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The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.

Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted a considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l Ensemble (TIGGE) project, a prominent contribution to THORPEX, has been initiated to enable advanced research and demonstration of the multimodel ensemble concept and to pave the way toward operational implementation of such a system at the international level. The objectives of TIGGE are 1) to facilitate closer cooperation between the academic and operational meteorological communities by expanding the availability of operational products for research, and 2) to facilitate exploring the concept and benefits of multimodel probabilistic weather forecasts, with a particular focus on high-impact weather prediction. Ten operational weather forecasting centers producing daily global ensemble forecasts to 1–2 weeks ahead have agreed to deliver in near–real time a selection of forecast data to the TIGGE data archives at the China Meteorological Agency, the European Centre for Medium-Range Weather Forecasts, and the National Center for Atmospheric Research. The volume of data accumulated daily is 245 GB (1.6 million global fields). This is offered to the scientific community as a new resource for research and education. The TIGGE data policy is to make each forecast accessible via the Internet 48 h after it was initially issued by each originating center. Quicker access can also be granted for field experiments or projects of particular interest to the World Weather Research Programme and THORPEX. A few examples of initial results based on TIGGE data are discussed in this paper, and the case is made for additional research in several directions.

In parallel with the growing concerns of climate change, sustainability, and a perceived lack of urban vibrancy and vitality, an increased number of planning and design movements, policies, and incentives have emerged in the US during the last decades, criticizing urban sprawl and praising the idea of 15-minute, compact cities. However, the tools meant to achieve these, including transport-oriented and mixed-use developments were typically hampered by residential perceptions and demand, especially in the spread, auto-dependent urban regions of the western USA. The aim of the research was to explore current trends in the development of mixed-use projects and the extent to which these processes are stimulated by sustainability concerns in Phoenix and Tempe, Arizona, located in one of the most spread urban regions in the world. Interviews were conducted with planners and city representatives in the Phoenix Metropolitan Area, which are complemented by the review of municipal strategies and zoning ordinances, visualization and analysis of GIS data, and implementation of site visits. The findings show that the strategic aspirations towards mixed-use developments lack environmental considerations due to public perceptions being tied to other issues related to mixed-use developments, which can be traced primarily to Phoenix Downtown. As general difficulties, concerns aggravated by political, administrative, and funding problems. However, decoupled from sustainability, mixed-use developments are likely to proliferate in the Phoenix and Tempe areas due to political will and continuous gentrification processes.

Negative biases in emotional processing are well recognised in people who are currently depressed but are less well described in those with a history of depression, where such biases may contribute to vulnerability to relapse. To compare accuracy, discrimination and bias in face emotion recognition in those with current and remitted depression. The sample comprised a control group (n = 101), a currently depressed group (n = 30) and a remitted depression group (n = 99). Participants provided valid data after receiving a computerised face emotion recognition task following standardised assessment of diagnosis and mood symptoms. In the control group women were more accurate in recognising emotions than men owing to greater discrimination. Among participants with depression, those in remission correctly identified more emotions than controls owing to increased response bias, whereas those currently depressed recognised fewer emotions owing to decreased discrimination. These effects were most marked for anger, fear and sadness but there was no significant emotion × group interaction, and a similar pattern tended to be seen for happiness although not for surprise or disgust. These differences were confined to participants who were antidepressant-free, with those taking antidepressants having similar results to the control group. Abnormalities in face emotion recognition differ between people with current depression and those in remission. Reduced discrimination in depressed participants may reflect withdrawal from the emotions of others, whereas the increased bias in those with a history of depression could contribute to vulnerability to relapse. The normal face emotion recognition seen in those taking medication may relate to the known effects of antidepressants on emotional processing and could contribute to their ability to protect against depressive relapse.

Accurate estimates of error variances in numerical analyses and forecasts (i.e. difference between analysis or forecast fields and nature on the resolved scales) are critical for the evaluation of forecasting systems, the tuning of data assimilation (DA) systems and the proper initialisation of ensemble forecasts. Errors in observations and the difficulty in their estimation, the fact that estimates of analysis errors derived via DA schemes, are influenced by the same assumptions as those used to create the analysis fields themselves, and the presumed but unknown correlation between analysis and forecast errors make the problem difficult. In this paper, an approach is introduced for the unbiased estimation of analysis and forecast errors. The method is independent of any assumption or tuning parameter used in DA schemes. The method combines information from differences between forecast and analysis fields ('perceived forecast errors') with prior knowledge regarding the time evolution of (1) forecast error variance and (2) correlation between errors in analyses and forecasts. The quality of the error estimates, given the validity of the prior relationships, depends on the sample size of independent measurements of perceived errors. In a simulated forecast environment, the method is demonstrated to reproduce the true analysis and forecast error within predicted error bounds. The method is then applied to forecasts from four leading numerical weather prediction centres to assess the performance of their corresponding DA and modelling systems. Error variance estimates are qualitatively consistent with earlier studies regarding the performance of the forecast systems compared. The estimated correlation between forecast and analysis errors is found to be a useful diagnostic of the performance of observing and DA systems. In case of significant model-related errors, a methodology to decompose initial value and model-related forecast errors is also proposed and successfully demonstrated.

Accurate estimates of 'true' error variance between Numerical Weather Prediction (NWP) analyses and forecasts and the 'reality' interpolated to a NWP model grid (Analysis and true Forecast Error Variance, hereafter AFEV) are critical for successful data assimilation and ensemble forecasting applications. Peña and Toth ( 2014 , PT14) introduced a Statistical Analysis and Forecast Error estimation (hereafter called SAFE) algorithm for the unbiased estimation of AFEV. The method uses variances between NWP forecasts and analyses (i.e. 'perceived' forecast errors) and assumptions about the time evolution of true error variances. PT14 successfully tested SAFE for the estimation of area mean error variances. In the present study, SAFE is extended by mitigating the effects of increased sampling noise and by accounting for the spatiotemporal evolution of forecast error variances, both critical for gridpoint-based applications. The enhanced method is evaluated in a Simulated Nature, Observations, Data Assimilation, and Prediction Environment using a quasi-geostrophic model and an ensemble Kalman Filter (EnKF). SAFE estimates of true analysis error variance are within 6% of the actual values, as compared to 24-55% deviations in EnKF estimates. The spatial correlation between estimated and actual true error variances was also found high (above 0.9) and comparable with EnKF estimates, but much higher than NMC method estimates (0.63-0.78). Estimates of the other two SAFE parameters, the growth rate and decorrelation of analysis and forecast error variances are within 3% of the corresponding actual values.

Low GABA transmission has been reported in suicide, and GABRA6 rs3219151 T allele has been associated with greater physiological and endocrine stress response in previous studies. Although environmental stress also plays a role in suicide, the possible role of this allele has not been investigated in this respect. In our present study effect of rs3219151 of GABRA6 gene in interaction with recent negative life events on lifetime and current depression, current anxiety, as well as lifetime suicide were investigated using regression models in a white European general sample of 2283 subjects. Post hoc measures for phenotypes related to suicide risk were also tested for association with rs3219151 in interaction with environmental stress. No main effect of the GABRA6 rs3219151 was detected, but in those exposed to recent negative life events GABRA6 T allele increased current anxiety and depression as well as specific elements of suicide risk including suicidal and death-related thoughts, hopelessness, restlessness and agitation, insomnia and impulsiveness as measured by the STOP task. Our data indicate that stress-associated suicide risk is elevated in carriers of the GABRA6 rs3219151 T allele with several independent markers and predictors of suicidal behaviours converging to this increased risk.

Cannabinoid receptor 1 (CB1) gene (CNR1) knockout mice are prone to develop anhedonic and helpless behavior after chronic mild stress. In humans, the CB1 antagonist rimonabant increases the risk of depressed mood disorders and anxiety. These studies suggest the hypothesis that genetic variation in CB1 receptor function influences the risk of depression in humans in response to stressful life events. In a population sample ( n =1269), we obtained questionnaire measures of personality (Big Five Inventory), depression and anxiety (Brief Symptom Inventory), and life events. The CNR1 gene was covered by 10 SNPs located throughout the gene to determine haplotypic association. Variations in the CNR1 gene were significantly associated with a high neuroticism and low agreeableness phenotype (explained variance 1.5 and 2.5%, respectively). Epistasis analysis of the SNPs showed that the previously reported functional 5′ end of the CNR1 gene significantly interacts with the 3′ end in these phenotypes. Furthermore, current depression scores significantly associated with CNR1 haplotypes but this effect diminished after covariation for recent life events, suggesting a gene × environment interaction. Indeed, rs7766029 showed highly significant interaction between recent negative life events and depression scores. The results represent the first evidence in humans that the CNR1 gene is a risk factor for depression––and probably also for co-morbid psychiatric conditions such as substance use disorders––through a high neuroticism and low agreeableness phenotype. This study also suggests that the CNR1 gene influences vulnerability to recent psychosocial adversity to produce current symptoms of depression.

We explored the cognitive structure of students in grade 1-grade 4 using word association in three topics (science, energy sources, and health) with six-six keywords (stimulus words) per topic. Based on the common associations given to the stimulus words, we calculated the relatedness coefficient for the stimulus word pairs. This was used to draw a cognitive structure, the conceptual network, characteristic of each group of learners. We found that the verbal version of the word association test is suitable for studying the cognitive structure of young schoolchildren. The typical conceptual network of the student groups shows a strong correlation with the knowledge structure of experts in grade 4 (in all three topics), as well as in grade 1 and grade 3 (in the topic of health). Using word association tests, we have been able to demonstrate that the number and strength of connections in the conceptual networks for groups of learners increases with the learners’ grade and their everyday experience of the topic.

The Ensemble Transform（ET） method has been shown to be useful in providing guidance for adaptive observation deployment.It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble subspace.In this paper,a new ET-based sensitivity（ETS） method,which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction,is proposed to specify regions for possible adaptive observations.ETS is a first order approximation of the ET;it requires just one calculation of a transformation matrix,increasing computational efficiency（60%-80%reduction in computational cost）.An explicit mathematical formulation of the ETS gradient is derived and described.Both the ET and ETS methods are applied to the Hurricane Irene（2011） case and a heavy rainfall case for comparison.The numerical results imply that the sensitive areas estimated by the ETS and ET are similar.However,ETS is much more efficient,particularly when the resolution is higher and the number of ensemble members is larger.