Chemical composition and reactivity of water on hexagonal Pt-group metal surfaces

The dissociation behaviour and valence-electronic structure of water adsorbed on clean and oxygen-covered Ru{0001}, Rh{111}, Pd{111}, Ir{111} and Pt{111} surfaces has been studied by high-resolution X-ray photoelectron spectroscopy with the aim of identifying similarities and trends within the Pt-group metals. On average, we find higher reactivity for the 4d metals (Ru, Rh, Pd) as compared to 5d (Ir, Pt), which is correlated with characteristic shifts in the 1b(1) and 3a(1) molecular orbitals of water. Small amounts of oxygen (<0.2 ML) induce dissociation of water on all five surfaces, for higher coverages (>0.25 ML) only intact water is observed. Under UHV conditions these higher coverages can only be reached on the 4d metals, the 5d metals are, therefore, not passivated.

Four-dimensional variational data assimilation for high resolution nested models

Four-dimensional variational data assimilation (4D-Var) is used in environmental prediction to estimate the state of a system from measurements. When 4D-Var is applied in the context of high resolution nested models, problems may arise in the representation of spatial scales longer than the domain of the model. In this paper we study how well 4D-Var is able to estimate the whole range of spatial scales present in one-way nested models. Using a model of the one-dimensional advection–diffusion equation we show that small spatial scales that are observed can be captured by a 4D-Var assimilation, but that information in the larger scales may be degraded. We propose a modification to 4D-Var which allows a better representation of these larger scales.

Forecast impact of targeted observations: sensitivity to observation error and proximity to steep orography

For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observations to the Greenland coast is also investigated. Experiments were conducted using the Met Office Unified Model (MetUM), over a limited-area domain at 24-km grid spacing, with a four-dimensional variational data assimilation (4D-Var) scheme. Reducing the operational dropsonde observation errors by one-half increases the maximum forecast improvement from 5% to 7%–10%, measured in terms of total energy. However, the largest impact is seen by replacing two dropsondes on the Greenland coast with two farther from the steep orography; this increases the maximum forecast improvement from 5% to 18% for an 18-h forecast (using operational observation errors). Forecast degradation caused by two dropsonde observations on the Greenland coast is shown to arise from spreading of data by the background errors up the steep slope of Greenland. Removing boundary layer data from these dropsondes reduces the forecast degradation, but it is only a partial solution to this problem. Although only from one case study, these results suggest that observations positioned within a correlation length scale of steep orography may degrade the forecast through the anomalous upslope spreading of analysis increments along terrain-following model levels.

Construction safety and digital design: a review

As digital technologies become widely used in designing buildings and infrastructure, questions arise about their impacts on construction safety. This review explores relationships between construction safety and digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art research on databases, virtual reality, geographic information systems, 4D CAD, building information modeling and sensing technologies, finding various digital tools for addressing safety issues in the construction phase, but few tools to support design for construction safety. It also considers a literature on safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of technologies, and has implications for the emerging research agenda around construction safety and digital design. Bringing these strands of literature together suggests new kinds of interventions, such as the development of tools and processes for using digital models to promote mindfulness through multi-party collaboration on safety

Nonlinear data assimilation in geosciences: an extremely efficient particle filter

Almost all research fields in geosciences use numerical models and observations and combine these using data-assimilation techniques. With ever-increasing resolution and complexity, the numerical models tend to be highly nonlinear and also observations become more complicated and their relation to the models more nonlinear. Standard data-assimilation techniques like (ensemble) Kalman filters and variational methods like 4D-Var rely on linearizations and are likely to fail in one way or another. Nonlinear data-assimilation techniques are available, but are only efficient for small-dimensional problems, hampered by the so-called ‘curse of dimensionality’. Here we present a fully nonlinear particle filter that can be applied to higher dimensional problems by exploiting the freedom of the proposal density inherent in particle filtering. The method is illustrated for the three-dimensional Lorenz model using three particles and the much more complex 40-dimensional Lorenz model using 20 particles. By also applying the method to the 1000-dimensional Lorenz model, again using only 20 particles, we demonstrate the strong scale-invariance of the method, leading to the optimistic conjecture that the method is applicable to realistic geophysical problems. Copyright c 2010 Royal Meteorological Society

The effect of nonlinearity on the variational assimilation of satellite observations using a simple column model

Cloud imagery is not currently used in numerical weather prediction (NWP) to extract the type of dynamical information that experienced forecasters have extracted subjectively for many years. For example, rapidly developing mid-latitude cyclones have characteristic signatures in the cloud imagery that are most fully appreciated from a sequence of images rather than from a single image. The Met Office is currently developing a technique to extract dynamical development information from satellite imagery using their full incremental 4D-Var (four-dimensional variational data assimilation) system. We investigate a simplified form of this technique in a fully nonlinear framework. We convert information on the vertical wind field, w(z), and profiles of temperature, T(z, t), and total water content, qt (z, t), as functions of height, z, and time, t, to a single brightness temperature by defining a 2D (vertical and time) variational assimilation testbed. The profiles of w, T and qt are updated using a simple vertical advection scheme. We define a basic cloud scheme to obtain the fractional cloud amount and, when combined with the temperature field, we convert this information into a brightness temperature, having developed a simple radiative transfer scheme. With the exception of some matrix inversion routines, all our code is developed from scratch. Throughout the development process we test all aspects of our 2D assimilation system, and then run identical twin experiments to try and recover information on the vertical velocity, from a sequence of observations of brightness temperature. This thesis contains a comprehensive description of our nonlinear models and assimilation system, and the first experimental results.

Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model

For data assimilation in numerical weather prediction, the initial forecast-error covariance matrix Pf is required. For variational assimilation it is particularly important to prescribe an accurate initial matrix Pf, since Pf is either static (in the 3D-Var case) or constant at the beginning of each assimilation window (in the 4D-Var case). At large scales the atmospheric flow is well approximated by hydrostatic balance and this balance is strongly enforced in the initial matrix Pf used in operational variational assimilation systems such as that of the Met Office. However, at convective scales this balance does not necessarily hold any more. Here we examine the extent to which hydrostatic balance is valid in the vertical forecast-error covariances for high-resolution models in order to determine whether there is a need to relax this balance constraint in convective-scale data assimilation. We use the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and a 1.5 km resolution version of the Unified Model for a case study characterized by the presence of convective activity. An ensemble of high-resolution forecasts valid up to three hours after the onset of convection is produced. We show that at 1.5 km resolution hydrostatic balance does not hold for forecast errors in regions of convection. This indicates that in the presence of convection hydrostatic balance should not be enforced in the covariance matrix used for variational data assimilation at this scale. The results show the need to investigate covariance models that may be better suited for convective-scale data assimilation. Finally, we give a measure of the balance present in the forecast perturbations as a function of the horizontal scale (from 3–90 km) using a set of diagnostics. Copyright © 2012 Royal Meteorological Society and British Crown Copyright, the Met Office

On variational data assimilation in continuous time

Variational data assimilation in continuous time is revisited. The central techniques applied in this paper are in part adopted from the theory of optimal nonlinear control. Alternatively, the investigated approach can be considered as a continuous time generalization of what is known as weakly constrained four-dimensional variational assimilation (4D-Var) in the geosciences. The technique allows to assimilate trajectories in the case of partial observations and in the presence of model error. Several mathematical aspects of the approach are studied. Computationally, it amounts to solving a two-point boundary value problem. For imperfect models, the trade-off between small dynamical error (i.e. the trajectory obeys the model dynamics) and small observational error (i.e. the trajectory closely follows the observations) is investigated. This trade-off turns out to be trivial if the model is perfect. However, even in this situation, allowing for minute deviations from the perfect model is shown to have positive effects, namely to regularize the problem. The presented formalism is dynamical in character. No statistical assumptions on dynamical or observational noise are imposed.

Educational simulation in construction: the virtual construction simulator

Students in the architecture, engineering, and construction disciplines are often challenged with visualizing and understanding the complex spatial and temporal relationships involved in designing and constructing three-dimensional (3D) structures. An evolving body of research traces the use of educational computer simulations to enhance student learning experiences through testing real-world scenarios and the development of student decision-making skills. Ongoing research at Pennsylvania State University aims to improve engineering education in construction through interactive construction project learning applications in an immersive virtual reality environment. This paper describes the first- and second-generation development of the Virtual Construction Simulator (VCS), a tool that enables students to simultaneously create and review construction schedules through 3D model interaction. The educational value and utility of VCS was assessed through surveys, focus group interviews, and a student exercise conducted in a construction management class. Results revealed VCS is a valuable and effective four-dimensional (4D) model creation and schedule review application that fosters collaborative work and greater student task focus. This paper concludes with a discussion of the findings and the future development steps of the VCS educational simulation

Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system

The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems. The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties.

The identification of QTL controlling ergot sclerotia size in hexaploid wheat implicates a role for the Rht dwarfing alleles

The fungal pathogen Claviceps purpurea infects ovaries of a broad range of temperate grasses and cereals, including hexaploid wheat, causing a disease commonly known as ergot. Sclerotia produced in place of seed carry a cocktail of harmful alkaloid compounds that result in a range of symptoms in humans and animals, causing ergotism. Following a field assessment of C. purpurea infection in winter wheat, two varieties ‘Robigus’ and ‘Solstice’ were selected which consistently produced the largest differential effect on ergot sclerotia weights. They were crossed to produce a doubled haploid mapping population, and a marker map, consisting of 714 genetic loci and a total length of 2895 cM was produced. Four ergot reducing QTL were identified using both sclerotia weight and size as phenotypic parameters; QCp.niab.2A and QCp.niab.4B being detected in the wheat variety ‘Robigus’, and QCp.niab.6A and QCp.niab.4D in the variety ‘Solstice’. The ergot resistance QTL QCp.niab.4B and QCp.niab.4D peaks mapped to the same markers as the known reduced height (Rht) loci on chromosomes 4B and 4D, Rht-B1 and Rht-D1, respectively. In both cases, the reduction in sclerotia weight and size was associated with the semi-dwarfing alleles, Rht-B1b from ‘Robigus’ and Rht-D1b from ‘Solstice’. Two-dimensional, two-QTL scans identified significant additive interactions between QTL QCp.niab.4B and QCp.niab.4D, and between QCp.niab.2A and QCp.niab.4B when looking at sclerotia size, but not between QCp.niab.2A and QCp.niab.4D. The two plant height QTL, QPh.niab.4B and QPh.niab.4D, which mapped to the same locations as QCp.niab.4B and QCp.niab.4D, also displayed significant genetic interactions.