Some challenges of middle atmosphere data assimilation

The assimilation of measurements from the stratosphere and mesosphere is becoming increasingly common as the lids of weather prediction and climate models rise into the mesosphere and thermosphere. However, the dynamics of the middle atmosphere pose specific challenges to the assimilation of measurements from this region. Forecast-error variances can be very large in the mesosphere and this can render assimilation schemes very sensitive to the details of the specification of forecast error correlations. An example is shown where observations in the stratosphere are able to produce increments in the mesosphere. Such sensitivity of the assimilation scheme to misspecification of covariances can also amplify any existing biases in measurements or forecasts. Since both models and measurements of the middle atmosphere are known to have biases, the separation of these sources of bias remains a issue. Finally, well-known deficiencies of assimilation schemes, such as the production of imbalanced states or the assumption of zero bias, are proposed explanations for the inaccurate transport resulting from assimilated winds. The inability of assimilated winds to accurately transport constituents in the middle atmosphere remains a fundamental issue limiting the use of assimilated products for applications involving longer time-scales.

Application of semiquantitative proteomics techniques to the Maillard reaction

Proteomic tools-in particular, mass spectrometry (MS)-have advanced significantly in recent years, and the identification of proteins within complex mixtures is now a routine procedure. Quantitative methods of analysis are less well advanced and continue to develop. These include the use of stable isotope ratio approaches, isotopically labeled peptide standards, and nonlabeling methods. This paper summarizes the use of MS as a proteomics tool to identify and semiquantify proteins and their modified forms by using examples of relevance to the Maillard reaction. Finally, some challenges for the future are presented.

Simulation models in farming systems research : potential and challenges

Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.

Cloud computing in e-science: research challenges and opportunities

SOA (Service Oriented Architecture), workflow, the Semantic Web, and Grid computing are key enabling information technologies in the development of increasingly sophisticated e-Science infrastructures and application platforms. While the emergence of Cloud computing as a new computing paradigm has provided new directions and opportunities for e-Science infrastructure development, it also presents some challenges. Scientific research is increasingly finding that it is difficult to handle “big data” using traditional data processing techniques. Such challenges demonstrate the need for a comprehensive analysis on using the above mentioned informatics techniques to develop appropriate e-Science infrastructure and platforms in the context of Cloud computing. This survey paper describes recent research advances in applying informatics techniques to facilitate scientific research particularly from the Cloud computing perspective. Our particular contributions include identifying associated research challenges and opportunities, presenting lessons learned, and describing our future vision for applying Cloud computing to e-Science. We believe our research findings can help indicate the future trend of e-Science, and can inform funding and research directions in how to more appropriately employ computing technologies in scientific research. We point out the open research issues hoping to spark new development and innovation in the e-Science field.

An approach to develop flexible systems with organizational-interoperability requirements

Flexibility of information systems (IS) have been studied to improve the adaption in support of the business agility as the set of capabilities to compete more effectively and adapt to rapid changes in market conditions (Glossary of business agility terms, 2003). However, most of work on IS flexibility has been limited to systems architecture, ignoring the analysis of interoperability as a part of flexibility from the requirements. This paper reports a PhD project, which proposes an approach to develop IS with flexibility features, considering some challenges of flexibility in small and medium enterprises (SMEs) such as the lack of interoperability and the agility of their business. The motivation of this research are the high prices of IS in developing countries and the usefulness of organizational semiotics to support the analysis of requirements for IS. (Liu, 2005).

Promoting core maths - background, evidence, plans, opportunities, and challenges

This article discusses some of the issues relating to the promotion of Core Maths to students, parents, schools and colleges and their senior leadership teams, and also to employers and higher education (HE). Some challenges are highlighted, and addressed, with suggestions for ways forward to secure the future of Core Maths and widespread adoption by all stakeholders. A summary of the background reports that led to the introduction of Core Maths, and the related educational landscape prior to its introduction, is included.

Data assimilation: From photon counts to Earth System forecasts

Data assimilation – the set of techniques whereby information from observing systems and models is combined optimally – is rapidly becoming prominent in endeavours to exploit Earth Observation for Earth sciences, including climate prediction. This paper explains the broad principles of data assimilation, outlining different approaches (optimal interpolation, three-dimensional and four-dimensional variational methods, the Kalman Filter), together with the approximations that are often necessary to make them practicable. After pointing out a variety of benefits of data assimilation, the paper then outlines some practical applications of the exploitation of Earth Observation by data assimilation in the areas of operational oceanography, chemical weather forecasting and carbon cycle modelling. Finally, some challenges for the future are noted.

A review on Arctic sea ice predictability and prediction on seasonal-to-decadal timescales

Sea ice plays a crucial role in the earth's energy and water budget and substantially impacts local and remote atmospheric and oceanic circulations. Predictions of Arctic sea ice conditions a few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea ice predictability on these timescales. Predictability mainly originates from persistence or advection of sea ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state-of-the-art models, current sea ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea ice forecasting are presented, along with suggestions for future research priorities.

Challenges in identifying factors which determine the placement of children in care? An international review

Placing a child in out-of-home care is one of the most important decisions made by professionals in the child care system, with substantial social, psychological, educational, medical and economic consequences. This paper considers the challenges and difficulties of building statistical models of this decision by reviewing the available international evidence. Despite the large number of empirical investigations over a 50 year period, a consensus on the variables associated with this decision is hard to identify. In addition, the individual models have low explanatory and predictive power and should not be relied on to make placement decisions. A number of reasons for this poor performance are offered, and some ways forwards suggested. This paper also aims to facilitate the emergence of a coherent and integrated international literature from the disconnected and fragmented empirical studies. Rather than one placement problem, there are many slightly different problems, and therefore it is expected that a number of related sub-literatures will emerge, each concentrating on a particular definition of the placement problem.

Establishing Lagrangian connections between observations within air masses crossing the Atlantic during the International Consortium for Atmospheric Research on Transport and Transformation experiment

The ITCT-Lagrangian-2K4 (Intercontinental Transport and Chemical Transformation) experiment was conceived with an aim to quantify the effects of photochemistry and mixing on the transformation of air masses in the free troposphere away from emissions. To this end, attempts were made to intercept and sample air masses several times during their journey across the North Atlantic using four aircraft based in New Hampshire (USA), Faial (Azores) and Creil (France). This article begins by describing forecasts from two Lagrangian models that were used to direct the aircraft into target air masses. A novel technique then identifies Lagrangian matches between flight segments. Two independent searches are conducted: for Lagrangian model matches and for pairs of whole air samples with matching hydrocarbon fingerprints. The information is filtered further by searching for matching hydrocarbon samples that are linked by matching trajectories. The quality of these "coincident matches'' is assessed using temperature, humidity and tracer observations. The technique pulls out five clear Lagrangian cases covering a variety of situations and these are examined in detail. The matching trajectories and hydrocarbon fingerprints are shown, and the downwind minus upwind differences in tracers are discussed.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Challenges of in situ conservation of crop wild relatives

Crop wild relatives (CWRs) will gain in importance as changing climates put both traditional and advanced cultivars under increasing stress, leading to a need for plant breeding to produce new varieties able to grow under the new climate regimes. Traditionally, the approach to the conservation of CWRs has been ex situ - the collection and maintenance of seed accessions in national, regional, and international germplasm banks, supplemented by field genebanks for species with recalcitrant seeds. More recently the need to maintain CWRs in their natural habitats (in situ) has been advocated. This is very different from on-farm conservation of traditional land races and is a complex multidisciplinary process. Particular problems that have to be addressed include the adoption of a workable definition of what is a CWR, application of priority-determining mechanisms because of the large number of candidate species of CWRs, assessment of the effectiveness of conservation approaches, the relative costs of in situ and ex situ approaches, integration of CWR in situ conservation into national programmes, and the challenges posed by global change. CWRs may be conserved in both protected and non-protected areas. Presence in the former is no guarantee of their survival and in most cases some degree of management intervention is required. Experience derived from recent EU- and GEF-funded CWR conservation initiatives will be drawn upon.

The ethical challenges of ubiquitous healthcare

Ubiquitous healthcare is an emerging area of technology that uses a large number of environmental and patient sensors and actuators to monitor and improve patients’ physical and mental condition. Tiny sensors gather data on almost any physiological characteristic that can be used to diagnose health problems. This technology faces some challenging ethical questions, ranging from the small-scale individual issues of trust and efficacy to the societal issues of health and longevity gaps related to economic status. It presents particular problems in combining developing computer/information/media ethics with established medical ethics. This article describes a practice-based ethics approach, considering in particular the areas of privacy, agency, equity and liability. It raises questions that ubiquitous healthcare will force practitioners to face as they develop ubiquitous healthcare systems. Medicine is a controlled profession whose practise is commonly restricted by government-appointed authorities, whereas computer software and hardware development is notoriously lacking in such regimes.