An exploration of the equivalent weights particle filter

Particle filters are fully non-linear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications the number of particles required by the sequential importance resampling (SIR) particle filter in order to capture the high probability region of the posterior, is too large to make them usable. However particle filters can be formulated using proposal densities, which gives greater freedom in how particles are sampled and allows for a much smaller number of particles. Here a particle filter is presented which uses the proposal density to ensure that all particles end up in the high probability region of the posterior probability density function. This gives rise to the possibility of non-linear data assimilation in large dimensional systems. The particle filter formulation is compared to the optimal proposal density particle filter and the implicit particle filter, both of which also utilise a proposal density. We show that when observations are available every time step, both schemes will be degenerate when the number of independent observations is large, unlike the new scheme. The sensitivity of the new scheme to its parameter values is explored theoretically and demonstrated using the Lorenz (1963) model.

Towards a general theory of extremes for observables of chaotic dynamical systems

In this paper we provide a connection between the geometrical properties of the attractor of a chaotic dynamical system and the distribution of extreme values. We show that the extremes of so-called physical observables are distributed according to the classical generalised Pareto distribution and derive explicit expressions for the scaling and the shape parameter. In particular, we derive that the shape parameter does not depend on the cho- sen observables, but only on the partial dimensions of the invariant measure on the stable, unstable, and neutral manifolds. The shape parameter is negative and is close to zero when high-dimensional systems are considered. This result agrees with what was derived recently using the generalized extreme value approach. Combining the results obtained using such physical observables and the properties of the extremes of distance observables, it is possible to derive estimates of the partial dimensions of the attractor along the stable and the unstable directions of the flow. Moreover, by writing the shape parameter in terms of moments of the extremes of the considered observable and by using linear response theory, we relate the sensitivity to perturbations of the shape parameter to the sensitivity of the moments, of the partial dimensions, and of the Kaplan–Yorke dimension of the attractor. Preliminary numer- ical investigations provide encouraging results on the applicability of the theory presented here. The results presented here do not apply for all combinations of Axiom A systems and observables, but the breakdown seems to be related to very special geometrical configurations.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

The effect of the equivalent-weights particle filter on dynamical balance in a primitive equation model

The disadvantage of the majority of data assimilation schemes is the assumption that the conditional probability density function of the state of the system given the observations [posterior probability density function (PDF)] is distributed either locally or globally as a Gaussian. The advantage, however, is that through various different mechanisms they ensure initial conditions that are predominantly in linear balance and therefore spurious gravity wave generation is suppressed. The equivalent-weights particle filter is a data assimilation scheme that allows for a representation of a potentially multimodal posterior PDF. It does this via proposal densities that lead to extra terms being added to the model equations and means the advantage of the traditional data assimilation schemes, in generating predominantly balanced initial conditions, is no longer guaranteed. This paper looks in detail at the impact the equivalent-weights particle filter has on dynamical balance and gravity wave generation in a primitive equation model. The primary conclusions are that (i) provided the model error covariance matrix imposes geostrophic balance, then each additional term required by the equivalent-weights particle filter is also geostrophically balanced; (ii) the relaxation term required to ensure the particles are in the locality of the observations has little effect on gravity waves and actually induces a reduction in gravity wave energy if sufficiently large; and (iii) the equivalent-weights term, which leads to the particles having equivalent significance in the posterior PDF, produces a change in gravity wave energy comparable to the stochastic model error. Thus, the scheme does not produce significant spurious gravity wave energy and so has potential for application in real high-dimensional geophysical applications.

Twin experiments with the equivalent weights particle filter and HadCM3

This paper investigates the use of a particle filter for data assimilation with a full scale coupled ocean–atmosphere general circulation model. Synthetic twin experiments are performed to assess the performance of the equivalent weights filter in such a high-dimensional system. Artificial 2-dimensional sea surface temperature fields are used as observational data every day. Results are presented for different values of the free parameters in the method. Measures of the performance of the filter are root mean square errors, trajectories of individual variables in the model and rank histograms. Filter degeneracy is not observed and the performance of the filter is shown to depend on the ability to keep maximum spread in the ensemble.

Implicit equal-weights particle filter

Filter degeneracy is the main obstacle for the implementation of particle filter in non-linear high-dimensional models. A new scheme, the implicit equal-weights particle filter (IEWPF), is introduced. In this scheme samples are drawn implicitly from proposal densities with a different covariance for each particle, such that all particle weights are equal by construction. We test and explore the properties of the new scheme using a 1,000-dimensional simple linear model, and the 1,000-dimensional non-linear Lorenz96 model, and compare the performance of the scheme to a Local Ensemble Kalman Filter. The experiments show that the new scheme can easily be implemented in high-dimensional systems and is never degenerate, with good convergence properties in both systems.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Four-dimensional variational data assimilation for high resolution nested models

Four-dimensional variational data assimilation (4D-Var) is used in environmental prediction to estimate the state of a system from measurements. When 4D-Var is applied in the context of high resolution nested models, problems may arise in the representation of spatial scales longer than the domain of the model. In this paper we study how well 4D-Var is able to estimate the whole range of spatial scales present in one-way nested models. Using a model of the one-dimensional advection–diffusion equation we show that small spatial scales that are observed can be captured by a 4D-Var assimilation, but that information in the larger scales may be degraded. We propose a modification to 4D-Var which allows a better representation of these larger scales.

Temporal patterns of nutrient availability around nests of leaf-cutting ants (Atta colombica) in secondary moist tropical forest

Leaf-cutting ants consume up to 10% of canopy leaves in the foraging area of their colony and therefore represent a key perturbation in the nutrient cycle of tropical forests. We used a chronosequence of nest sites on Barro, Colorado Island, Panama, to assess the influence of leaf-cutting ants (Atta colombica) on nutrient availability in a neotropical rainforest. Twelve nest sites were sampled, including active nests, recently abandoned nests (<1 year) and long-abandoned nests (>1 year). Waste material discarded by the ants down-slope from the nests contained large concentrations of nitrogen and phosphorus in both total and soluble forms, but decomposed within one year after the nests were abandoned. Despite this, soil under the waste material contained high concentrations of nitrate and ammonium that persisted after the disappearance of the waste, although soluble phosphate returned to background concentrations within one year of nest abandonment. Fine roots were more abundant in soil under waste than control soils up to one year after nest abandonment, but were not significantly different for older sites. In contrast to the waste dumps, soil above the underground nest chambers consistently contained lower nutrient concentrations than control soils, although this was not statistically significant. We conclude that the 'islands of fertility' created by leaf-cutting ants provide a nutritional benefit to nearby plants for less than one year after nest abandonment in the moist tropical environment of Barro Colorado Island. Published by Elsevier Ltd.

Convectively coupled equatorial waves in high resolution Hadley centre climate models

Current global atmospheric models fail to simulate well organised tropical phenomena in which convection interacts with dynamics and physics. A new methodology to identify convectively coupled equatorial waves, developed by NCAS-Climate, has been applied to output from the two latest models of the Met Office/Hadley Centre which have fundamental differences in dynamical formulation. Variability, horizontal and vertical structures, and propagation characteristics of tropical convection and equatorial waves, along with their coupled behaviour in the models are examined and evaluated against a previous comprehensive study of observations. It is shown that, in general, the models perform well for equatorial waves coupled with off-equatorial convection. However they perform poorly for waves coupled with equatorial convection. The vertical structure of the simulated wave is not conducive to energy conversion/growth and does not support the correct physical-dynamical coupling that occurs in the real world. The following figure shows an example of the Kelvin wave coupled with equatorial convection. It shows that the models fail to simulate a key feature of convectively coupled Kelvin wave in observations, namely near surface anomalous equatorial zonal winds together with intensified equatorial convection and westerly winds in phase with the convection. The models are also not able to capture the observed vertical tilt structure and the vertical propagation of the Kelvin wave into the lower stratosphere as well as the secondary peak in the mid-troposphere, particularly in HadAM3. These results can be used to provide a test-bed for experimentation to improve the coupling of physics and dynamics in climate and weather models.

Why the western Pacific subtropical high has extended westward since the late 1970s

The western Pacific subtropical high (WPSH) is closely related to Asian climate. Previous examination of changes in the WPSH found a westward extension since the late 1970s, which has contributed to the inter-decadal transition of East Asian climate. The reason for the westward extension is unknown, however. The present study suggests that this significant change of WPSH is partly due to the atmosphere's response to the observed Indian Ocean-western Pacific (IWP) warming. Coordinated by a European Union's Sixth Framework Programme, Understanding the Dynamics of the Coupled Climate System (DYNAMITE), five AGCMs were forced by identical idealized sea surface temperature patterns representative of the IWP warming and cooling. The results of these numerical experiments suggest that the negative heating in the central and eastern tropical Pacific and increased convective heating in the equatorial Indian Ocean/ Maritime Continent associated with IWP warming are in favor of the westward extension of WPSH. The SST changes in IWP influences the Walker circulation, with a subsequent reduction of convections in the tropical central and eastern Pacific, which then forces an ENSO/Gill-type response that modulates the WPSH. The monsoon diabatic heating mechanism proposed by Rodwell and Hoskins plays a secondary reinforcing role in the westward extension of WPSH. The low-level equatorial flank of WPSH is interpreted as a Kelvin response to monsoon condensational heating, while the intensified poleward flow along the western flank of WPSH is in accord with Sverdrup vorticity balance. The IWP warming has led to an expansion of the South Asian high in the upper troposphere, as seen in the reanalysis.

Children's lives, children's futures: A study of children starting secondary school

This title presents a fascinating analysis of how children in their first year of high school feel about their schools, its place in their lives and its role in their futures. This highly topical monograph focuses on how children in their first year of high school feel about school, its place in their lives and its role in their futures. The theoretical context of the study is the focus in educational studies on children's voice and children's active role in education, together with the focus in the sociology of childhood on children as active constructors of their lives and childhood as a subject of serious study. The importance of young people's life plans and the alignment between education and ambitions was recognized in the Sloan Foundation study of American teenagers. In many Western societies there is concern that children from less advantaged social backgrounds have limited aspirations, and are disproportionately unlikely to go to university. This book is highly relevant to understanding the nature of children's engagement with education, the choices and constraints they experience and the reasons some young people fail to take advantage of educational opportunities. "Continuum Studies in Educational Research" (CSER) is a major new series in the field of educational research. Written by experts and scholars for experts and scholars, this ground-breaking series focuses on research in the areas of comparative education, history, lifelong learning, philosophy, policy, post-compulsory education, psychology and sociology. Based on cutting edge research and written with lucidity and passion, the CSER series showcases only those books that really matter in education - studies that are major, that will be remembered for having made a difference.

Chlorine tolerant, multilayer reverse-somosis membranes with high permeate flux and high salt rejection

A new class of high molecular weight polyethersulfone ionomers is described in which the ionic content can be varied, at will, over a very wide and fully-controllable range. A novel type of coating process enables these materials to be deposited from alcohol-type solvents as cohesive but very thin (50 – 250 nm) films on porous support-membranes, giving high-flux membranes (3.3 – 5.0 L m-2 h-1 bar-1) with very good, though not outstanding salt rejection (typically 92 - 96%). A secondary layer, of formaldehyde-cross-linked polyvinyl alcohol, can be deposited from aqueous solution on the surface of the ionomer membrane, and this layer increases salt rejection to greater than 99% without serious loss of water permeability. The final multi-layer membrane shows excellent chlorine tolerance in reverse-osmosis operation.

Two-dimensional anharmonic oscillator: application to 2,5-dihydrofuran

A program has been developed to calculate the energy levels and corresponding wavefunctions for a two‐dimensional anharmonic potential surface of at least C2v symmetry. This program has been employed to explain the high resolution splittings observed in the far infrared spectrum of 2,5‐dihydrofuran. The magnitude of the cross term connecting the ring‐twisting and ring‐puckering modes of 2,5‐dihydrofuran is sufficiently large to be significant. The potential surface determined also suggests that the ring‐twisting mode may be slightly anharmonic.