Heavy Rainfall Warning Assessment Tool User Guide. Version 1.2

This report is a User Guide to a PC tool for assessing Heavy Rainfall Warnings. Development of the PC tool formed an important operational output of the Environment Agency and Met Office funded project: "Development of Rainfall Forecast Performance Monitoring Criteria. Phase 1: Development of Methodology and Algorithms" (Jones et al., 2003). The Heavy Rainfall Warning (HRW) Assessment Tool is a toolkit for Microsoft Excel. The tool allows the user to configure an assessment framework for a particular format of Heavy Rainfall Warning, enter and save data for forecasts and ground-truths, and generate a range of performance measures and other statistics for new and previously saved data. Summary tables are presented using Excel's PivotTable feature, from which charts can also be generated. Performance measures are provided to assess forecasts of heavy rainfall in continuous variable, categorical and probability form: these include bias, rmse, R-squared Efficiency, skill scores and the Continuous Brier Score

Remarks on separating words

The separating words problem asks for the size of the smallest DFA needed to distinguish between two words of length <= n (by accepting one and rejecting the other). In this paper we survey what is known and unknown about the problem, consider some variations, and prove several new results

Composition dependence of ion transport coefficients in gas mixtures

A simple momentum-transfer theory for the composition dependence of ion mobilities and diffusion coefficients in gas mixtures at arbitrary field strengths is corrected, extended, and compared with a similar theory based on momentum and energy transfer, and with results based on direct solution of the Boltzmann equation by Kihara's method. Final equations are recommended for predicting composition dependences, given only results on ion mobilities and diffusion coefficients in the pure component gases

Mammographic image restoration using maximum entropy deconvolution

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure

Response to comment on "solid recovered fuel: Materials flow analysis and fuel property development during the mechanical processing of biodried waste"

Laner and Cencic1 comment on Velis et al. (2013)2 clarifying certain points on the use of the material flow analysis (MFA) software STAN3. We welcome the correspondence and the opportunity this exchange provides to discuss optimal approaches to using STAN. In keeping with Velis et al.2 these physically impossible, and otherwise insignificant, negative flows have enabled improvements to STAN. Here, we elaborate on the practicalities of using STAN in our research and on the correctness and validation of our results, notwithstanding the inclusion of negative flows. We explain the contribution of our approach to solid waste management and resource recovery