11 Sep 2025

A Calibration Method for Projecting Future Extremes via a Linear Mapping of Parameters

Abstract: In order to study potential impacts arising from climate change, future projections of numerical model output often must be calibrated to be comparable to observations. Rather than calibrating the data values themselves, we propose a novel statistical calibration method for extremes that assumes there exists a linear relationship between parameters associated with model output and parameters associated with observations. This approach allows us to capture uncertainty in both parameter estimates and the linear calibration, which we achieve via bootstrap. To focus on extreme behavior, we assume both model output and observations have distributions composed of a mixture model combining a Weibull distribution with a generalized Pareto distribution for the tail. A simulation study shows good coverage rates. We apply the method to project future daily-averaged river runoff at the Purgatoire River in southeastern Colorado.

15 Feb 2022

Detection Limits of Trinitrotoluene and Ammonium Nitrate in Soil by Raman Spectroscopy

Abstract: The detection limit of 2,4,6-trinitrotoluene (TNT) and ammonium nitrate (AN) in mixtures of Ottawa sand (OS) was studied using a Raman microscope applying conventional calibration curves, Pearson correlation coefficients, and two-sample t-tests. By constructing calibration curves, the conventionally defined detection limits were estimated to be 1.9 ± 0.4% by mass in OS and 1.9 ± 0.3% by mass in OS for TNT and AN. Both TNT and AN were detectable in concentrations as low as 1% by mass when Pearson correlation coefficients were used to compare averaged spectra to a library containing spectra from a range of soil types. AN was detectable in concentrations as low as 1% by mass when a test sample of spectra was compared to the same library using two-sample t-tests. TNT was not detectable at a concentration of 1% by mass when using two-sample t-tests.