Operational Multiscale Environment Model Research Articles

Investigation of local flow features in Istanbul. Part I: high‐resolution sensitivity simulations

ABSTRACTThe three‐dimensional non‐hydrostatic mesoscale model, OMEGA (Operational Multiscale Environment model with Grid Adaptivity), is utilized to investigate the thermally driven local flows and their interaction with each other over the province of Istanbul, Turkey. Idealized case simulations are conducted in order to describe the contribution of sea–land breezes and urban heat island (UHI) circulation to the local flow over the city. The city of Istanbul is located between two water bodies, Black Sea in the north and Sea of Marmara in the south. A convergence zone is observed at about 1000 LST over the region due to the merge of two sea breezes that develop at both sides and advance towards inland areas. Investigation further indicates that the other geographic features of the city have substantial effects on the local flow. The Bosphorus Strait, which connects Black Sea to Sea of Marmara and divides the city into two parts, channels the flow. Although not significantly high and steep, the topography enhances the flow strength. Besides, urbanized regions in the south, by generating UHI circulation, prevent the inland penetration of the sea breeze that develops in the south. The numerical simulations reveal that the large‐scale wind direction controls the inland penetration of the sea breeze. One of the outstanding features of the local flows is that all hypothetical simulations including the Bosphorus indicate a strong fan‐like airflow in the immediate south of the strait. The flow is more intense in the northerly prevailing wind case. It is also found that stronger large‐scale flows (e.g. 2.5 and 10 m s−1) do not allow the formation of a sea breeze over the region. A warmer sea surface than land surface (about 2 °C) makes changes in the duration and extension of the land–sea breeze circulation. The duration of a land breeze circulation is long, and the southerly sea breeze is onset close to the south coast under the northeasterly flow conditions.

Modeling Urban Atmospheric Anthrax Spores Dispersion: Assessment of Health Impacts and Policy Implications

AbstractPurposeTo model atmospheric dispersion of weapon grade anthrax spores over an urban region using a mass transit system (subway) as a delivery system. IntroductionThe accessibility and carrying capacity of mass transit system (s) makes it an attractive target for terrorists. There are multiple, historical accounts of terrorists attacks involving transit systems. Metropolitan underground transportation rail systems (subways) are of special interest because of a potential harm to a large number of commuters and damage to the transportation infrastructure. MethodsWe assumed that multiple release points of anthrax spores within the National Capital Region (NCR‐Washington, District of Columbia) METRO rail system will result in the escape of the spores into the atmosphere. Potential factors affecting atmospheric dispersion of spores and the regional infrastructure were the subject of literature searches, modeling exercises and expert reviews. A Geographic Information System (GIS) and the Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) were used to model the affected area. ResultsThe GIS together with the OMEGA software programs characterized the extent of the ground footprint and provided the number of the NCR residential, businesses and health care infrastructure under the plume at 120 minutes after the release. It was concluded that the maximum environmental and health impacts would occur if the spores were released in the early morning during the summer months (low humidity and ultra‐violet radiation). This assumption was based on the analysis of the weather patterns and prevailing winds in the NCR. ConclusionsThe atmospheric dispersion model superimposed over the regional GIS suggests that the extent of the affected infrastructure will be negligible. However it has the potential to disrupt the functioning of the mass transit system, affect serviced residential areas, commercial and/or federal facilities. While the number of affected medical and other facilities will be minimal, a significant number of commuters and resident will be exposed to the spores and overload the existing health care infrastructure. Policy implicationsAdopting a set of realistic threats assessment tools using GIS and atmospheric modeling, and addressing risk reduction strategies such as deploying real time biological agents monitoring sensors, can significantly improve the security of metropolitan communities served by interconnected mass transport systems. Field testing to validate our modeling assumptions and results will help with the training of disaster response teams, health care providers, transportation system operators and improve passengers' safety. Limitations This study has not been validated by actual field test data and as such is hypothetical and subject to a significant bias. Assumption of a release of a large amount (several kilograms) of anthrax spores undetected might be unrealistic based on existing physical surveillance and security practices. Patterns of spores' dispersion into fixed structures serviced by the subway system were not modeled in this research. The dispersion model does not take into account the progressive reduction of the number of anthrax spores as a function of the distance from the release source.

An operational multiscale system for hazards prediction, mapping, and response

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.

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

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.

Hurricane track forecasting with OMEGA

In the early days of computing, geophysical fluid dynamics (GFD), predominately 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 mod- eling 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.

Application of the multidimensional positive definite advection transport algorithm (MPDATA) to environmental modelling on adaptive unstructured grids

AbstractTwenty years ago, the multidimensional, positive definite, advection transport algorithm was introduced by Smolarkiewicz. Over the two decades since, it has been applied countless times to numerous problems, however almost always on rectilinear grids. One of the few exceptions is the Operational Multiscale Environment model with Grid Adaptivity (OMEGA), an atmospheric simulation system originally designed to simulate atmospheric dispersion in the planetary boundary layer, but since then used for both mesoscale (from meso‐α to meso‐γ) dispersion and weather forecasting. One of the unique aspects of OMEGA is the triangular unstructured grid geometry which leads in a natural way to the creation of a global grid with continuously variable resolution from roughly 100 km over the oceans to less than 10 km over regions of interest. Another unique aspect is the concept of dynamically adapting grid resolution—sometimes also called solution‐adaptive grid resolution. A central element of the modelling system, however, is its advection solver—MPDATA. This paper presents the implementation of MPDATA on an unstructured grid and demonstrates its accuracy and efficiency using analytic and idealized test cases. Copyright © 2005 John Wiley & Sons, Ltd.

An Operational Multiscale Hurricane Forecasting System

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.

Evaluation of the Operational Multiscale Environment Model with Grid Adaptivity against the European Tracer Experiment

Abstract 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, O...

A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA)

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.