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Uncertainty Propagation (uncertainty + propagation)
Selected AbstractsUncertainty propagation in a London flood simulationJOURNAL OF FLOOD RISK MANAGEMENT, Issue 1 2009B.W. Golding Abstract Following recent costly flood events in the United Kingdom, there is considerable societal and political pressure to reduce flooding and improve warnings. In response to this, the Flood Risk Management Research Consortium (FRMRC) has been created to investigate the potential of several areas of existing research to be brought into operational use. In this paper, the estimation of flood impact and probability is analysed and illustrated with examples from a simulated forecast of a Thames Estuary flood event carried out at a FRMRC workshop. The forecast modelling chain consisted of meteorology, storm surge, estuary hydrodynamics, defence failure and inundation. The workshop concluded that end-to-end propagation of probability was feasible in an integrated real-time flood forecasting system, and that the basis of such a system had been demonstrated. [source] Evaluating and expressing the propagation of uncertainty in chemical fate and bioaccumulation modelsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 4 2002Matthew MacLeod Abstract First-order analytical sensitivity and uncertainty analysis for environmental chemical fate models is described and applied to a regional contaminant fate model and a food web bioaccumulation model. By assuming linear relationships between inputs and outputs, independence, and log-normal distributions of input variables, a relationship between uncertainty in input parameters and uncertainty in output parameters can be derived, yielding results that are consistent with a Monte Carlo analysis with similar input assumptions. A graphical technique is devised for interpreting and communicating uncertainty propagation as a function of variance in input parameters and model sensitivity. The suggested approach is less calculationally intensive than Monte Carlo analysis and is appropriate for preliminary assessment of uncertainty when models are applied to generic environments or to large geographic areas or when detailed parameterization of input uncertainties is unwarranted or impossible. This approach is particularly useful as a starting point for identification of sensitive model inputs at the early stages of applying a generic contaminant fate model to a specific environmental scenario, as a tool to support refinements of the model and the uncertainty analysis for site-specific scenarios, or for examining defined end points. The analysis identifies those input parameters that contribute significantly to uncertainty in outputs, enabling attention to be focused on defining median values and more appropriate distributions to describe these variables. [source] Propagation of uncertainty from observing systems into NWP: COST-731 Working Group 1ATMOSPHERIC SCIENCE LETTERS, Issue 2 2010A. Rossa Abstract The COST-731 Action is focused on uncertainty propagation in hydrometeorological forecasting chains. The goals and activities of the Action Working Group 1 can be subdivided by (1) describing and studying the impact of imperfect observations, mostly from radar, (2) exploiting radar data assimilation as a promising avenue for improved short-range precipitation forecasts and (3) high-resolution ensemble forecasting. Activities of Working Group 1 are presented along with their possible significance for hydrological applications. Copyright © 2010 Royal Meteorological Society and Crown copyright [source] Propagation of uncertainty from observing systems and NWP into hydrological models: COST-731 Working Group 2ATMOSPHERIC SCIENCE LETTERS, Issue 2 2010Massimiliano Zappa Abstract The COST-731 action is focused on uncertainty propagation in hydrometeorologica l forecasting chains. Goals and activities of the action Working Group 2 are presented. Five foci for discussion and research have been identified: (1) understand uncertainties, (2) exploring, designing and comparing methodologies for the use of uncertainty in hydrological models, (3) providing feedback on sensitivity to data and forecast providers, (4) transferring methodologies among the different communities involved and (5) setting up test-beds and perform proof-of-concepts. Current examples of different perspectives on uncertainty propagation are presented. Copyright © 2010 Royal Meteorological Society [source] | ||||||||||