticity anomalies attributable to the MJO were found to be an excellent diagnostic of the changes in the large-scale cyclogenesis patterns.
Abstract
The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.
Abstract
The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.
Abstract
The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction. In the fall of 1999, OMEGA was used for the first time to examine the structure and evolution of a hurricane (Floyd, 1999). The first simulation of Floyd was conducted in an operational forecast mode; additional simulations exploiting both the static as well as the dynamic grid adaptation options in OMEGA were performed later as part of a sensitivity–capability study. While a horizontal grid resolution ranging from about 120 km down to about 40 km was employed in the operational run, resolutions down to about 15 km were used in the sensitivity study to explicitly model the structure of the inner core. All the simulations produced very similar storm tracks and reproduced the salient features of the observed storm such as the recurvature off the Florida coast with an average 48-h position error of 65 km. In addition, OMEGA predicted the landfall near Cape Fear, North Carolina, with an accuracy of less than 100 km up to 96 h in advance. It was found that a higher resolution in the eyewall region of the hurricane, provided by dynamic adaptation, was capable of generating better-organized cloud and flow fields and a well-defined eye with a central pressure lower than the environment by roughly 50 mb. Since that time, forecasts were performed for a number of other storms including Georges (1998) and six 2000 storms (Tropical Storms Beryl and Chris, Hurricanes Debby and Florence, Tropical Storm Helene, and Typhoon Xangsane). The OMEGA mean track error for all of these forecasts of 101, 140, and 298 km at 24, 48, and 72 h, respectively, represents a significant improvement over the National Hurricane Center (NHC) 1998 average of 156, 268, and 374 km, respectively. In a direct comparison with the GFDL model, OMEGA started with a considerably larger position error yet came within 5% of the GFDL 72-h track error. This paper details the simulations produced and documents the results, including a comparison of the OMEGA forecasts against satellite data, observed tracks, reported pressure lows and maximum wind speed, and the rainfall distribution over land.
Abstract
The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction. In the fall of 1999, OMEGA was used for the first time to examine the structure and evolution of a hurricane (Floyd, 1999). The first simulation of Floyd was conducted in an operational forecast mode; additional simulations exploiting both the static as well as the dynamic grid adaptation options in OMEGA were performed later as part of a sensitivity–capability study. While a horizontal grid resolution ranging from about 120 km down to about 40 km was employed in the operational run, resolutions down to about 15 km were used in the sensitivity study to explicitly model the structure of the inner core. All the simulations produced very similar storm tracks and reproduced the salient features of the observed storm such as the recurvature off the Florida coast with an average 48-h position error of 65 km. In addition, OMEGA predicted the landfall near Cape Fear, North Carolina, with an accuracy of less than 100 km up to 96 h in advance. It was found that a higher resolution in the eyewall region of the hurricane, provided by dynamic adaptation, was capable of generating better-organized cloud and flow fields and a well-defined eye with a central pressure lower than the environment by roughly 50 mb. Since that time, forecasts were performed for a number of other storms including Georges (1998) and six 2000 storms (Tropical Storms Beryl and Chris, Hurricanes Debby and Florence, Tropical Storm Helene, and Typhoon Xangsane). The OMEGA mean track error for all of these forecasts of 101, 140, and 298 km at 24, 48, and 72 h, respectively, represents a significant improvement over the National Hurricane Center (NHC) 1998 average of 156, 268, and 374 km, respectively. In a direct comparison with the GFDL model, OMEGA started with a considerably larger position error yet came within 5% of the GFDL 72-h track error. This paper details the simulations produced and documents the results, including a comparison of the OMEGA forecasts against satellite data, observed tracks, reported pressure lows and maximum wind speed, and the rainfall distribution over land.
