Related Experiment Videos
Uncertainty analysis for computer model projections of hurricane losses
Ronald L Iman1, Mark E Johnson, Charles C Watson
1Southwest Technology Consultants, Albuquerque, NM 87122, USA. ron@swtechcon.com
Risk Analysis : an Official Publication of the Society for Risk Analysis
|November 22, 2005
Summary
Hurricane loss projections are uncertain, as shown by Hurricane Charley. This study analyzes uncertainty in wind speed and loss projections using a Holland B parameter model to improve forecasting accuracy.
Area of Science:
- Meteorology and atmospheric science
- Risk management and actuarial science
- Structural engineering and wind engineering
Background:
- Hurricane Charley (2004) demonstrated significant uncertainty in real-time hurricane loss projections, with estimates fluctuating dramatically.
- The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) mandates rigorous auditing of loss projection models for insurance rate filings.
- Audits include crucial uncertainty and sensitivity analyses performed by multidisciplinary expert teams.
Purpose of the Study:
- To perform an uncertainty and sensitivity analysis on loss projections derived from a Holland B parameter hurricane wind field model.
- To quantify the impact of input variables on wind speed and loss uncertainty in hurricane modeling.
- To inform and improve future uncertainty and sensitivity analyses for hurricane loss projection models.
Main Methods:
- Utilized a Holland B parameter hurricane wind field model for loss projection.
- Conducted uncertainty analysis to quantify the reduction in uncertainty attributable to each input variable.
- Performed sensitivity analysis to assess the influence of input variables on model outputs.
Main Results:
- The analysis quantified the expected percentage reduction in uncertainty for both wind speed and financial loss.
- Identified key input variables that significantly influence the uncertainty of hurricane loss projections.
- Provided a framework for understanding and reducing uncertainty in hurricane damage assessments.
Conclusions:
- Uncertainty and sensitivity analyses are critical for validating hurricane loss projection models.
- The Holland B parameter model's uncertainty can be systematically analyzed to improve forecasting.
- This research contributes to more reliable insurance rate filings and better hurricane risk management.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
00:59Propagation of Uncertainty from Random Error
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...
01:10Propagation of Uncertainty from Systematic Error
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 particular...
00:54Uncertainty: Confidence Intervals
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 't,' or...
00:59Uncertainty: Overview
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.
01:29Mathematical Modeling: Problem Solving
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...