An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments

X-ray standing-wave photoemission experiments involving multilayered samples are emerging as unique probes of the buried interfaces that are ubiquitous in current device and materials research. Such data require for their analysis a structure optimization process comparing experiment to theory that is not straightforward. In this work, we present a new computer program for optimizing the analysis of standing-wave data, called SWOPT, that automates this trial-and-error optimization process. The program includes an algorithm that has been developed for computationally expensive problems: so-called black-box simulation optimizations. It also includes a more efficient version of the Yang X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S., Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which is about an order of magnitude faster than the original version. Human interaction is not required during optimization. We tested our optimization algorithm on real and hypothetical problems and show that it finds better solutions significantly faster than a random search approach. The total optimization time ranges, depending on the sample structure, from minutes to a few hours on a modern laptop computer, and can be up to 100x faster than a corresponding manual optimization. These speeds make the SWOPT program a valuable tool for realtime analyses of data during synchrotron experiments

Multi-scale modeling study of the source contributions to near-surface ozone and sulfur oxides levels over California during the ARCTAS-CARB period

Chronic high surface ozone (O_3) levels and the increasing sulfur oxides (SO_x = SO_2 + SO_4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sourcesto SC O_3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budgetduring the ARCTAS-CARB experiment period in 2008. Sensitivity simulations quantify contributions of biogenic and fire emissions to SC O_3 levels. California biogenic and fire emissions contribute 3–4 ppb to near-surface O_3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SO_x levels (up to ~0.7 ppb of SO_2 and ~1.3 ppb of SO_4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SO_x observed at 1–4 km is estimated to enhance surface SO_x over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SO_x levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SO_x emission inventories (EIs) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared withobservations. The CARB EI shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SO_x by about a factor of two. Adjoint sensitivity analysis indicated that SO_2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO_2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10–70% of SO2 and 20–60% fine SO_4 on-shore and over the most terrestrial areas, with contributions decreasing with in-land distance from the coast. Maritime emissions also modify the photochemical environment, shifting O_3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and O_3 concentrations in SC

Multi-scale modeling study of the source contributions to near-surface ozone and sulfur oxides levels over California during the ARCTAS-CARB period

Chronic high surface ozone (O3) levels and the increasing sulfur oxides (SOx = SO2+SO4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sourcesto SC O3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budgetduring the ARCTAS-CARB experiment period in 2008. Sensitivity simulations quantify contributions of biogenic and fire emissions to SC O3 levels. California biogenic and fire emissions contribute 3–4 ppb to near-surface O3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SOx levels (up to ~0.7 ppb of SO2 and ~1.3 ppb of SO4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SOx observed at 1–4 km is estimated to enhance surface SOx over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SOx levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SOx emission inventories (EIs) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared withobservations. The CARB EI shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SOx by about a factor of two. Adjoint sensitivity analysis indicated that SO2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10–70% of SO2 and 20–60% fine SO4 on-shore and over the most terrestrial areas, with contributions decreasing with in-land distance from the coast. Maritime emissions also modify the photochemical environment, shifting O3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and O3concentrations in SC

Simulating reactive nitrogen, carbon monoxide, and ozone in California during ARCTAS-CARB 2008 with high wildfire activity

Predictions of O_3, CO, total NO_y and individual NO_y species (NO, NO_2, HNO_3, PAN, alkyl nitrates and aerosol nitrate) from a fine resolution regional air quality modeling system for the South Coast Air Basin (SoCAB) and San Joaquin Valley Air Basin (SJVAB) of California are presented and evaluated for the 2008 ARCTAS-CARB campaign. The measurements of the chemical compounds from the fire plumes during the field campaign allow for the evaluation of the model's ability to simulate fire-influenced air masses as well. In general, the model successfully simulated the broad spatial distribution of chemical compounds in both air basins as well as the variation within the basins. Using inventories that reflect 2008 emissions levels, the model performed well in simulating NO_x (NO + NO_2) in SoCAB. Therefore, the under prediction of O_3 over these areas is more likely caused by uncertainties with the VOC emissions, chemistry, or discrepancies in the meteorology. The model did not capture the relatively high levels of O_3, and some reactive nitrogen species that were measured off shore of the SoCAB, indicating potential missing sources or the transport from on shore to off shore was not successfully captured. In SJVAB, the model had good performance in simulating different chemical compounds in the Fresno and Arvin areas. However, enhanced concentrations of O_3, NO_x, HNO_3 and PAN near dairy farms were significantly underestimated in the model. Negative biases also exist for O_3 and HNO_3 near oil fields, suggesting larger uncertainties associated with these emission sources. While the model simulated the total NO_y mixing ratios reasonably well, the prediction for partitioning between individual compounds showed larger uncertainties in the model simulation. Although the fire emissions inventory was updated to include the latest emissions estimates and speciation profiles, our model shows limited improvement in simulating the enhancement of O_3, CO, and PAN under fire impact as compared to a previous version of the modeling system. Further improvements in simulating fire emissions, especially the timing and the plume injection heights, are desired in order to better simulate the impact of fires

Photoelectron diffraction and holography: Present status and future prospects

Photoelectron diffraction and photoelectron holography, a newly developed variant of it, can provide a rich range of information concerning surface structure. These methods are sensitive to atomic type, chemical state, and spin state. The theoretical prediction of diffraction patterns is also well developed at both the single scattering and multiple scattering levels, and quantitative fits of experiment to theory can lead to structures with accuracies in the {plus_minus}0.03 {Angstrom} range. Direct structural information can also be derived from forward scattering in scanned-angle measurements at higher energies, path length differences contained in scanned-energy data at lower energies, and holographic inversions of data sets spanning some region in angle and energy space. Diffraction can also affect average photoelectron emission depths. Circular dichroism in core-level emission can be fruitfully interpreted in terms of photoelectron diffraction theory, as can measurements with spin-resolved core-spectra, and studies of surface magnetic structures and phase transitions should be possible with these methods. Synchrotron radiation is a key element of fully utilizing these techniques

Assessment of climate change impact on wintertime meteorology over California using dynamical downscaling method with a bias correction technique

Climate change can potentially have great impacts on wintertime precipitation and stagnant conditions, which are critical for both water resources and wintertime particulate matter (PM), in California. This study utilizes the Weather Research and Forecasting model to dynamically downscale a bias-corrected coarse-resolution global climate model dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to a grid size of 4 × 4 km2 over California for a present (2003–2012) and a future (2046–2055) decade. Compared to the present climate, an increase in 2-m temperature (up to 2 K) and water vapor mixing ratio (up to 1 g/kg) and a decrease in planetary boundary layer height (up to 80 m) are projected by the 2050s for the entire state of California. The number of stagnant days over the San Joaquin Valley is expected to increase by approximately 6% in the future decade, indicating potential exacerbation of the winter PM issue in this region. The wintertime precipitation is projected to increase by up to 50% in northern California and, conversely, to decrease by up to 40% in southern California during 2046–2055. The solid phase precipitation is projected to decrease over mountain ranges with lower elevations despite an overall increase in total precipitation, while it is projected to increase over the eastern side of the Sierra Nevada with elevation over 2 km

An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments

X-ray standing-wave photoemission experiments involving multilayered samples are emerging as unique probes of the buried interfaces that are ubiquitous in current device and materials research. Such data require for their analysis a structure optimization process comparing experiment to theory that is not straightforward. In this work, we present a new computer program for optimizing the analysis of standing-wave data, called SWOPT, that automates this trial-and-error optimization process. The program includes an algorithm that has been developed for computationally expensive problems: so-called black-box simulation optimizations. It also includes a more efficient version of the Yang X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S., Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which is about an order of magnitude faster than the original version. Human interaction is not required during optimization. We tested our optimization algorithm on real and hypothetical problems and show that it finds better solutions significantly faster than a random search approach. The total optimization time ranges, depending on the sample structure, from minutes to a few hours on a modern computer, and can be up to 100x faster than a corresponding manual optimization. These speeds make the SWOPT program a valuable tool for real-time analyses of data during synchrotron experiments