905 resultados para interim


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The ability of the HiGEM climate model to represent high-impact, regional, precipitation events is investigated in two ways. The first focusses on a case study of extreme regional accumulation of precipitation during the passage of a summer extra-tropical cyclone across southern England on 20 July 2007 that resulted in a national flooding emergency. The climate model is compared with a global Numerical Weather Prediction (NWP) model and higher resolution, nested limited area models. While the climate model does not simulate the timing and location of the cyclone and associated precipitation as accurately as the NWP simulations, the total accumulated precipitation in all models is similar to the rain gauge estimate across England and Wales. The regional accumulation over the event is insensitive to horizontal resolution for grid spacings ranging from 90km to 4km. Secondly, the free-running climate model reproduces the statistical distribution of daily precipitation accumulations observed in the England-Wales precipitation record. The model distribution diverges increasingly from the record for longer accumulation periods with a consistent under-representation of more intense multi-day accumulations. This may indicate a lack of low-frequency variability associated with weather regime persistence. Despite this, the overall seasonal and annual precipitation totals from the model are still comparable to those from ERA-Interim.

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Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.

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Purpose The research objective of this study is to understand how institutional changes to the EU regulatory landscape may affect corresponding institutionalized operational practices within financial organizations. Design/methodology/approach The study adopts an Investment Management System as its case and investigates different implementations of this system within eight financial organizations, predominantly focused on investment banking and asset management activities within capital markets. At the systems vendor site, senior systems consultants and client relationship managers were interviewed. Within the financial organizations, compliance, risk and systems experts were interviewed. Findings The study empirically tests modes of institutional change. Displacement and Layering were found to be the most prevalent modes. However, the study highlights how the outcomes of Displacement and Drift may be similar in effect as both modes may cause compliance gaps. The research highlights how changes in regulations may create gaps in systems and processes which, in the short term, need to be plugged by manual processes. Practical implications Vendors abilities to manage institutional change caused by Drift, Displacement, Layering and Conversion and their ability to efficiently and quickly translate institutional variables into structured systems has the power to ease the pain and cost of compliance as well as reducing the risk of breeches by reducing the need for interim manual systems. Originality/value The study makes a contribution by applying recent theoretical concepts of institutional change to the topic of regulatory change uses this analysis to provide insight into the effects of this new environment

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Policy-makers are creating mechanisms to help developing countries cope with loss and damage from climate change, but the negotiations are largely neglecting scientific questions about what the impacts of climate change actually are. Mitigation efforts have failed to prevent the continued increase of anthropogenic greenhouse gas (GHG) emissions. Adaptation is now unlikely to be sufficient to prevent negative impacts from current and future climate change1. In this context, vulnerable nations argue that existing frameworks to promote mitigation and adaptation are inadequate, and have called for a third international mechanism to deal with residual climate change impacts, or “loss and damage”2. In 2013, the United Nations Framework Convention on Climate Change (UNFCCC) responded to these calls and established the Warsaw International Mechanism (WIM) to address loss and damage from the impacts of climate change in developing countries3. An interim Executive Committee of party representatives has been set up, and is currently drafting a two-year workplan comprising meetings, reports, and expert groups; and aiming to enhance knowledge and understanding of loss and damage, strengthen dialogue among stakeholders, and promote enhanced action and support. Issues identified as priorities for the WIM thus far include: how to deal with non-economic losses, such as loss of life, livelihood, and cultural heritage; and linkages between loss and damage and patterns of migration and displacement2. In all this, one fundamental issue still demands our attention: which losses and damages are relevant to the WIM? What counts as loss and damage from climate change?

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A new generation of reanalysis products is currently being produced that provides global gridded atmospheric data spanning more than a century. Such data may be useful for characterising the observed long-term variability of extreme precipitation events, particularly in regions where spatial coverage of surface observations is limited, and in the pre-satellite era. An analysis of extreme precipitation events is performed over England and Wales, investigating the ability of Twentieth Century Reanalysis and ERA-Interim to represent extreme precipitation accumulations as recorded in the England and Wales Precipitation dataset on accumulation time-scales from 1 to 7 days. Significant correlations are found between daily precipitation accumulation observations and both reanalysis products. A hit-rate analysis indicates that the reanalyses have hit rates (as defined by an event above the 98th percentile) of approximately 40–65% for extreme events in both summer (JJA) and winter (DJF). This suggests that both ERA-Interim and Twentieth Century Reanalysis are difficult to use for representing individual extreme precipitation events over England and Wales.

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This study assesses the influence of the El Niño–Southern Oscillation (ENSO) on global tropical cyclone activity using a 150-yr-long integration with a high-resolution coupled atmosphere–ocean general circulation model [High-Resolution Global Environmental Model (HiGEM); with N144 resolution: ~90 km in the atmosphere and ~40 km in the ocean]. Tropical cyclone activity is compared to an atmosphere-only simulation using the atmospheric component of HiGEM (HiGAM). Observations of tropical cyclones in the International Best Track Archive for Climate Stewardship (IBTrACS) and tropical cyclones identified in the Interim ECMWF Re-Analysis (ERA-Interim) are used to validate the models. Composite anomalies of tropical cyclone activity in El Niño and La Niña years are used. HiGEM is able to capture the shift in tropical cyclone locations to ENSO in the Pacific and Indian Oceans. However, HiGEM does not capture the expected ENSO–tropical cyclone teleconnection in the North Atlantic. HiGAM shows more skill in simulating the global ENSO–tropical cyclone teleconnection; however, variability in the Pacific is overpronounced. HiGAM is able to capture the ENSO–tropical cyclone teleconnection in the North Atlantic more accurately than HiGEM. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity, is used to further understand the response of tropical cyclone activity to ENSO in the North Atlantic and western North Pacific. The vertical wind shear response over the Caribbean is not captured in HiGEM compared to HiGAM and ERA-Interim. Biases in the mean ascent at 500 hPa in HiGEM remain in HiGAM over the western North Pacific; however, a more realistic low-level vorticity in HiGAM results in a more accurate ENSO–tropical cyclone teleconnection.

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ERA-Interim reanalysis data from the past 35 years have been used with a newly-developed feature tracking algorithm to identify Indian monsoon depressions originating in or near the Bay of Bengal. These were then rotated, centralised and combined to give a fully three-dimensional 106-depression composite structure – a considerably larger sample than any previous detailed study on monsoon depressions and their structure. Many known features of depression structure are confirmed, particularly the existence of a maximum to the southwest of the centre in rainfall and other fields, and a westward axial tilt in others. Additionally, the depressions are found to have significant asymmetry due to the presence of the Himalayas; a bimodal mid-tropospheric potential vorticity core; a separation into thermally cold- (~–1.5K) and neutral- (~0K) cores near the surface with distinct properties; and that the centre has very large CAPE and very small CIN. Variability as a function of background state has also been explored, with land/coast/sea, diurnal, ENSO, active/break and Indian Ocean Dipole contrasts considered. Depressions are found to be markedly stronger during the active phase of the monsoon, as well as during La Niña. Depressions on land are shown to be more intense and more tightly constrained to the central axis. A detailed schematic diagram of a vertical cross-section through a composite depression is also presented, showing its inherent asymmetric structure.

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Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed.

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Since 2007 a large decline in Arctic sea ice has been observed. The large-scale atmospheric circulation response to this decline is investigated in ERA-Interim reanalyses and HadGEM3 climate model experiments. In winter, post-2007 observed circulation anomalies over the Arctic, North Atlantic and Eurasia are small compared to interannual variability. In summer, the post-2007 observed circulation is dominated by an anticyclonic anomaly over Greenland which has a large signal-to-noise ratio. Climate model experiments driven by observed SST and sea ice anomalies are able to capture the summertime pattern of observed circulation anomalies, although the magnitude is a third of that observed. The experiments suggest high SSTs and reduced sea ice in the Labrador Sea lead to positive temperature anomalies in the lower troposphere which weaken the westerlies over North America through thermal wind balance. The experiments also capture cyclonic anomalies over Northwest Europe, which are consistent with downstream Rossby wave propagation

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In an adaptive seamless phase II/III clinical trial interim analysis, data are used for treatment selection, enabling resources to be focused on comparison of more effective treatment(s) with a control. In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focuses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

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The theory of wave–mean flow interaction requires a partition of the atmospheric flow into a notional background state and perturbations to it. Here, a background state, known as the Modified Lagrangian Mean (MLM), is defined as the zonally symmetric state obtained by requiring that every potential vorticity (PV) contour lying within an isentropic layer encloses the same mass and circulation as in the full flow. For adiabatic and frictionless flow, these two integral properties are time-invariant and the MLM state is a steady solution of the primitive equations. The time dependence in the adiabatic flow is put into the perturbations, which can be described by a wave-activity conservation law that is exact even at large amplitude. Furthermore, the effects of non-conservative processes on wave activity can be calculated from the conservation law. A new method to calculate the MLM state is introduced, where the position of the lower boundary is obtained as part of the solution. The results are illustrated using Northern Hemisphere ERA-Interim data. The MLM state evolves slowly, implying that the net non-conservative effects are weak. Although ‘adiabatic eddy fluxes’ cannot affect the MLM state, the effects of Rossby-wave breaking, PV filamentation and subsequent dissipation result in sharpening of the polar vortex edge and meridional shifts in the MLM zonal flow, both at tropopause level and on the winter stratospheric vortex. The rate of downward migration of wave activity during stratospheric sudden warmings is shown to be given by the vertical scale associated with polar vortex tilt divided by the time-scale for wave dissipation estimated from the wave-activity conservation law. Aspects of troposphere–stratosphere interaction are discussed. The new framework is suitable to examine the climate and its interactions with disturbances, such as midlatitude storm tracks, and makes a clean partition between adiabatic and non-conservative processes.

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Terahertz (THz) radiation is being developed as a tool for the analysis of cultural heritage, and due to recent advances in technology is now available commercially in systems which can be deployed for field analysis. The radiation is capable of penetrating up to one centimetre of wall plaster and is delivered in ultrafast pulses which are reflected from layers within this region. The technique is non-contact, non-invasive and non-destructive. While sub-surface radar is able to penetrate over a metre of wall plaster, producing details of internal structures, infrared and ultraviolet techniques produce information about the surface layers of wall plaster. THz radiation is able to provide information about the interim region of up to approximately one centimetre into the wall surface. Data from Chartres Cathedral, France, Riga Dome Cathedral, Latvia, and Chartreuse du Val de Bénédiction, France is presented each with different research questions. The presence of sub-surface paint layers was expected from documentary evidence, dating to the 13th Century, at Chartres Cathedral. In contrast, at the Riga Dome Cathedral surface painting had been obscured as recently as 1941 during the Russian occupation of Latvia using white lead-based paint. In the 13th Century, wall paintings at the Chapel of the Frescos, Chartreuse du Val de Benediction in Villeneuve les Avignon were constructed using sinopia under-painting on plaster covering uneven stonework.. This paper compares and contrasts the ability of THz radiation to provide information about sub-surface features in churches and Cathedrals across Europe by analysing depth based profiles gained from the reflected signal. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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Extreme rainfall events continue to be one of the largest natural hazards in the UK. In winter, heavy precipitation and floods have been linked with intense moisture transport events associated with atmospheric rivers (ARs), yet no large-scale atmospheric precursors have been linked to summer flooding in the UK. This study investigates the link between ARs and extreme rainfall from two perspectives: 1) Given an extreme rainfall event, is there an associated AR? 2) Given an AR, is there an associated extreme rainfall event? We identify extreme rainfall events using the UK Met Office daily rain-gauge dataset and link these to ARs using two different horizontal resolution atmospheric datasets (ERA-Interim and 20th Century Re-analysis). The results show that less than 35% of winter ARs and less than 15% of summer ARs are associated with an extreme rainfall event. Consistent with previous studies, at least 50% of extreme winter rainfall events are associated with an AR. However, less than 20% of the identified summer extreme rainfall events are associated with an AR. The dependence of the water vapor transport intensity threshold used to define an AR on the years included in the study, and on the length of the season, is also examined. Including a longer period (1900-2012) compared to previous studies (1979-2005) reduces the water vapor transport intensity threshold used to define an AR.

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Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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Two methods are developed to estimate net surface energy fluxes based upon satellite-based reconstructions of radiative fluxes at the top of atmosphere and the atmospheric energy tendencies and transports from the ERA-Interim reanalysis. Method 1 applies the mass adjusted energy divergence from ERA-Interim while method 2 estimates energy divergence based upon the net energy difference at the top of atmosphere and the surface from ERA-Interim. To optimise the surface flux and its variability over ocean, the divergences over land are constrained to match the monthly area mean surface net energy flux variability derived from a simple relationship between the surface net energy flux and the surface temperature change. The energy divergences over the oceans are then adjusted to remove an unphysical residual global mean atmospheric energy divergence. The estimated net surface energy fluxes are compared with other data sets from reanalysis and atmospheric model simulations. The spatial correlation coefficients of multi-annual means between the estimations made here and other data sets are all around 0.9. There are good agreements in area mean anomaly variability over the global ocean, but discrepancies in the trend over the eastern Pacific are apparent.