Combined bias and outlier identification in dynamic data reconciliation

Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).

Examination of long-wave radiative bias in general circulation models over North Africa during May–July

Satellite data are used to quantify and examine the bias in the outgoing long-wave (LW) radiation over North Africa during May–July simulated by a range of climate models and the Met Office global numerical weather prediction (NWP) model. Simulations from an ensemble-mean of multiple climate models overestimate outgoing clear-sky long-wave radiation (LWc) by more than 20 W m−2 relative to observations from Clouds and the Earth's Radiant Energy System (CERES) for May–July 2000 over parts of the west Sahara, and by 9 W m−2 for the North Africa region (20°W–30°E, 10–40°N). Experiments with the atmosphere-only version of the High-resolution Hadley Centre Global Environment Model (HiGEM), suggest that including mineral dust radiative effects removes this bias. Furthermore, only by reducing surface temperature and emissivity by unrealistic amounts is it possible to explain the magnitude of the bias. Comparing simulations from the Met Office NWP model with satellite observations from Geostationary Earth Radiation Budget (GERB) instruments suggests that the model overestimates the LW by 20–40 W m−2 during North African summer. The bias declines over the period 2003–2008, although this is likely to relate to improvements in the model and inhomogeneity in the satellite time series. The bias in LWc coincides with high aerosol dust loading estimated from the Ozone Monitoring Instrument (OMI), including during the GERBILS field campaign (18–28 June 2007) where model overestimates in LWc greater than 20 W m−2 and OMI-estimated aerosol optical depth (AOD) greater than 0.8 are concurrent around 20°N, 0–20°W. A model-minus-GERB LW bias of around 30 W m−2 coincides with high AOD during the period 18–21 June 2007, although differences in cloud cover also impact the model–GERB differences. Copyright © Royal Meteorological Society and Crown Copyright, 2010

Changes in race-specific virulence in pseudomonas syringae pv. phaseolicola are Associated with a chimeric transposable element and rare deletion events in a plasmid-borne pathogenicity island

Virulence for bean and soybean is determined by effector genes in a plasmid-borne pathogenicity island (PAI) in race 7 strain 1449B of Pseudomonas syringae pv. phaseolicola. One of the effector genes, avrPphF, confers either pathogenicity, virulence, or avirulence depending on the plant host and is absent from races 2, 3, 4, 6, and 8 of this pathogen. Analysis of cosmid clones and comparison of DNA sequences showed that the absence of avrPphF from strain 1448A is due to deletion of a continuous 9.5-kb fragment. The remainder of the PAI is well conserved in strains 1448A and 1449B. The left junction of the deleted region consists of a chimeric transposable element generated from the fusion of homologs of IS1492 from Pseudomonas putida and IS1090 from Ralstonia eutropha. The borders of the deletion were conserved in 66 P. syringae pv. phaseolicola strains isolated in different countries and representing the five races lacking avrPphF. However, six strains isolated in Spain had a 10.5-kb deletion that extended 1 kb further from the right junction. The perfect conservation of the 28-nucleotide right repeat of the IS1090 homolog in the two deletion types and in the other 47 insertions of the IS1090 homolog in the 1448A genome strongly suggests that the avrPphF deletions were mediated by the activity of the chimeric mobile element. Our data strongly support a clonal origin for the races of P. syringae pv. phaseolicola lacking avrPphF.

Interpretation bias and anxiety in childhood: stability, specificity and longitudinal associations

Background: Biases in the interpretation of ambiguous material are central to cognitive models of anxiety; however, understanding of the association between interpretation and anxiety in childhood is limited. To address this, a prospective investigation of the stability and specificity of anxious cognitions and anxiety and the relationship between these factors was conducted. Method: Sixty-five children (10–11 years) from a community sample completed measures of self-reported anxiety, depression, and conduct problems, and responded to ambiguous stories at three time points over one-year. Results: Individual differences in biases in interpretation of ambiguity (specifically “anticipated distress” and “threat interpretation”) were stable over time. Furthermore, anticipated distress and threat interpretation were specifically associated with anxiety symptoms. Distress anticipation predicted change in anxiety symptoms over time. In contrast, anxiety scores predicted change in threat interpretation over time. Conclusions: The results suggest that different cognitive constructs may show different longitudinal links with anxiety. These preliminary findings extend research and theory on anxious cognitions and their link with anxiety in children, and suggest that these cognitive processes may be valuable targets for assessment and intervention.

Race-to-the-bottom or -top at home or abroad: Health and safety standards and the multinational firm

We develop a model to illustrate potential complexities in the relationship between corporate geographical diversification and the health and safety (H&S) standards set in national jurisdictions. A firm, that initially has a plant in its home country, may choose to also have one or two foreign plants in order to improve its bargaining position versus local governments, and so ensure reduced H&S standards, i.e. a race-to-the-bottom. However, contrary to the main focus of the popular debate on this topic, we note the potential for the race-to-the-bottom tendency to be exerted on H&S standards in the multinational company’s home rather than host country, and also for an upward push on H&S to instead result.

Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe

Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections.