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Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

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In this paper, we present a systematic approach for characterization and reconstruction of statistically optimal representative unit cells of polydisperse particulate composites. Microtomography is used to gather rich three-dimensional data of a packed glass bead system. First-, second-, and third-order probability functions are used to characterize the morphology of the material, and the parallel augmented simulated annealing algorithm is employed for reconstruction of the statistically equivalent medium. Both the fully resolved probability spectrum and the geometrically exact particle shapes are considered in this study, rendering the optimization problem multidimensional with a highly complex objective function. A ten-phase particulate composite composed of packed glass beads in a cylindrical specimen is investigated, and a unit cell is reconstructed on massively parallel computers. Further, rigorous error analysis of the statistical descriptors (probability functions) is presented and a detailed comparison between statistics of the voxel-derived pack and the representative cell is made.

Abstract. Spatiotemporal characteristics of surface ozone (O 3 ) variations over South Korea are investigated with consideration of meteorological factors and timescales based on the Kolmogorov-Zurbenko filter (KZ filter), using measurement data at 124 air quality monitoring sites and 72 weather stations for the 12 yr period of 1999-2010. In general, O 3 levels at coastal cities are high due to dynamic effects of the sea breeze while those at the inland and Seoul Metropolitan Area (SMA) cities are low due to the NO x titration by local precursor emissions. We examine the meteorological influences on O 3 using a combined analysis of the KZ filter and linear regressions between O 3 and meteorological variables. We decomposed O 3 time series at each site into short-term, seasonal, and long-term components by the KZ filter and regressed on meteorological variables. Impact of temperature on the O 3 levels is significantly high in the highly populated SMA and inland region, but low in the coastal region. In particular, the probability of high O 3 occurrence doubles with 4 • C of temperature increase in the SMA during high O 3 months (May-October). This implies that those regions will experience frequent high O 3 events in a future warming climate. In terms of short-term variation, the distribution of high O 3 probability classified by wind direction shows the effect of both local precursor emissions and long-range transport from China. In terms of long-term variation, the O 3 concentrations have increased by +0.26 ppbv yr −1 (parts per billion by volume) on nationwide average, but their trends show large spatial variability. Singular value decomposition analyses further reveal that the long-term temporal evolution of O 3 is similar to that of nitrogen dioxide, although the spatial distribution of their trends is different. This study will be helpful as a reference for diagnostics and evaluation of regional-and local-scale O 3 and climate simulations, and as a guide to appropriate O 3 control policy in South Korea.