Journal ArticleDOI
The Data Uncertainty Engine (DUE): A software tool for assessing and simulating uncertain environmental variables
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Data Uncertainty Engine (DUE) provides a conceptual framework for structuring an uncertainty analysis, allowing users without direct experience of uncertainty methods to develop realistic uncertainty models for their data.About:
This article is published in Computers & Geosciences.The article was published on 2007-02-01. It has received 97 citations till now. The article focuses on the topics: Uncertainty analysis & Uncertain data.read more
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Journal ArticleDOI
Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality
Abstract: This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen) This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method Water quality variables presented a more complex picture with many component errors For all variables, it was easy to find examples where relative error magnitudes exceeded 40% We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties Copyright © 2012 John Wiley & Sons, Ltd
Journal ArticleDOI
Managing uncertainty in integrated environmental modelling: The UncertWeb framework
Lucy Bastin,Dan Cornford,Richard Jones,Gerard B. M. Heuvelink,Edzer Pebesma,Christoph Stasch,Stefano Nativi,Paolo Mazzetti,Matthew Williams +8 more
TL;DR: The scope and architecture required to support uncertainty management as developed in UncertWeb, which includes tools which support elicitation, aggregation/disaggregation, visualisation and uncertainty/sensitivity analysis, is described.
Journal ArticleDOI
The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations
TL;DR: The Ensemble Verification System (EVS) is a flexible, user-friendly, software tool designed to verify ensemble forecasts of numeric variables, such as temperature, precipitation and streamflow, which can be applied to forecasts from any number of discrete locations and can aggregate verification statistics across several discrete locations.
References
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BookDOI
Modern Applied Statistics with S
W. N. Venables,Brian D. Ripley +1 more
TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Book
The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Journal ArticleDOI
Fuzzy sets as a basis for a theory of possibility
TL;DR: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.