Sensitivity of feature-based analysis methods of storm tracks to the form of background field removal

For the tracking of extrema associated with weather systems to be applied to a broad range of fields it is necessary to remove a background field that represents the slowly varying, large spatial scales. The sensitivity of the tracking analysis to the form of background field removed is explored for the Northern Hemisphere winter storm tracks for three contrasting fields from an integration of the U. K. Met Office's (UKMO) Hadley Centre Climate Model (HadAM3). Several methods are explored for the removal of a background field from the simple subtraction of the climatology, to the more sophisticated removal of the planetary scales. Two temporal filters are also considered in the form of a 2-6-day Lanczos filter and a 20-day high-pass Fourier filter. The analysis indicates that the simple subtraction of the climatology tends to change the nature of the systems to the extent that there is a redistribution of the systems relative to the climatological background resulting in very similar statistical distributions for both positive and negative anomalies. The optimal planetary wave filter removes total wavenumbers less than or equal to a number in the range 5-7, resulting in distributions more easily related to particular types of weather system. For the temporal filters the 2-6-day bandpass filter is found to have a detrimental impact on the individual weather systems, resulting in the storm tracks having a weak waveguide type of behavior. The 20-day high-pass temporal filter is less aggressive than the 2-6-day filter and produces results falling between those of the climatological and 2-6-day filters.

Adaptive constraints for feature tracking

In this paper extensions to an existing tracking algorithm are described. These extensions implement adaptive tracking constraints in the form of regional upper-bound displacements and an adaptive track smoothness constraint. Together, these constraints make the tracking algorithm more flexible than the original algorithm (which used fixed tracking parameters) and provide greater confidence in the tracking results. The result of applying the new algorithm to high-resolution ECMWF reanalysis data is shown as an example of its effectiveness.

Feature-point detection using distance transforms: Application to tracking tropical convective complexes

The identification, tracking, and statistical analysis of tropical convective complexes using satellite imagery is explored in the context of identifying feature points suitable for tracking. The feature points are determined based on the shape of complexes using the distance transform technique. This approach has been applied to the determination feature points for tropical convective complexes identified in a time series of global cloud imagery. The feature points are used to track the complexes, and from the tracks statistical diagnostic fields are computed. This approach allows the nature and distribution of organized deep convection in the Tropics to be explored.

Feature tracking on the unit-sphere

Techniques used in a previous study of the objective identification and tracking of meteorological features in model data are extended to the unit sphere. An alternative feature detection scheme is described based on cubic interpolation for the sphere and local maximization. The extension of the tracking technique, used in the previous study, to the unit sphere is described. An example of the application of these techniques to a global relative vorticity field from a model integration are presented and discussed.

A comparison of recent reanalysis datasets using objective feature tracking: Storm tracks and tropical easterly waves

Data from four recent reanalysis projects [ECMWF, NCEP-NCAR, NCEP - Department of Energy ( DOE), NASA] have been diagnosed at the scale of synoptic weather systems using an objective feature tracking method. The tracking statistics indicate that, overall, the reanalyses correspond very well in the Northern Hemisphere (NH) lower troposphere, although differences for the spatial distribution of mean intensities show that the ECMWF reanalysis is systematically stronger in the main storm track regions but weaker around major orographic features. A direct comparison of the track ensembles indicates a number of systems with a broad range of intensities that compare well among the reanalyses. In addition, a number of small-scale weak systems are found that have no correspondence among the reanalyses or that only correspond upon relaxing the matching criteria, indicating possible differences in location and/or temporal coherence. These are distributed throughout the storm tracks, particularly in the regions known for small-scale activity, such as secondary development regions and the Mediterranean. For the Southern Hemisphere (SH), agreement is found to be generally less consistent in the lower troposphere with significant differences in both track density and mean intensity. The systems that correspond between the various reanalyses are considerably reduced and those that do not match span a broad range of storm intensities. Relaxing the matching criteria indicates that there is a larger degree of uncertainty in both the location of systems and their intensities compared with the NH. At upper-tropospheric levels, significant differences in the level of activity occur between the ECMWF reanalysis and the other reanalyses in both the NH and SH winters. This occurs due to a lack of coherence in the apparent propagation of the systems in ERA15 and appears most acute above 500 hPa. This is probably due to the use of optimal interpolation data assimilation in ERA15. Also shown are results based on using the same techniques to diagnose the tropical easterly wave activity. Results indicate that the wave activity is sensitive not only to the resolution and assimilation methods used but also to the model formulation.

Integrating socio-economics and ecology: a taxonomy of quantitative methods and a review of their use in agro-ecology

Answering many of the critical questions in conservation, development and environmental management requires integrating the social and natural sciences. However, understanding the array of available quantitative methods and their associated terminology presents a major barrier to successful collaboration. We provide an overview of quantitative socio-economic methods that distils their complexity into a simple taxonomy. We outline how each has been used in conjunction with ecological models to address questions relating to the management of socio-ecological systems. We review the application of social and ecological quantitative concepts to agro-ecology and classify the approaches used to integrate the two disciplines. Our review included all published integrated models from 2003 to 2008 in 27 journals that publish agricultural modelling research. Although our focus is on agro-ecology, many of the results are broadly applicable to other fields involving an interaction between human activities and ecology. We found 36 papers that integrated social and ecological concepts in a quantitative model. Four different approaches to integration were used, depending on the scale at which human welfare was quantified. Most models viewed humans as pure profit maximizers, both when calculating welfare and predicting behaviour. Synthesis and applications. We reached two main conclusions based on our taxonomy and review. The first is that quantitative methods that extend predictions of behaviour and measurements of welfare beyond a simple market value basis are underutilized by integrated models. The second is that the accuracy of prediction for integrated models remains largely unquantified. Addressing both problems requires researchers to reach a common understanding of modelling goals and data requirements during the early stages of a project.

Genetic characterization of tick-borne flaviviruses: new insights into evolution, pathogenetic determinants and taxonomy

Here, we analyze the complete coding sequences of all recognized tick-borne flavivirus species, including Gadgets Gully, Royal Farm and Karshi virus, seabird-associated flaviviruses, Kadam virus and previously uncharacterized isolates of Kyasanur Forest disease virus and Omsk hemorrhagic fever virus. Significant taxonomic improvements are proposed, e.g. the identification of three major groups (mammalian, seabird and Kadam tick-borne flavivirus groups), the creation of a new species (Karshi virus) and the assignment of Tick-borne encephalitis and Louping ill viruses to a unique species (Tick-borne encephalitis virus) including four viral types (i.e. Western Tick-borne encephalitis virus, Eastern Tick-borne encephalitis virus, Turkish sheep Tick-borne encephalitis virus and Louping ill Tick-borne encephalitis virus). The analyses also suggest a complex relationship between viruses infecting birds and those infecting mammals. Ticks that feed on both categories of vertebrates may constitute the evolutionary bridge between the three distinct identified lineages.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.