The factor structure of the Edinburgh postnatal depression scale in a South African peri-urban settlement

The factor structure of the Edinburgh Postnatal Depression scale (EPDS) and similar instruments have received little attention in the literature. The researchers set out to investigate the construct validity and reliability of the EPDS amongst impoverished South African women. The EPDS was translated into isiXhosa (using Brislin's back translation method) and administered by trained interviewers to 147 women in Khayelitsha, South Africa. Responses were subjected to maximum likelihood confirmatory factor analysis. A single factor structure was found, consistent with the theory on which the EPDS was based. Internal consistency was satisfactory (a = 0.89).

A comparative study of Java and C performance in two large-scale parallel applications

In the 1990s the Message Passing Interface Forum defined MPI bindings for Fortran, C, and C++. With the success of MPI these relatively conservative languages have continued to dominate in the parallel computing community. There are compelling arguments in favour of more modern languages like Java. These include portability, better runtime error checking, modularity, and multi-threading. But these arguments have not converted many HPC programmers, perhaps due to the scarcity of full-scale scientific Java codes, and the lack of evidence for performance competitive with C or Fortran. This paper tries to redress this situation by porting two scientific applications to Java. Both of these applications are parallelized using our thread-safe Java messaging system—MPJ Express. The first application is the Gadget-2 code, which is a massively parallel structure formation code for cosmological simulations. The second application uses the finite-domain time-difference method for simulations in the area of computational electromagnetics. We evaluate and compare the performance of the Java and C versions of these two scientific applications, and demonstrate that the Java codes can achieve performance comparable with legacy applications written in conventional HPC languages. Copyright © 2009 John Wiley & Sons, Ltd.

Uncertainty in climate change impacts on basin-scale freshwater resources – preface to the special issue: the QUEST-GSI methodology and synthesis of results

This paper presents a preface to this Special Issue on the results of the QUEST-GSI (Global Scale Impacts) project on climate change impacts on catchment-scale water resources. A detailed description of the unified methodology, subsequently used in all studies in this issue, is provided. The project method involved running simulations of catchment-scale hydrology using a unified set of past and future climate scenarios, to enable a consistent analysis of the climate impacts around the globe. These scenarios include "policy-relevant" prescribed warming scenarios. This is followed by a synthesis of the key findings. Overall, the studies indicate that in most basins the models project substantial changes to river flow, beyond that observed in the historical record, but that in many cases there is considerable uncertainty in the magnitude and sign of the projected changes. The implications of this for adaptation activities are discussed.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

A method for assessing confidence in requirements analysis

Context: During development managers, analysts and designers often need to know whether enough requirements analysis work has been done and whether or not it is safe to proceed to the design stage. Objective: This paper describes a new, simple and practical method for assessing our confidence in a set of requirements. Method: We identified 4 confidence factors and used a goal oriented framework with a simple ordinal scale to develop a method for assessing confidence. We illustrate the method and show how it has been applied to a real systems development project. Results: We show how assessing confidence in the requirements could have revealed problems in this project earlier and so saved both time and money. Conclusion: Our meta-level assessment of requirements provides a practical and pragmatic method that can prove useful to managers, analysts and designers who need to know when sufficient requirements analysis has been performed.

Evaluation of an improved tracer method to monitor cheese curd syneresis at varying milk fat levels in a cheese vat

Previous studies have reported that cheese curd syneresis kinetics can be monitored by dilution of chemical tracers, such as Blue Dextran, in whey. The objective of this study was to evaluate an improved tracer method to monitor whey volumes expelled over time during syneresis. Two experiments with different ranges of milk fat (0-5% and 2.3-3.5%) were carried out in an 11 L double-O laboratory scale cheese vat. Tracer was added to the curd-whey mixture during the cutting phase of cheese making and samples were taken at 10 min intervals up to 75 min after cutting. The volume of whey expelled was measured gravimetrically and the dilution of tracer in the whey was measured by absorbance at 620 nm. The volumes of whey expelled were significantly reduced at higher milk fat levels. Whey yield was predicted with a SEP ranging from 3.2 to 6.3 g whey/100 mL of milk and a CV ranging from 2.03 to 2.7% at different milk fat levels.

A method for the economic valuation of animal welfare benefits using a single welfare score

Unless the benefits to society of measures to protect and improve the welfare of animals are made transparent by means of their valuation they are likely to go unrecognised and cannot easily be weighed against the costs of such measures as required, for example, by policy-makers. A simple single measure scoring system, based on the Welfare Quality® index, is used, together with a choice experiment economic valuation method, to estimate the value that people place on improvements to the welfare of different farm animal species measured on a continuous (0-100) scale. Results from using the method on a survey sample of some 300 people show that it is able to elicit apparently credible values. The survey found that 96% of respondents thought that we have a moral obligation to safeguard the welfare of animals and that over 72% were concerned about the way farm animals are treated. Estimated mean annual willingness to pay for meat from animals with improved welfare of just one point on the scale was £5.24 for beef cattle, £4.57 for pigs and £5.10 for meat chickens. Further development of the method is required to capture the total economic value of animal welfare benefits. Despite this, the method is considered a practical means for obtaining economic values that can be used in the cost-benefit appraisal of policy measures intended to improve the welfare of animals.

A new method for the determination of Holocene palaeohydrology

This paper describes a new method for the assessment of palaeohydrology through the Holocene. A palaeoclimate model was linked with a hydrological model, using a weather generator to correct bias in the rainfall estimates, to simulate the changes in the flood frequency and the groundwater response through the late Pleistocene and Holocene for the Wadi Faynan in southern Jordan, a site considered internationally important due to its rich archaeological heritage spanning the Pleistocene and Holocene. This is the first study to describe the hydrological functioning of the Wadi Faynan, a meso-scale (241 km2) semi-arid catchment, setting this description within the framework of contemporary archaeological investigations. Historic meteorological records were collated and supplemented with new hydrological and water quality data. The modelled outcomes indicate that environmental changes, such as deforestation, had a major impact on the local water cycle and this amplified the effect of the prevailing climate on the flow regime. The results also show that increased rainfall alone does not necessarily imply better conditions for farming and highlight the importance of groundwater. The discussion focuses on the utility of the method and the importance of the local hydrology to the sustained settlement of the Wadi Faynan through pre-history and history.

An inverse method for determining source characteristics for emergency response applications

Following a malicious or accidental atmospheric release in an outdoor environment it is essential for first responders to ensure safety by identifying areas where human life may be in danger. For this to happen quickly, reliable information is needed on the source strength and location, and the type of chemical agent released. We present here an inverse modelling technique that estimates the source strength and location of such a release, together with the uncertainty in those estimates, using a limited number of measurements of concentration from a network of chemical sensors considering a single, steady, ground-level source. The technique is evaluated using data from a set of dispersion experiments conducted in a meteorological wind tunnel, where simultaneous measurements of concentration time series were obtained in the plume from a ground-level point-source emission of a passive tracer. In particular, we analyze the response to the number of sensors deployed and their arrangement, and to sampling and model errors. We find that the inverse algorithm can generate acceptable estimates of the source characteristics with as few as four sensors, providing these are well-placed and that the sampling error is controlled. Configurations with at least three sensors in a profile across the plume were found to be superior to other arrangements examined. Analysis of the influence of sampling error due to the use of short averaging times showed that the uncertainty in the source estimates grew as the sampling time decreased. This demonstrated that averaging times greater than about 5min (full scale time) lead to acceptable accuracy.

A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems

We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The “disaggregation” approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a “zero-order” model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set “truth.” Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events

The development of NWP models with grid spacing down to 1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.

A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms

The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical downscaling approach in combination with dynamical downscaling is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical downscaling. This new tool can be easily applied to large ensembles of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.

Large-scale two-dimensional turbulence in the atmosphere

Global FGGE data are used to investigate several aspects of large-scale turbulence in the atmosphere. The approach follows that for two-dimensional, nondivergent turbulent flows which are homogeneous and isotropic on the sphere. Spectra of kinetic energy, enstrophy and available potential energy are obtained for both the stationary and transient parts of the flow. Nonlinear interaction terms and fluxes of energy and enstrophy through wavenumber space are calculated and compared with the theory. A possible method of parameterizing the interactions with unresolved scales is considered. Two rather different flow regimes are found in wavenumber space. The high-wavenumber regime is dominated by the transient components of the flow and exhibits, at least approximately, several of the conditions characterizing homogeneous and isotropic turbulence. This region of wavenumber space also displays some of the features of an enstrophy-cascading inertial subrange. The low-wavenumber region, on the other hand, is dominated by the stationary component of the flow, exhibits marked anisotropy and, in contrast to the high-wavenumber regime, displays a marked change between January and July.

Identification of micro-scale anthropogenic CO2, heat and moisture sources – processing eddy covariance fluxes for a dense urban environment

Anthropogenic emissions of heat and exhaust gases play an important role in the atmospheric boundary layer, altering air quality, greenhouse gas concentrations and the transport of heat and moisture at various scales. This is particularly evident in urban areas where emission sources are integrated in the highly heterogeneous urban canopy layer and directly linked to human activities which exhibit significant temporal variability. It is common practice to use eddy covariance observations to estimate turbulent surface fluxes of latent heat, sensible heat and carbon dioxide, which can be attributed to a local scale source area. This study provides a method to assess the influence of micro-scale anthropogenic emissions on heat, moisture and carbon dioxide exchange in a highly urbanized environment for two sites in central London, UK. A new algorithm for the Identification of Micro-scale Anthropogenic Sources (IMAS) is presented, with two aims. Firstly, IMAS filters out the influence of micro-scale emissions and allows for the analysis of the turbulent fluxes representative of the local scale source area. Secondly, it is used to give a first order estimate of anthropogenic heat flux and carbon dioxide flux representative of the building scale. The algorithm is evaluated using directional and temporal analysis. The algorithm is then used at a second site which was not incorporated in its development. The spatial and temporal local scale patterns, as well as micro-scale fluxes, appear physically reasonable and can be incorporated in the analysis of long-term eddy covariance measurements at the sites in central London. In addition to the new IMAS-technique, further steps in quality control and quality assurance used for the flux processing are presented. The methods and results have implications for urban flux measurements in dense urbanised settings with significant sources of heat and greenhouse gases.