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00:59Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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01:10Propagation of Uncertainty from Systematic Error
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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00:54Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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03:37Uncertainty in Measurement: Accuracy and Precision
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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01:30Typical Model Studies
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Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.
Xianlong Hou1, Ben R Hodges2, Dongyu Feng2
1Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong 518055, PR China.
Marine Pollution Bulletin
|January 28, 2017
Summary
This study quantifies oil spill forecast uncertainty using Monte Carlo simulations and a HyosPy-based model. This improves emergency response by providing reliable predictions for oil spill impact assessment.
Area of Science:
- Environmental Science
- Marine Pollution
- Computational Fluid Dynamics
Background:
- Increasing oil transport in Texas bays heightens collision risks and potential oil spills.
- Effective oil spill response necessitates accurate, rapid forecasts of oil spread for ecological protection and emergency management.
Purpose of the Study:
- To quantify uncertainty in operational oil spill forecast models.
- To develop a reliability assessment for oil spill forecasts to aid emergency managers.
Main Methods:
- Implemented Monte Carlo simulation to generate forecast probability maps.
- Quantified forecast uncertainty by comparing forecast probability maps with hindcast simulations.
- Developed a HyosPy-based statistical model to assess forecast reliability (belief degree).
Main Results:
- Successfully quantified oil spill forecast uncertainty.
- Developed a prototype for analyzing uncertainty and reliability in numerical oil spill models.
- Demonstrated a method to improve the accuracy and trustworthiness of oil spill predictions.
Conclusions:
- Understanding forecast uncertainty and reliability is crucial for effective oil spill response planning.
- The developed methods provide a prototype for enhancing real-time operational oil spill response and impact assessment capabilities.
- This research supports emergency managers in making better-informed decisions during oil spill incidents.