An Operational Multiscale Hurricane Forecasting System

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.

Horizontal grids for global weather and climate prediction models: a review

“…Atmospheric models have also been developed using unstructured triangular and pentagonal-hexagonal grids (e.g. Bacon et al, 2000;Gopalakrishnan et al, 2002;Ford et al, 2004a,b;Pain et al, 2005;Piggott et al, 2005;Läuter et al, 2007;Weller and Weller, 2008;Bernard et al, 2009b;Weller et al, 2009Weller et al, , 2010Ringler et al, 2011) and are similar in many ways to those for oceanic applications. An important virtue of unstructured grids is their ability to match a grid to awkward-shaped boundaries, such as the land boundaries of oceanic models.…”

Targeted GOES Satellite Observations to Improve Hurricane Track Forecast: A Case Study of Hurricane Floyd

“…A complete description of OMEGA can be found in BACON et al (2000), while the details of the model validation studies can be found in BOYBEYI et al (2001) and GOPALAKRISHNAN et al (2002). OMEGA is a fully non-hydrostatic, three-dimensional prognostic model based on an adaptive, unstructured triangular prism grid that is referenced to a rotating Cartesian coordinate system.…”

A Godunov-Type Scheme for Atmospheric Flows on Unstructured Grids: Scalar Transport

“…For example, BACON et al (2000) have used unstructured meshes with both static and dynamic grid adaptation for atmospheric modeling in a fully compressible and nonhydrostatic model -the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). The OMEGA model is based on unstructured prisms and has been applied to many atmospheric problems and validated extensively (e.g., BOYBEYI et al, 2001;GOPALAKRISHNAN et al, 2002). SARMA et al (1999) have demonstrated significant improvement in solution accuracy by using dynamic grid adaptation for predicting chemical plume concentrations.…”

“…Solution-adaptive techniques (i.e., dynamic grid adaptation) can also be implemented with relative ease to improve the solution by dynamically adapting the mesh to evolving physical features. Solution-adaptive methods can be helpful, for example, in predicting the trajectory of hazardous materials (AHMAD et al, 1998;SARMA et al, 1999;GHORAI et al, 2000), simulations of convective weather systems such as squall lines and mesoscale convective complexes, and for tracking hurricanes (GOPALAKRISHNAN et al, 2002).…”