A heavy winter monsoon rainfall episode influenced by easterly waves, a westerly trough, blocking and the ITCZ

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Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright

The Integrated WRF/Urban Modeling System: Development, Evaluation, and Applications to Urban Environmental Problems

To bridge the gaps between traditional mesoscale modeling and microscale modeling, the National Center for Atmospheric Research (NCAR), in collaboration with other agencies and research groups, has developed an integrated urban modeling system coupled to the Weather Research and Forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modeling system consists of: 1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, 2) coupling to fine-scale Computational Fluid Dynamic (CFD) Reynolds-averaged Navier–Stokes (RANS) and Large-Eddy Simulation (LES) models for Transport and Dispersion (T&D) applications, 3) procedures to incorporate high-resolution urban land-use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and 4) an urbanized high-resolution land-data assimilation system (u-HRLDAS). This paper provides an overview of this modeling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modeling system illustrate its promising utility, as a regional climate-modeling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios

Burnout Among Surgeons in the UK During the COVID-19 Pandemic: A Cohort Study

BackgroundSurgeon burnout has implications for patient safety and workforce sustainability. The aim of this study was to establish the prevalence of burnout among surgeons in the UK during the COVID-19 pandemic.MethodsThis cross-sectional online survey was set in the UK National Health Service and involved 601 surgeons across the UK of all specialities and grades. Participants completed the Maslach Burnout Inventory and a bespoke questionnaire. Outcome measures included emotional exhaustion, depersonalisation and low personal accomplishment, as measured by the Maslach Burnout Inventory-Human Services Survey (MBI-HSS).ResultsA total of 142 surgeons reported having contracted COVID-19. Burnout prevalence was particularly high in the emotional exhaustion (57%) and depersonalisation (50%) domains, while lower on the low personal accomplishment domain (15%). Burnout prevalence was unrelated to COVID-19 status; however, the greater the perceived impact of COVID-19 on work, the higher the prevalence of emotional exhaustion and depersonalisation. Degree of worry about contracting COVID-19 oneself and degree of worry about family and friends contacting COVID-19 was positively associated with prevalence on all three burnout domains. Across all three domains, burnout prevalence was exceptionally high in the Core Trainee 1–2 and Specialty Trainee 1–2 grades.ConclusionsThese findings highlight potential undesirable implications for patient safety arising from surgeon burnout. Moreover, there is a need for ongoing monitoring in addition to an enhanced focus on mental health self-care in surgeon training and the provision of accessible and confidential support for practising surgeons

Identification of Surgeon Burnout via a Single-Item Measure

BackgroundBurnout is endemic in surgeons in the UK and linked with poor patient safety and quality of care, mental health problems, and workforce sustainability. Mechanisms are required to facilitate the efficient identification of burnout in this population. Multi-item measures of burnout may be unsuitable for this purpose owing to assessment burden, expertise required for analysis, and cost.AimsTo determine whether surgeons in the UK reporting burnout on the 22-item Maslach Burnout Inventory (MBI) can be reliably identified by a single-item measure of burnout.MethodsConsultant (n = 333) and trainee (n = 217) surgeons completed the MBI and a single-item measure of burnout. We applied tests of discriminatory power to assess whether a report of high burnout on the single-item measure correctly classified MBI cases and non-cases.ResultsThe single-item measure demonstrated high discriminatory power on the emotional exhaustion burnout domain: the area under the curve was excellent for consultants and trainees (0.86 and 0.80), indicating high sensitivity and specificity. On the depersonalisation domain, discrimination was acceptable for consultants (0.76) and poor for trainees (0.69). In contrast, discrimination was acceptable for trainees (0.71) and poor for consultants (0.62) on the personal accomplishment domain.ConclusionsA single-item measure of burnout is suitable for the efficient assessment of emotional exhaustion in consultant and trainee surgeons in the UK. Administered regularly, such a measure would facilitate the early identification of at-risk surgeons and swift intervention, as well as the monitoring of group-level temporal trends to inform resource allocation to coincide with peak periods

Modeling microstructurally aware non-convex domains

by Sourav Mukul TewariM.Tech

Need for considering urban climate change factors on stroke, neurodegenerative diseases, and mood disorders studies

Abstract The adverse health impacts of climate change have been well documented. It is increasingly apparent that the impacts are disproportionately higher in urban populations, especially underserved communities. Studies have linked urbanization and air pollution with health impacts, but the exacerbating role of urban heat islands (UHI) in the context of neurodegenerative diseases has not been well addressed. The complex interplay between climate change, local urban air pollution, urbanization, and a rising population in cities has led to the byproduct of increased heat stress in urban areas. Some urban neighborhoods with poor infrastructure can have excessive heat even after sunset, increasing internal body temperature and leading to hyperthermic conditions. Such conditions can put individuals at higher risk of stroke by creating a persistent neuroinflammatory state, including, in some instances, Alzheimer’s Disease (AD) phenotypes. Components of the AD phenotype, such as amyloid beta plaques, can disrupt long-term potentiation (LTP) and long-term depression (LTD), which can negatively alter the mesolimbic function and thus contribute to the pathogenesis of mood disorders. Furthermore, although a link has not previously been established between heat and Parkinson’s Disease (PD), it can be postulated that neuroinflammation and cell death can contribute to mitochondrial dysfunction and thus lead to Lewy Body formation, which is a hallmark of PD. Such postulations are currently being presented in the emerging field of ‘neurourbanism’. This study highlights that: (i) the impact of urban climate, air pollution and urbanization on the pathogenesis of neurodegenerative diseases and mood disorders is an area that needs further investigation; (ii) urban climate- health studies need to consider the heterogeneity in the urban environment and the impact it has on the UHI. In that, a clear need exists to go beyond the use of airport-based representative climate data to a consideration of more spatially explicit, high-resolution environmental datasets for such health studies, especially as they pertain to the development of locally-relevant climate adaptive health solutions. Recent advances in the development of super-resolution (downscaled climate) datasets using computational tools such as convolution neural networks (CNNs) and other machine learning approaches, as well as the emergence of urban field labs that generate spatially explicit temperature and other environmental datasets across different city neighborhoods, will continue to become important. Future climate – health studies need to develop strategies to benefit from such urban climate datasets that can aid the creation of localized, effective public health assessments and solutions