New Insights into History Matching via Sequential Monte Carlo

The aim of the history matching method is to locate non-implausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via a series of waves where at each wave an emulator is fitted to a small number of training samples. An implausibility measure is defined which takes into account the closeness of simulated and observed outputs as well as emulator uncertainty. As the waves progress, the emulator becomes more accurate so that training samples are more concentrated on promising regions of the space and poorer parts of the space are rejected with more confidence. Whilst history matching has proved to be useful, existing implementations are not fully automated and some ad-hoc choices are made during the process, which involves user intervention and is time consuming. This occurs especially when the non-implausible region becomes small and it is difficult to sample this space uniformly to generate new training points. In this article we develop a sequential Monte Carlo (SMC) algorithm for implementation which is semi-automated. Our novel SMC approach reveals that the history matching method yields a non-implausible distribution that can be multi-modal, highly irregular and very difficult to sample uniformly. Our SMC approach offers a much more reliable sampling of the non-implausible space, which requires additional computation compared to other approaches used in the literature

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation and the methodology is illustrated using real and simulated data sets.Comment: 8 graphs 35 page

Profiling aerosol optical, microphysical and hygroscopic properties in ambient conditions by combining in situ and remote sensing

We present the In situ/Remote sensing aerosol Retrieval Algorithm (IRRA) that combines airborne in situ and lidar remote sensing data to retrieve vertical profiles of ambient aerosol optical, microphysical and hygroscopic properties, employing the ISORROPIA II model for acquiring the particle hygroscopic growth. Here we apply the algorithm on data collected from the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft during the ACEMED campaign in the Eastern Mediterranean. Vertical profiles of aerosol microphysical properties have been derived successfully for an aged smoke plume near the city of Thessaloniki with aerosol optical depth of ∼0.4 at 532 nm, single scattering albedos of ∼0.9-0.95 at 550 nm and typical lidar ratios for smoke of ∼60-80 sr at 532 nm. IRRA retrieves highly hydrated particles above land, with 55 and 80% water volume content for ambient relative humidity of 80 and 90%, respectively. The proposed methodology is highly advantageous for aerosol characterization in humid conditions and can find valuable applications in aerosol-cloud interaction schemes. Moreover, it can be used for the validation of active space-borne sensors, as is demonstrated here for the case of CALIPSO

Organic geochemical studies of soils from the Rothamsted Classical Experiments - V. The fate of lipids in different long-term soil experiments

Lipid extracts from four long-term experiments (Broadbalk Wilderness, Geescroft Wilderness, Hoos®eld Spring Barley and Park Grass) were analysed using a combination of gas chromatography, gas chromatography±mass spectrometry and gas chromatography±combustion±isotope ratio mass spectrometry. The lipid content of the primary organic inputs for each soil were also analysed in order to assess the early diagenetic fate of the various compound classes present. Soil pH was observed to, either directly or indirectly, have a signi®cant e\u80ect on lipids with a relative increase in abundance of n-alkanes at higher pH (7.31) and a large relative increase in n-alkanoic and o-hydroxy acids at low pH (3.74). Triacylglycerols exhibited severe losses irrespective of pH. In an arable soil, n-alkanoic acids showed a temporal decrease in concentration whilst levels of n-alkanols remained static, the di\u80erence was ascribed to a more rapid turnover and possible leachate migration of the n-alkanoic acids. The phytosterol, sitosterol, was observed to rapidly diminish in soils possibly as a result of assimilation by soil dwelling invertebrates. Analysis of 5b-stigmastanol (a faecal biomarker) showed that it remained at levels indicative of manuring even after 113 years. Furthermore, analysis of 5b-stanyl esters revealed a manuring signal even more persistent than that exhibited by the free stanols. Knowledge of the biogeochemical cycling of lipids in the soil environment will help facilitate understanding of the processes which underpin carbon cycling in soils

Organic geochemical studies of soils from the Rothamsted classical experiments VI The occurrence and source of organic acids in an experimental grassland soil

In this work a detailed modeling of three-phase distribution transformers aimed at complementing well-known approaches is presented. Thus, incidence of angular displacement and tapping is taken into account in the proposed models, considering both actual values and per unit. The analysis is based on minimal data requirement: solely short-circuit admittance is needed since three-phase transformers are treated as non-magnetically-coupled single-phase transformers. In order to support the proposed methodology, results obtained through laboratory tests are presented