7,245 research outputs found
Assimilation of Perimeter Data and Coupling with Fuel Moisture in a Wildland Fire - Atmosphere DDDAS
We present a methodology to change the state of the Weather Research
Forecasting (WRF) model coupled with the fire spread code SFIRE, based on
Rothermel's formula and the level set method, and with a fuel moisture model.
The fire perimeter in the model changes in response to data while the model is
running. However, the atmosphere state takes time to develop in response to the
forcing by the heat flux from the fire. Therefore, an artificial fire history
is created from an earlier fire perimeter to the new perimeter, and replayed
with the proper heat fluxes to allow the atmosphere state to adjust. The method
is an extension of an earlier method to start the coupled fire model from a
developed fire perimeter rather than an ignition point. The level set method is
also used to identify parameters of the simulation, such as the spread rate and
the fuel moisture. The coupled model is available from openwfm.org, and it
extends the WRF-Fire code in WRF release.Comment: ICCS 2012, 10 pages; corrected some DOI typesetting in the reference
Quality of service based data-aware scheduling
Distributed supercomputers have been widely used for solving complex computational problems and modeling complex phenomena such as black holes, the environment, supply-chain economics, etc. In this work we analyze the use of these distributed supercomputers for time sensitive data-driven applications. We present the scheduling challenges involved in running deadline sensitive applications on shared distributed supercomputers running large parallel jobs and introduce a ``data-aware\u27\u27 scheduling paradigm that overcomes these challenges by making use of Quality of Service classes for running applications on shared resources. We evaluate the new data-aware scheduling paradigm using an event-driven hurricane simulation framework which attempts to run various simulations modeling storm surge, wave height, etc. in a timely fashion to be used by first responders and emergency officials. We further generalize the work and demonstrate with examples how data-aware computing can be used in other applications with similar requirements
Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms
Recent project-management literature and high-profile disastersâthe financial crisis, the BP
Deepwater Horizon oil spill, and the Fukushima nuclear accidentâillustrate the flaws of
traditional risk models for complex projects. This research examines how various groups with
interests in the Arctic offshore define risks. The findings link the wicked problem framework and
the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems
are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The
authors synthesize literature on the topic to offer strategies for navigating wicked problems,
provide new variables to deconstruct traditional risk models, and integrate objective and
subjective schools of risk analysis
Distributed simulation of city inundation by coupled surface and subsurface porous flow for urban flood decision support system
We present a decision support system for flood early warning and disaster
management. It includes the models for data-driven meteorological predictions,
for simulation of atmospheric pressure, wind, long sea waves and seiches; a
module for optimization of flood barrier gates operation; models for stability
assessment of levees and embankments, for simulation of city inundation
dynamics and citizens evacuation scenarios. The novelty of this paper is a
coupled distributed simulation of surface and subsurface flows that can predict
inundation of low-lying inland zones far from the submerged waterfront areas,
as observed in St. Petersburg city during the floods. All the models are
wrapped as software services in the CLAVIRE platform for urgent computing,
which provides workflow management and resource orchestration.Comment: Pre-print submitted to the 2013 International Conference on
Computational Scienc
Simulation and Prediction of Storm Surges and Waves Driven by Hurricanes and Assessment of Coastal Flooding and Inundation
River, Estuarine and Coastal Dynamic
Emergency Water Information Network (EWIN)
Flooding is a global problem and as a representative example, Mexico is currently struggling to
manage flood situations which are increasing in regularity and severity. Many developing countries have substandard flood monitoring infrastructure. However, in common with the UK, they
have state-of-the-art cellular mobile phone systems. In this research, expertise in water engineering and radio communications from the UK and Mexico have been combined to design a cost
effective flood forecasting system based on hydrology sensing and mobile networks.
Recent events such as hurricane Patricia in Mexico (October 2015) has emphasised the need for
systems that can predict the dynamic behaviour of large-scale water flows. Currently, management of flood situations in many developing countries is carried out through prediction of water
behaviour (Hydro Meteorological Warning System). This system is based on estimates of rainfall,
runoff and water levels. In Mexico two central registers and rain measuring stations are used to
gather data. The data collected is compared with pre-established risk thresholds which determine
whether a warning should be issued.
In general, the rainy season in Mexico occurs during the summer and fall, starting in May and
ending in October. Along the main waterways, the change in state is dynamic between dry and
rainy both in terms of the water volume in the channels and the vegetation on the banks. Vegetation in Mexico is normally sparse but grows quickly and in abundance during the rainy season.
During flood events, new rivers form along river beds that are normally empty. These conditions
are typical of flooding in many countries.
In order to develop a real time flood forecasting system, several areas of research need to be investigated. These include: data sensing at the appropriate location and time, wireless transmission
of flood data, sensor data fusion, model generation and prediction at the remote weather station.
This multidisciplinary research project is addressing each of these areas by employing UK expertise in Water Engineering and Radio Communications to complement the research base in Mexico
Performance Measures to Assess Resiliency and Efficiency of Transit Systems
Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jerseyâs Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service.
This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster
Impactâbased forecasting for pluvial floods
Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5Â min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings
Hurricane risk analysis: A review on the physically-based approach
This paper reviews recent studies that take a physically-based approach to better assess and manage hurricane risk. Such a methodology includes three components: modeling the storm climatology (which defines TC risk in terms of the upper tail of the storm statistics); modeling landfalling hazards; and characterizing damage and losses
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