Stochastic parameterization of shallow cumulus convection estimated from high-resolution data

In this paper, we report on the development of a methodology for stochastic parameterization of convective transport by shallow cumulus convection in weather and climate models. We construct a parameterization based on Large-Eddy Simulation (LES) data. These simulations resolve the turbulent fluxes of heat and moisture and are based on a typical case of non-precipitating shallow cumulus convection above sea in the trade-wind region. Using clustering, we determine a finite number of turbulent flux pairs for heat and moisture that are representative for the pairs of flux profiles observed in these simulations. In the stochastic parameterization scheme proposed here, the convection scheme jumps randomly between these pre-computed pairs of turbulent flux profiles. The transition probabilities are estimated from the LES data, and they are conditioned on the resolved-scale state in the model column. Hence, the stochastic parameterization is formulated as a data-inferred conditional Markov chain (CMC), where each state of the Markov chain corresponds to a pair of turbulent heat and moisture fluxes. The CMC parameterization is designed to emulate, in a statistical sense, the convective behaviour observed in the LES data. The CMC is tested in single-column model (SCM) experiments. The SCM is able to reproduce the ensemble spread of the temperature and humidity that was observed in the LES data. Furthermore, there is a good similarity between time series of the fractions of the discretized fluxes produced by SCM and observed in LES

Stochastic Parameterization of Convective Area Fractions with a Multicloud Model Inferred from Observational Data

Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convectio

Osmotic swelling-induced activation of the extracellular-signal-regulated protein kinases Erk-1 and Erk-2 in intestine 407 cells involves the Ras/Raf-signalling pathway

Human Intestine 407 cells respond to hypo-osmotic stress with a rapid stimulation of compensatory ionic conductances accompanied by a transient increase in the activity of the extracellular-signal-regulated protein kinases Erk-1 and Erk-2. In this study, we examined the upstream regulators of hypotonicity-induced Erk-1/Erk-2 activation and their possible role in cell-volume regulation. The hypotonicity-provoked Erk-1/Erk-2 activation was greatly reduced in cells pretreated with the specific mitogen-activated/Erk-activating kinase inhibitor PD098059 and was preceded by a transient stimulation of Raf-1. Pretreatment of the cells with PMA, GF109203X, wortmannin or Clostridium botulinum C3 exoenzyme did not appreciably affect the hypotonicity-provoked Erk-1/Erk-2 stimulation, suggesting the osmosensitive signalling pathway to be largely independent of protein kinase C and p21(rho). In contrast, expression of dominant negative RasN17 completely abolished the hypotonicity-induced Erk-1/Erk-2 activation. Stimulation of the swelling-induced ion efflux was independent of activation of these mitogen-activated protein kinases, as revealed by hypotonicity-provoked isotope efflux from 125I-- and 86Rb+-loaded cells after pretreatment with PD098059 and after

Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health:An Example Involving Periodic Workers' Health Assessments

Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs) in the context of occupational health. Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario from a workers' health assessment to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. Results Respect for autonomy is affected by uncertainty about what future consequences the worker is consenting to as a result of the fluctuating nature of ML-DSTs and validity evidence used to inform the worker. A beneficent advisory process is influenced because the three elements of evidence based practice are affected through use of a ML-DST. The principle of non-maleficence is challenged by the balance between group-level benefits and individual harm, the vulnerability of the worker in the occupational context, and the possibility of function creep. Justice might be empowered when the ML-DST is valid, but profiling and discrimination are potential risks. Conclusions Implications of ethical considerations have been described for the socially responsible design of ML-DSTs. Three recommendations were provided to minimize undesirable adverse effects of the development and implementation of ML-DSTs

Adipocyte-specific protein tyrosine phosphatase 1B deletion increases lipogenesis, adipocyte cell size and is a minor regulator of glucose homeostasis

Peer reviewedPublisher PD

Human and value sensitive aspects of mobile app design: a Foucauldian perspective

Value sensitive concerns remain relatively neglected by software design processes leading to potential failure of technology acceptance. By drawing upon an inter-disciplinary study that employed participatory design methods to develop mobile apps in the domain of youth justice, this paper examines a critical example of an unintended consequence that created user concerns around Focauldian concepts including power, authority, surveillance and governmentality. The primary aim of this study was to design, deploy and evaluate social technology that may help to promote better engagement between case workers and young people to help reduce recidivism, and support young people’s transition towards social inclusion in society. A total of 140 participants including practitioners (n=79), and young people (n=61) contributed to the data collection via surveys, focus groups and one-one interviews. The paper contributes an important theoretically located discussion around both how co-design is helpful in giving ‘voice’ to key stakeholders in the research process and observing the risk that competing voices may lead to tensions and unintended outcomes. In doing so, software developers are exposed to theories from social science that have significant impact on their product