19 resultados para Oregon.

em CentAUR: Central Archive University of Reading - UK


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A combination of idealized numerical simulations and analytical theory is used to investigate the spacing between convective orographic rainbands over the Coastal Range of western Oregon. The simulations, which are idealized from an observed banded precipitation event over the Coastal Range, indicate that the atmospheric response to conditionally unstable flow over the mountain ridge depends strongly on the subridge-scale topographic forcing on the windward side of the ridge. When this small-scale terrain contains only a single scale (l) of terrain variability, the band spacing is identical to l, but when a spectrum of terrain scales are simultaneously present, the band spacing ranges between 5 and 10 km, a value that is consistent with observations. Based on the simulations, an inviscid linear model is developed to provide a physical basis for understanding the scale selection of the rainbands. This analytical model, which captures the transition from lee waves upstream of the orographic cloud to moist convection within it, reveals that the spacing of orographic rainbands depends on both the projection of lee-wave energy onto the unstable cap cloud and the growth rate of unstable perturbations within the cloud. The linear model is used in tandem with numerical simulations to determine the sensitivity of the band spacing to a number of environmental and terrain-related parameters.

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The triggering of convective orographic rainbands by small-scale topographic features is investigated through observations of a banded precipitation event over the Oregon Coastal Range and simulations using a cloud-resolving numerical model. A quasi-idealized simulation of the observed event reproduces the bands in the radar observations, indicating the model’s ability to capture the physics of the band-formation process. Additional idealized simulations reinforce that the bands are triggered by lee waves past small-scale topographic obstacles just upstream of the nominal leading edge of the orographic cloud. Whether a topographic obstacle in this region is able to trigger a strong rainband depends on the phase of its lee wave at cloud entry. Convective growth only occurs downstream of obstacles that give rise to lee-wave-induced displacements that create positive vertical velocity anomalies w_c and nearly zero buoyancy anomalies b_c as air parcels undergo saturation. This relationship is quantified through a simple analytic condition involving w_c, b_c, and the static stability N_m^2 of the cloud mass. Once convection is triggered, horizontal buoyancy gradients in the cross-flow direction generate circulations that align the bands parallel to the flow direction.

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We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

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Radar images and numerical simulations of three shallow convective precipitation events over the Coastal Range in western Oregon are presented. In one of these events, unusually well-defined quasi-stationary banded formations produced large precipitation enhancements in favored locations, while varying degrees of band organization and lighter precipitation accumulations occurred in the other two cases. The difference between the more banded and cellular cases appeared to depend on the vertical shear within the orographic cap cloud and the susceptibility of the flow to convection upstream of the mountain. Numerical simulations showed that the rainbands, which appeared to be shear-parallel convective roll circulations that formed within the unstable orographic cap cloud, developed even over smooth mountains. However, these banded structures were better organized, more stationary, and produced greater precipitation enhancement over mountains with small-scale topographic obstacles. Low-amplitude random topographic roughness elements were found to be just as effective as more prominent subrange-scale peaks at organizing and fixing the location of the orographic rainbands.

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Proteomic tools-in particular, mass spectrometry (MS)-have advanced significantly in recent years, and the identification of proteins within complex mixtures is now a routine procedure. Quantitative methods of analysis are less well advanced and continue to develop. These include the use of stable isotope ratio approaches, isotopically labeled peptide standards, and nonlabeling methods. This paper summarizes the use of MS as a proteomics tool to identify and semiquantify proteins and their modified forms by using examples of relevance to the Maillard reaction. Finally, some challenges for the future are presented.

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The correlated k-distribution (CKD) method is widely used in the radiative transfer schemes of atmospheric models, and involves dividing the spectrum into a number of bands and then reordering the gaseous absorption coefficients within each one. The fluxes and heating rates for each band may then be computed by discretizing the reordered spectrum into of order 10 quadrature points per major gas, and performing a pseudo-monochromatic radiation calculation for each point. In this paper it is first argued that for clear-sky longwave calculations, sufficient accuracy for most applications can be achieved without the need for bands: reordering may be performed on the entire longwave spectrum. The resulting full-spectrum correlated k (FSCK) method requires significantly fewer pseudo-monochromatic calculations than standard CKD to achieve a given accuracy. The concept is first demonstrated by comparing with line-by-line calculations for an atmosphere containing only water vapor, in which it is shown that the accuracy of heating-rate calculations improves approximately in proportion to the square of the number of quadrature points. For more than around 20 points, the root-mean-squared error flattens out at around 0.015 K d−1 due to the imperfect rank correlation of absorption spectra at different pressures in the profile. The spectral overlap of m different gases is treated by considering an m-dimensional hypercube where each axis corresponds to the reordered spectrum of one of the gases. This hypercube is then divided up into a number of volumes, each approximated by a single quadrature point, such that the total number of quadrature points is slightly fewer than the sum of the number that would be required to treat each of the gases separately. The gaseous absorptions for each quadrature point are optimized such they minimize a cost function expressing the deviation of the heating rates and fluxes calculated by the FSCK method from line-by-line calculations for a number of training profiles. This approach is validated for atmospheres containing water vapor, carbon dioxide and ozone, in which it is found that in the troposphere and most of the stratosphere, heating-rate errors of less than 0.2 K d−1 can be achieved using a total of 23 quadrature points, decreasing to less than 0.1 K d−1 for 32 quadrature points. It would be relatively straightforward to extend the method to include other gases.

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Inference on the basis of recognition alone is assumed to occur prior to accessing further information (Pachur & Hertwig, 2006). A counterintuitive result of this is the “less-is-more” effect: a drop in the accuracy with which choices are made as to which of two or more items scores highest on a given criterion as more items are learned (Frosch, Beaman & McCloy, 2007; Goldstein & Gigerenzer, 2002). In this paper, we show that less-is-more effects are not unique to recognition-based inference but can also be observed with a knowledge-based strategy provided two assumptions, limited information and differential access, are met. The LINDA model which embodies these assumptions is presented. Analysis of the less-is-more effects predicted by LINDA and by recognition-driven inference shows that these occur for similar reasons and casts doubt upon the “special” nature of recognition-based inference. Suggestions are made for empirical tests to compare knowledge-based and recognition-based less-is-more effects

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Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.

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When we first encounter the narrator of Austerlitz, he is wandering around the unfamiliar town of Antwerp with, he tells us, “unsicheren Schritten” (1; 9). As well as reflecting the unfamiliarity of the locale, these “uncertain steps” evince a proud modesty characteristic of the classic Sebaldian narrator, a wanderer who discreetly relays the stories of the people and places he is privileged to encounter. Although Sebald does not use the phrase, steps of this sort, unpurposed yet unerring, are made with what is commonly known in German as somnambule Sicherheit: the legendary surefootedness of the sleepwalker. The convergence of sleepwalking and certainty in a single phrase poses an interesting challenge to one of the central tenets of the English-language canonization of Sebald, for his writing has been most highly valued for its ability to move the reader through apparent certainties towards a salutary uncertainty. But somnambule Sicherheit also presents the possibility that the current may be reversed, that narrative may move under cover of uncertainty towards certainty. That Sebald criticism has not been more troubled by this possibility is in no small part due to the fact that it tends to deploy the notion of sleepwalking with a minimum of reflection on its theoretical ramifications. To evoke some of the complexities of this matter, I first offer a brief cultural history of sleepwalking, as well as a brief account of the topic of uncertainty in Sebald criticism. Most of my argument, however, involves an extended comparative analysis of sleepwalking in Sebald's Austerlitz and Hermann Broch's 1933 trilogy The Sleepwalkers. Although these writers have not previously been the object of any sustained comparison, sleepwalking in Broch's novels illuminates much that is left implicit on the topic in Sebald's fiction and points toward some difficult questions regarding the role of aesthetics and agency in Sebald's work.

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Idealised convection-permitting simulations are used to quantify the impact of embedded convection on the precipitation generated by moist flow over midlatitude mountain ridges. A broad range of mountain dimensions and moist stabilities are considered to encompass a spectrum of physically plausible flows. The simulations reveal that convection only enhances orographic precipitation in cap clouds that are otherwise unable to efficiently convert cloud condensate into precipitate. For tall and wide mountains (e.g. the Washington Cascades or the southern Andes), precipitate forms efficiently through vapour deposition and collection, even in the absence of embedded convection. When embedded convection develops in such clouds, it produces competing effects (enhanced condensation in updraughts and enhanced evaporation through turbulent mixing and compensating subsidence) that cancel to yield little net change in precipitation. By contrast, convection strongly enhances precipitation over short and narrow mountains (e.g. the UK Pennines or the Oregon Coastal Range) where precipitation formation is otherwise highly inefficient. Although cancellation between increased condensation and evaporation still occurs, the enhanced precipitation formation within the convective updraughts leads to a net increase in precipitation efficiency. The simulations are physically interpreted through non-dimensional diagnostics and relevant time-scales that govern advective, microphysical, and convective processes.