WOMBAT v1.0: a fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

WOMBAT v1.0: A fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian-synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019, Atmos. Chem. Phys., vol. 19). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants.

Searching for Something Positive in Future Negative: The Adolescent and Adult Time Inventory–Time Attitudes Scale

The Adolescent and Adult Time Inventory–Time Attitudes Scale (AATI-TA) measures emotional engagement with the past, present, and future, and scores have been shown to relate meaningfully to health outcomes. For past, present, and future, five items are used to assess both positive and negative attitudes. Although evidence for the hypothesized six-factor solution has been widely reported, some studies have indicated problems with the Future Negative items. Given that a large and growing literature has emerged on the six-factor AATI-TA, and that AATI-TA scores have shown much better and more consistent fit than other temporal psychology measures, we sought to investigate the future negative factor in detail. Secondary analyses were performed on two datasets. The first was a University convenience sample ( N = 410) and the second was an adolescent sample ( N = 1,612). Confirmatory factor analyses revealed that the fit for the five Future Negative items was poor. Modification indices suggested that a correlated error term between Items 4 and 10 would result in good fit, and this was indeed the case. Models without Item 4 or Item 10 also yielded acceptable fit. Analyses using all four operationalizations of Future Negative (original scale, without Item 4 or Item 10, or with the correlated error between Items 4 and 10) to predict symptoms of anxiety and depression, and emotional self-efficacy revealed minor differences in the predictive validity coefficients. Potential ways forward, including a correlated error term or the dropping or replacement of Item 10, are discussed.

Validity, reliability and acceptability of Professionalism Mini-Evaluation Exercise (P-MEX) for emergency medicine residency training

Professionalism is a core competency in the medical profession. In this paper, we aimed to confirm the validity, reliability and acceptability of the Professionalism Mini-Evaluation Exercise (P-MEX) instrument for the emergency medicine (EM) residency program. Twenty-two EM attending physicians completed 383 P-MEX forms (the Persian version) for 90 EM residents. Construct validity was assessed via structural equation modeling (SEM). The reliability coefficient was estimated by the generalizability theory, and acceptability was assessed using two researcher-made questionnaires to evaluate the perspectives of residents and assessors. There was a consensus among the participants regarding the content of P-MEX. According to the results of SEM, the first implementation of the original model was associated with a moderate fit and high item loadings. The model modified with correlated error variances for two pairs of items showed an appropriate fit. The reliability of P-MEX was 0.81 for 14 occasions. The perception survey indicated high acceptability for P-MEX from the viewpoint of the residents and increasing satisfaction with P-MEX among the assessors over time. According to the results of the research, P-MEX is a reliable, valid, and acceptable instrument for assessing professionalism in EM residents.