The Euler equations are solved for non-hydrostatic atmospheric flow problems in two dimensions using the Conservation Laws Package (CLAWPACK) with adaptive mesh refinement (AMR). The CLAWPACK software uses high-resolution wave propagation methods (LeVeque 2002) for solving hyperbolic conservation laws. In the current study, the Riemann problem is solved using flux-based wave decomposition (Bale et al. 2002; LeVeque 2002; Ahmad and Lindeman 2007). The computational efficiency achieved by using adaptive mesh refinement is demonstrated. The methodology shows promise for simulating multi-scale, time-critical and computationally intensive flow problems and flows which are dominated by large gradients of velocities and other thermodynamic quantities. A decrease in computational resources while maintaining the accuracy of the solution has obvious benefits in responding to emergency-response scenarios, such as dispersion of hazardous materials in the atmosphere.

*† ‡ § The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is used to simulate an episode of the Santa Ana winds over Southern California. The OMEGA model is based on an unstructured adaptive grid. The use of an unstructured gird allows the model to resolve complex terrain features with a good degree of accuracy and is ideally suited to simulate atmospheric flows which develop due to topographic forcing. This paper describes in detail simulations using the OMEGA model for an episode of the Santa Ana winds during October 2007. The wind and turbulence fields obtained from OMEGA were used to drive the Atmospheric Dispersion Model (ADM). The results of the ADM simulation of the Santa Ana fires are also reported.

*† ‡ § ** †† A computationally efficient acoustic ray-tracing code based on unstructured adaptive grids is described in detail. The use of unstructured grids allows wave propagation simulations on complex computational domains. Detailed surface layer physics is included to take into account the atmospheric variability under different stability conditions and landuse inhomogeneities. The ray-tracing model has been coupled with meso- and microscale atmospheric flow models. The method shows promise in simulating long-range acoustic wave propagation in the atmosphere over complex terrain.

By definition, a crisis is a situation that requires assistance to be managed. Hence, response to a crisis involves the merging of local and non-local emergency response personnel. In this situation, it is critical that each participant: (1) know the roles and responsibilities of each of the other participants; (2) know the capabilities of each of the participants; and (3) have a common basis for action. For many types of natural disasters, this entails having a common operational picture of the unfolding events, including detailed information on the weather, both current and forecasted, that may impact on either the emergency itself or on response activities. The Consequences Assessment Tool Set (CATS) is a comprehensive package of hazard prediction models and casualty and damage assessment tools that provides a linkage between a modeled or observed effect and the attendant consequences for populations, infrastructure, and resources, and, hence, provides the common operational picture for emergency response. 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 to provide specific weather analysis and forecast capability that can be merged into the geographic information system framework of CATS. This paper documents the problem of emergency response as an end-to-end system and presents the integrated CATS–OMEGA system as a prototype of such a system that has been used successfully in a number of different situations.

This study performs a set of Observing System Simulation Experiments (OSSEs) using Geostationary Operational Environmental Satellite (GOES) soundings. The primary objective of the OSSEs is to demonstrate that targeted observations can improve forecast accuracy by enhancing the initial conditions and mitigating their uncertainties. Hurricane Floyd (1999) is chosen as a study case. The main reason for choosing hurricane Floyd as a test case is that the movement of the storm was dictated by a mid-level complex polar jet steering flow region. This well-defined feature allowed us to examine the inaccuracy of analysis over the steering flow area using GOES soundings as targeted observations and its impact on the forecast track error. The set of experiments starts from a baseline forecast of hurricane Floyd using the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). From GOES satellite soundings, atmospheric vertical profiles were extracted to simulate targeted observations. These data extracts were assimilated in the initial conditions to simulate new forecasts of hurricane Floyd which were then compared against both the baseline track and observed track. It was found that targeted observations in a forecast sensitive area can help to reduce hurricane forecast track error. Assimilation of only the subset of data (about 50 soundings) from the subjectively chosen fully sampled target region produced a considerable reduction of the track forecast errors (about 30%) within the first critical three days of the forecast.

Multidimensional positive definite advection transport algorithm (MPDATA) was proposed in the early eighties as a simple positive‐definite advection scheme with small implicit diffusion, for evaluating the advection of water‐substance constituents in atmospheric cloud models. Over the two decades, MPDATA evolved from an advection scheme into a class of generalized transport algorithms that expand beyond advective transport to alternate PDEs and complete fluid models with a wide range of underlying governing equations. Recently, MPDATA has attracted attention in the context of several mutually‐beneficial developments such as (i) quantification of MPDATA implicit turbulence modelling capability in the spirit of monotonically integrated large eddy simulations (MILES), (ii) extensions to flow solvers cast in generalized time‐dependent curvilinear coordinates, and (iii) unstructured‐grid formulations. The aim of this paper is to assist the special issue on MPDATA methods for fluids by providing an up to date comprehensive review of the approach, including the underlying concepts, principles of implementation, and guidance to the accumulated literature. Copyright © 2005 John Wiley & Sons, Ltd.

In the early days of computing, geophysical fluid dynamics (GFD), predominantly numerical weather prediction (NWP), was a dominant factor in the design of computer architecture and algorithms. This early work focused initially on finite difference algorithms on rectangular computational grids and later on spectral methods. After the initial work of von Neumann, Charney, and Arakawa however, the focus shifted from the basic algorithms to improvements in the physical models. Further work on fundamental numerical algorithms shifted to other disciplines-predominately the then emerging aerospace community. As a result, for 40 years, the GFD community has been using numerical techniques that are virtually unchanged. The two primary numerical methodologies that have been used for modeling the atmosphere and the ocean have been spectral methods for global modeling and structured rectilinear grids for regional modeling. In 1992, work began on the Operational Multiscale Environment model with Grid Adaptivity (OMEGA), a new atmospheric simulation tool based upon an adaptive unstructured grid. Over the past 14 years, this model has found application to atmospheric dispersion, point weather forecasting, and, the topic of this paper, hurricane track forecasting. The unstructured grid paradigm employed in OMEGA has the advantage of flexibility in providing high resolution where required by either static physical properties (terrain elevation, coastlines, land use) or the changing dynamical situation. This paper provides a description of this new paradigm and presents its use in atmospheric simulation.

An article essentially consisting of one or more of Ti-Al intermetallic compounds is fabricated so as to have a volume ratio of voids of 0.2 to 3.5% and a maximum size of voids less than 50 mu m, by preparing a mixture of materials selected from a group consisting of Ti, Ti alloys, Al, Al alloys, and Ti-Al compounds, having a composition suitable for forming a desired Ti-Al intermetallic compound, and heating the mixture so that the mixture may be sintered. Typically, the temperature and pressure for the heating or sintering process, and the particle size of the material are appropriately selected so that a desired porosity and a desired range of void sizes may be obtained. The mechanical strength of the article according to the present invention is not only improved (as compared to the conventional Ti-Al intermetallic compounds) but is highly predictable, or, in other words, highly reliable. The fabrication costs can be reduced because the fabrication process involves only relatively low temperatures when simultaneously pressing and heating the article being formed. Furthermore, during the process of fabrication, such an article may have a highly favorable workability (as compared to the prior art) so that a final shape can be given to the article without involving any undue difficulty.

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.

Weather forecasting in and around Antarctica is notoriously difficult. This arises in part because of the failure of current models to include the important orographic and coastal features of the continent and problems with basic surface and boundary layer physics and cloud processes. The uncertainty in model forecasts presents logistical problems for the United States Antarctic Program (USAP), including cancelled and aborted flight operations. It has also contributed to an increased risk for USAP personnel both in deployment phase and in the ability to mount rapid recovery operations in an emergency. The Ross Island Meteorology Experiment is proposed to improve our basic understanding of the meteorology of the Ross Sea region. The Ross Sea area is considered to be a representative region for studies of physical processes and of moisture, momentum, and energy fluxes, hence the knowledge gained on RIME will improve our modeling for the entire continent of Antarctica. A model is the instantiation of our understanding of the physical processes of the system the model purports to represent. To the extent that a model agrees with observations, it represents a confirmation of the physical understanding and the techniques used to simulate the physical processes encapsulated in the model. To the extent that a model disagrees with observations, it represents evidence that either the physical understanding is lacking or the techniques used to simulate the physical processes are inappropriate or both. In the case of Antarctica, it is easy to document that the techniques used to date are not sufficient to the task; it is also quite evident that our physical understanding is incomplete. The weather of Antarctica is dominated by three processes: (1) the polar high and the baroclinic waves that circumnavigate the continent leading to incursions into the continental landmass; (2) terrain forcing as the synoptic circulation interacts with the steep terrain and coastal and ice boundaries; and (3) katabatic processes that arise from strong radiative cooling of the surface and the downslope acceleration of the flow. These processes then couple with the moisture and surface features leading to rapid degradation in ceiling and visibility and high wind situations that preclude air operations. This has a severe impact on the science mission of the USAP due to the cancellation of flight operations, or worse the aborting of an operation in process. Weather forecasting, however, involves a system of systems, each of which must be considered in order to improve forecast skill. The system of systems includes: (1) data acquisition; (2) data ingest, quality control, and assimilation; (3) physical modeling; (4) visualization; and (5) results dissemination. Only via a balanced approach will it be possible to improve forecasting for operations in Antarctica. Several groups are proposing new observation systems (ATOVS, COSMIC) that will add much needed data to the system, but this new data must be ingested, quality controlled, and assimilated into the forecasting systems. RIME has the potential not only to add to our understanding of the basic physical processes but to also provide ground truth to verify some of the remotely sensed data that is soon to be collected. Finally, RIME could serve as a testbed for testing new operational forecasting concepts. This could include a distributed modeling concept in which local computing resources are used to provide operational numerical weather prediction and / or high bandwidth communications channels could be used to provide a reach-back capability to enable remote resources to provide support. This paper discusses the physical modeling aspects of RIME (surface properties, air-surface fluxes, multiscale dynamics, thermodynamics, and microphysics), on the required datasets for model *Corresponding author address: David P. Bacon, Center for Atmospheric Physics, Science Applications International Corporation, 1710 SAIC Dr., McLean, VA 22102; e-mail: david.p.bacon@saic.com

Over the past 40 years there have been significant improvements in weather forecasting. These improvements are primarily due to (1) improved model physics and increased numerical grid resolution made possible by ever-increasing computational power, and (2) improved model initialization made possible by the use of satellite-derived remotely sensed data. In spite of these improvements, however, we are still not able to consistently and accurately forecast some of the most complex nonlinear diabatic mesoscale phenomena, such as propagating tropical mesoscale convective systems/cloud clusters, tropical storms, and intense extratropical storms. These phenomena develop over very fine spatial scales of motion and temporal periods and are dependent on convection for their existence. Poor observations of convection, boundary layer dynamics, and the larger scale pre-convective environment are often the cause of these substandard simulations and thus require improved observational data density and numerical forecast grid resolution. This paper performs a set of Observing System Simulation Experiments (OSSE). The objective of the OSSE experiments is to demonstrate that an adaptive (targeted) observational strategy can improve forecast accuracy over existing more conventional observational strategies in terms of enhancing the initial conditions and subsequent accuracy of the simulations of a numerical weather prediction model. For the proof of this concept, hurricane Floyd (1999) is chosen as a test case. The set of experiments starts from a baseline high-resolution forecast of hurricane Floyd using the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). This baseline run serves as the truth set for the OSSE under a “perfect model” assumption. From the baseline run, atmospheric vertical profiles were extracted to simulate “pseudo-observations” using different adaptive strategies. These data extracts were used to create new coarse-resolution forecasts of hurricane Floyd that were then compared against the both baseline and real atmospheric observations. In general, the experiments show that additional adaptive observations in sensitive areas can help to reduce hurricane forecast errors significantly from a Numerical Weather Prediction (NWP) model.

The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is a new atmospheric simulation system that merges state-of-the-art computational fluid dynamics techniques with a comprehensive non-hydrostatic equation set. Based upon an unstructured triangular prism grid, OMEGA permits simulation of hazardous releases at all scales with horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the planetary boundary layer to 1 km in the free troposphere.

The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) is a multiscale nonhydrostatic atmospheric simulation system based on an adaptive unstructured grid. The basic philosophy behind the OMEGA development has been the creation of an operational tool for real-time aerosol and gas hazard prediction. The model development has been guided by two basic design considerations in order to meet the operational requirements: 1) the application of an unstructured dynamically adaptive mesh numerical technique to atmospheric simulation, and 2) the use of embedded atmospheric dispersion algorithms. An important step in proving the utility and accuracy of OMEGA is the full-scale testing of the model using simulations of real-world atmospheric events and qualitative as well as quantitative comparisons of the model results with observations. The main objective of this paper is to provide a comprehensive evaluation of OMEGA against a major dispersion experiment in operational mode. Therefore, OMEGA was run to create a 72-h forecast for the first release period (23-26 October 1994) of the European Tracer Experiment (ETEX). The predicted meteorological and dispersion fields were then evaluated against both the atmospheric observations and the ETEX dispersion measurements up to 60 h after the start of the release. In general, the evaluation showed that the OMEGA dispersion results were in good agreement in the position, shape, and extent of the tracer cloud. However, the model prediction indicated that there was a limited spreading of the predictions around the measurements with a small tendency to underestimate the concentration values.