Predicting mesh density for adaptive modelling of the global atmosphere

The shallow water equations are solved using a mesh of polygons on the sphere, which adapts infrequently to the predicted future solution. Infrequent mesh adaptation reduces the cost of adaptation and load-balancing and will thus allow for more accurate mapping on adaptation. We simulate the growth of a barotropically unstable jet adapting the mesh every 12 h. Using an adaptation criterion based largely on the gradient of the vorticity leads to a mesh with around 20 per cent of the cells of a uniform mesh that gives equivalent results. This is a similar proportion to previous studies of the same test case with mesh adaptation every 1–20 min. The prediction of the mesh density involves solving the shallow water equations on a coarse mesh in advance of the locally refined mesh in order to estimate where features requiring higher resolution will grow, decay or move to. The adaptation criterion consists of two parts: that resolved on the coarse mesh, and that which is not resolved and so is passively advected on the coarse mesh. This combination leads to a balance between resolving features controlled by the large-scale dynamics and maintaining fine-scale features.

Adjoint goal-based error norms for adaptive mesh ocean modelling

Flow in the world's oceans occurs at a wide range of spatial scales, from a fraction of a metre up to many thousands of kilometers. In particular, regions of intense flow are often highly localised, for example, western boundary currents, equatorial jets, overflows and convective plumes. Conventional numerical ocean models generally use static meshes. The use of dynamically-adaptive meshes has many potential advantages but needs to be guided by an error measure reflecting the underlying physics. A method of defining an error measure to guide an adaptive meshing algorithm for unstructured tetrahedral finite elements, utilizing an adjoint or goal-based method, is described here. This method is based upon a functional, encompassing important features of the flow structure. The sensitivity of this functional, with respect to the solution variables, is used as the basis from which an error measure is derived. This error measure acts to predict those areas of the domain where resolution should be changed. A barotropic wind driven gyre problem is used to demonstrate the capabilities of the method. The overall objective of this work is to develop robust error measures for use in an oceanographic context which will ensure areas of fine mesh resolution are used only where and when they are required. (c) 2006 Elsevier Ltd. All rights reserved.

Improving flood models using LiDAR: progress and problems

Airborne scanning laser altimetry (LiDAR) is an important new data source for river flood modelling. LiDAR can give dense and accurate DTMs of floodplains for use as model bathymetry. Spatial resolutions of 0.5m or less are possible, with a height accuracy of 0.15m. LiDAR gives a Digital Surface Model (DSM), so vegetation removal software (e.g. TERRASCAN) must be used to obtain a DTM. An example used to illustrate the current state of the art will be the LiDAR data provided by the EA, which has been processed by their in-house software to convert the raw data to a ground DTM and separate vegetation height map. Their method distinguishes trees from buildings on the basis of object size. EA data products include the DTM with or without buildings removed, a vegetation height map, a DTM with bridges removed, etc. Most vegetation removal software ignores short vegetation less than say 1m high. We have attempted to extend vegetation height measurement to short vegetation using local height texture. Typically most of a floodplain may be covered in such vegetation. The idea is to assign friction coefficients depending on local vegetation height, so that friction is spatially varying. This obviates the need to calibrate a global floodplain friction coefficient. It’s not clear at present if the method is useful, but it’s worth testing further. The LiDAR DTM is usually determined by looking for local minima in the raw data, then interpolating between these to form a space-filling height surface. This is a low pass filtering operation, in which objects of high spatial frequency such as buildings, river embankments and walls may be incorrectly classed as vegetation. The problem is particularly acute in urban areas. A solution may be to apply pattern recognition techniques to LiDAR height data fused with other data types such as LiDAR intensity or multispectral CASI data. We are attempting to use digital map data (Mastermap structured topography data) to help to distinguish buildings from trees, and roads from areas of short vegetation. The problems involved in doing this will be discussed. A related problem of how best to merge historic river cross-section data with a LiDAR DTM will also be considered. LiDAR data may also be used to help generate a finite element mesh. In rural area we have decomposed a floodplain mesh according to taller vegetation features such as hedges and trees, so that e.g. hedge elements can be assigned higher friction coefficients than those in adjacent fields. We are attempting to extend this approach to urban area, so that the mesh is decomposed in the vicinity of buildings, roads, etc as well as trees and hedges. A dominant points algorithm is used to identify points of high curvature on a building or road, which act as initial nodes in the meshing process. A difficulty is that the resulting mesh may contain a very large number of nodes. However, the mesh generated may be useful to allow a high resolution FE model to act as a benchmark for a more practical lower resolution model. A further problem discussed will be how best to exploit data redundancy due to the high resolution of the LiDAR compared to that of a typical flood model. Problems occur if features have dimensions smaller than the model cell size e.g. for a 5m-wide embankment within a raster grid model with 15m cell size, the maximum height of the embankment locally could be assigned to each cell covering the embankment. But how could a 5m-wide ditch be represented? Again, this redundancy has been exploited to improve wetting/drying algorithms using the sub-grid-scale LiDAR heights within finite elements at the waterline.

A data collection case study supporting requirements oriented prediction and management in software developments

Consider the statement "this project should cost X and has risk of Y". Such statements are used daily in industry as the basis for making decisions. The work reported here is part of a study aimed at providing a rational and pragmatic basis for such statements. Of particular interest are predictions made in the requirements and early phases of projects. A preliminary model has been constructed using Bayesian Belief Networks and in support of this, a programme to collect and study data during the execution of various software development projects commenced in May 2002. The data collection programme is undertaken under the constraints of a commercial industrial regime of multiple concurrent small to medium scale software development projects. Guided by pragmatism, the work is predicated on the use of data that can be collected readily by project managers; including expert judgements, effort, elapsed times and metrics collected within each project.

Discriminative pattern mining in software fault detection

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

Why high seed densities within buried mesh bags may overestimate depletion rates of soil seed banks

1. Estimates of seed bank depletion rates are essential for modelling and management of plant populations. The seed bag burial method is often used to measure seed mortality in the soil. However, the density of seeds within seed bags is higher than densities in natural seed banks, which may elevate levels of pathogens and influence seed mortality. The aim of this study was to quantify the effects of fungi and seed density within buried mesh bags on the mortality of seeds. Striga hermonthica was chosen as the study species because it has been widely studied but different methods for measuring seed mortality in the soil have yielded contradictory estimates. 2. Seed bags were buried in soil and exhumed at regular time intervals to monitor mortality of the seeds in three field experiments during two rainy seasons. The effect of fungal activity on seed mortality was evaluated in a fungi exclusion experiment. Differences in seed-to-seed interaction were obtained by using two and four densities within the seed bags in consecutive years. Densities were created by mixing 1000 seeds with 0, 10, 100 or 1000 g of coarse sand. 3. The mortality rate was significantly lower when fungi were excluded, indicating the possible role of pathogenic fungi. 4. Decreasing the density of seeds in bags significantly reduced seed mortality, most probably because of decreased seed-to-seed contamination by pathogenic fungi. 5. Synthesis and applications. Models of plant populations in general and annual weeds in particular often use values from the literature for seed bank depletion rates. These depletion rates have often been estimated by the seed bag burial method, yet seed density within seed bags may be unrealistically high. Consequently, estimates of seed mortality rates may be too high because of an overestimation of the effects of soil or seed-borne pathogens. Species that have been classified from such studies as having short-lived seed banks may need to be re-assessed using realistic densities either within seed bags or otherwise. Similarly, models of seed bank dynamics based on such overestimated depletion rates may lead to incorrect conclusions regarding the seed banks and, perhaps, the management of weeds and rare species.

HDC poinsettia tracker: flexible graphical tracking software

Growing pot poinsettia and similar crops involves careful crop monitoring and management to ensure that height specifications are met. Graphical tracking represents a target driven approach to decision support with simple interpretation. HDC (Horticultural Development Council) Poinsettia Tracker implements a graphical track based on the Generalised Logistic Curve, similar to that of other tracking packages. Any set of curve parameters can be used to track crop progress. However, graphical tracks must be expected to be site and cultivar specific. By providing a simple Curve fitting function, growers can easily develop their own site and variety specific ideal tracks based on past records with increasing quality as more seasons' data are added. (C) 2009 Elsevier B.V. All rights reserved.

Teachers as researchers and teachers as software developers: how use-case analysis helps build better educational software

A series of government initiatives has raised both the profile of ICT in the curriculum and the expectation that high quality teaching and learning resources will be accessible across electronic networks. In order for e-learning resources such as websites to have the maximum educational impact, teachers need to be involved in their design and development. Use-case analysis provides a means of defining user requirements and other constraints in such a way that software developers can produce e-learning resources which reflect teachers' professional knowledge and support their classroom practice. It has some features in common with the participatory action research used to develop other aspects of classroom practice. Two case-studies are presented: one involves the development of an on-line resource centred on transcripts of original historical documents; the other describes how 'Learning how to Learn', a major, distributed research project funded under the ESRC Teaching and Learning Research Programme is using use-case analysis to develop web resources and services.

A simulation model of self-organising evolvability in software systems

The evolvability of a software artifact is its capacity for producing heritable or reusable variants; the inverse quality is the artifact's inertia or resistance to evolutionary change. Evolvability in software systems may arise from engineering and/or self-organising processes. We describe our 'Conditional Growth' simulation model of software evolution and show how, it can be used to investigate evolvability from a self-organisation perspective. The model is derived from the Bak-Sneppen family of 'self-organised criticality' simulations. It shows good qualitative agreement with Lehman's 'laws of software evolution' and reproduces phenomena that have been observed empirically. The model suggests interesting predictions about the dynamics of evolvability and implies that much of the observed variability in software evolution can be accounted for by comparatively simple self-organising processes.