Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons

Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review

This paper reviews the issues to be faced in attempting to create a microsimulation of health care demand, health care finance and the economic impact of health behaviour. These issues identified via an in-depth review of seven dynamic microsimulation models, selected from an initial set of 27 models in order to highlight the main differences in approaches and modelling options currently adopted. After presenting a brief description of each of the seven selected models, the main modelling approaches are summarized and critically appraised using five main distinguishing criteria. These criteria are the use of alignment techniques, model complexity (as reflected in the range of variables used), theoretical foundations, type of starting population, and the extent and detail of financial issues covered. Building upon this appraisal, the paper goes on to show how the ‘12 SAGE lessons’ apply in the field of health care microsimulation. The trade-off between complexity and predictive power is shown to be key. Finally an appendix summarises the main features of all 27 of the dynamic microsimulation models originally surveyed.health care, microsimulation

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Accounting for outliers and calendar effects in surrogate simulations of stock return sequences

Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework for the determination of weak chaos in time series dynamics. Existing SDA procedures do not account properly for the rich structures observed in stock return sequences, attributed to the presence of heteroscedasticity, seasonal effects and outliers. In this paper we suggest a modification of the SDA framework, based on the robust estimation of location and scale parameters of mean-stationary time series and a probabilistic framework which deals with outliers. A demonstration on the NASDAQ Composite index daily returns shows that the proposed approach produces surrogates that faithfully reproduce the structure of the original series while being manifestations of linear-random dynamics.Comment: 21 pages, 7 figure

Engineering Advanced Training Environment for Crisis Management: The Pandora Project

The paper describes the technical framework of a near real-life training environment for learning activities suitable for training in crisis scenarios. The underlying architecture features a design that makes provision for a learning environment capable of training collaborative, as well as independent, decision making skills among crisis managers in potential crisis situations. Modelling the training scenarios takes into consideration both the pragmatic nature of responding to crisis, as well as the human behavioural factors involved in dealing with situations of chaos and uncertainty. This work is part of ongoing research on the Pandora1 project, which aims to provide a near-real training environment at affordable cost

Infrastructure dynamics: A selected bibliography

The term infrastructure is used to denote the set of life support and public service systems which is necessary for the development of growth of human settlements. Included are some basic references in the field of dynamic simulation, as well as a number of relevant applications in the area of infrastructure planning. The intent is to enable the student or researcher to quickly identify such applications to the extent necessary for initiating further work in the field