An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies

Time series of global and regional mean Surface Air Temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies and Simple Kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, Root Mean Square Errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Non-interpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979-2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

Using drama techniques for the teaching of early years science : a case study

This paper investigates the effect of drama techniques when employed to facilitate teaching and learning early years science. The focus is a lesson intervention designed for a group of children aged between four and five years old. A number of different drama techniques, such as teacher in role, hot seating and miming, were employed for the teaching of the water cycle. The techniques were implemented based on their nature and on what they can offer to young children considering their previous experiences. Before the beginning of the intervention, six children were randomly selected from the whole class, who were interviewed, aiming to identify their initial ideas in regards to the water cycle. The same children were interviewed after the end of the intervention in an attempt to identify the ways in which their initial ideas were changed. The results appear to be promising in terms of facilitating children’s scientific understanding and show an improvement in the children’s use of vocabulary in relation to the specific topic.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Observing wind, aerosol particles, clouds and precipitation: Finland's new ground-based remote-sensing network

The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances

Active remote sensing of marine boundary-layer clouds is challenging as drizzle drops often dominate the observed radar reflectivity. We present a new method to simultaneously retrieve cloud and drizzle vertical profiles in drizzling boundary-layer clouds using surface-based observations of radar reflectivity, lidar attenuated backscatter, and zenith radiances under conditions when precipitation does not reach the surface. Specifically, the vertical structure of droplet size and water content of both cloud and drizzle is characterised throughout the cloud. An ensemble optimal estimation approach provides full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from large-eddy simulation snapshots of cumulus under stratocumulus, where cloud water path is retrieved with an error of 31 g m−2 . The method also performs well in non-drizzling clouds where no assumption of the cloud profile is required. We then apply the method to observations of marine stratocumulus obtained during the Atmospheric Radiation Measurement MAGIC deployment in the Northeast Pacific. Here, retrieved cloud water path agrees well with independent three-channel microwave radiometer retrievals, with a root mean square difference of 10–20 g m−2.

Predicting how many animals will be where: how to build, calibrate and evaluate individual-based models

Individual-based models (IBMs) can simulate the actions of individual animals as they interact with one another and the landscape in which they live. When used in spatially-explicit landscapes IBMs can show how populations change over time in response to management actions. For instance, IBMs are being used to design strategies of conservation and of the exploitation of fisheries, and for assessing the effects on populations of major construction projects and of novel agricultural chemicals. In such real world contexts, it becomes especially important to build IBMs in a principled fashion, and to approach calibration and evaluation systematically. We argue that insights from physiological and behavioural ecology offer a recipe for building realistic models, and that Approximate Bayesian Computation (ABC) is a promising technique for the calibration and evaluation of IBMs. IBMs are constructed primarily from knowledge about individuals. In ecological applications the relevant knowledge is found in physiological and behavioural ecology, and we approach these from an evolutionary perspective by taking into account how physiological and behavioural processes contribute to life histories, and how those life histories evolve. Evolutionary life history theory shows that, other things being equal, organisms should grow to sexual maturity as fast as possible, and then reproduce as fast as possible, while minimising per capita death rate. Physiological and behavioural ecology are largely built on these principles together with the laws of conservation of matter and energy. To complete construction of an IBM information is also needed on the effects of competitors, conspecifics and food scarcity; the maximum rates of ingestion, growth and reproduction, and life-history parameters. Using this knowledge about physiological and behavioural processes provides a principled way to build IBMs, but model parameters vary between species and are often difficult to measure. A common solution is to manually compare model outputs with observations from real landscapes and so to obtain parameters which produce acceptable fits of model to data. However, this procedure can be convoluted and lead to over-calibrated and thus inflexible models. Many formal statistical techniques are unsuitable for use with IBMs, but we argue that ABC offers a potential way forward. It can be used to calibrate and compare complex stochastic models and to assess the uncertainty in their predictions. We describe methods used to implement ABC in an accessible way and illustrate them with examples and discussion of recent studies. Although much progress has been made, theoretical issues remain, and some of these are outlined and discussed.

An annotation-free method for evaluating privacy protection techniques in videos

While several privacy protection techniques are pre- sented in the literature, they are not complemented with an established objective evaluation method for their assess- ment and comparison. This paper proposes an annotation- free evaluation method that assesses the two key aspects of privacy protection that are privacy and utility. Unlike some existing methods, the proposed method does not rely on the use of subjective judgements and does not assume a spe- cific target type in the image data. The privacy aspect is quantified as an appearance similarity and the utility aspect is measured as a structural similarity between the original raw image data and the privacy-protected image data. We performed an extensive experimentation using six challeng- ing datasets (including two new ones) to demonstrate the effectiveness of the evaluation method by providing a per- formance comparison of four state-of-the-art privacy pro- tection techniques.

Collocating satellite-based radar and radiometer measurements — methodology and usage examples

Collocations between two satellite sensors are occasions where both sensors observe the same place at roughly the same time. We study collocations between the Microwave Humidity Sounder (MHS) on-board NOAA-18 and the Cloud Profiling Radar (CPR) on-board CloudSat. First, a simple method is presented to obtain those collocations and this method is compared with a more complicated approach found in literature. We present the statistical properties of the collocations, with particular attention to the effects of the differences in footprint size. For 2007, we find approximately two and a half million MHS measurements with CPR pixels close to their centrepoints. Most of those collocations contain at least ten CloudSat pixels and image relatively homogeneous scenes. In the second part, we present three possible applications for the collocations. Firstly, we use the collocations to validate an operational Ice Water Path (IWP) product from MHS measurements, produced by the National Environment Satellite, Data and Information System (NESDIS) in the Microwave Surface and Precipitation Products System (MSPPS). IWP values from the CloudSat CPR are found to be significantly larger than those from the MSPPS. Secondly, we compare the relation between IWP and MHS channel 5 (190.311 GHz) brightness temperature for two datasets: the collocated dataset, and an artificial dataset. We find a larger variability in the collocated dataset. Finally, we use the collocations to train an Artificial Neural Network and describe how we can use it to develop a new MHS-based IWP product. We also study the effect of adding measurements from the High Resolution Infrared Radiation Sounder (HIRS), channels 8 (11.11 μm) and 11 (8.33 μm). This shows a small improvement in the retrieval quality. The collocations described in the article are available for public use.

Methane cross-validation between three Fourier Transform Spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three datasets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier Transform Spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier Transform Infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Lab at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1° of freedom per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS–PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ± 3 × 1022 molecules cm−2. Expressed as median ± median absolute deviation (expressed in absolute or relative terms), these differences are 0.11 ± 9.60 × 10^20 molecules cm−2 (0.012 ± 1.018 %) for TANSO-FTS–PEARL-FTS, −2.6 ± 2.6 × 10^21 molecules cm−2 (−1.6 ± 1.6 %) for ACE-FTS–PEARL-FTS, and 7.4 ± 6.0 × 10^20 molecules cm−2 (0.78 ± 0.64 %) for TANSO-FTS–ACE-FTS. The differences for ACE-FTS–PEARL-FTS and TANSO-FTS–PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS–ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns.

Human resource allocation management in multiple projects using sociometric techniques

This article describes a new application of key psychological concepts in the area of Sociometry for the selection of workers within organizations in which projects are developed. The project manager can use a new procedure to determine which individuals should be chosen from a given pool of resources and how to combine them into one or several simultaneous groups/projects in order to assure the highest possible overall work efficiency from the standpoint of social interaction. The optimization process was carried out by means of matrix calculations performed using a computer or even manually, and based on a number of new ratios generated ad-hoc and composed on the basis of indices frequently used in Sociometry.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants

Ruminant husbandry is a major source of anthropogenic greenhouse gases (GHG). Filling knowledge gaps and providing expert recommendation are important for defining future research priorities, improving methodologies and establishing science-based GHG mitigation solutions to government and non-governmental organisations, advisory/extension networks, and the ruminant livestock sector. The objectives of this review is to summarize published literature to provide a detailed assessment of the methodologies currently in use for measuring enteric methane (CH4) emission from individual animals under specific conditions, and give recommendations regarding their application. The methods described include respiration chambers and enclosures, sulphur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. This includes automated head chambers (e.g. the GreenFeed system), the use of carbon dioxide (CO2) as a marker, and (handheld) laser CH4 detection. Each of the techniques are compared and assessed on their capability and limitations, followed by methodology recommendations. It is concluded that there is no ‘one size fits all’ method for measuring CH4 emission by individual animals. Ultimately, the decision as to which method to use should be based on the experimental objectives and resources available. However, the need for high throughput methodology e.g. for screening large numbers of animals for genomic studies, does not justify the use of methods that are inaccurate. All CH4 measurement techniques are subject to experimental variation and random errors. Many sources of variation must be considered when measuring CH4 concentration in exhaled air samples without a quantitative or at least regular collection rate, or use of a marker to indicate (or adjust) for the proportion of exhaled CH4 sampled. Consideration of the number and timing of measurements relative to diurnal patterns of CH4 emission and respiratory exchange are important, as well as consideration of feeding patterns and associated patterns of rumen fermentation rate and other aspects of animal behaviour. Regardless of the method chosen, appropriate calibrations and recovery tests are required for both method establishment and routine operation. Successful and correct use of methods requires careful attention to detail, rigour, and routine self-assessment of the quality of the data they provide.

Identification of Hammondia heydorni oocysts by a heminested-PCR (hnPCR-AP10) based on the H-heydorni RAPD fragment AP10

Toxoplasma gondii, Hammondia hammondi, Neospora caninum, Neospora hughesi and Hammondia heydorni are members of the Toxoplasmatinae sub-family. They are closely related coccidians with similarly sized oocysts. Molecular diagnostic techniques, especially those based on polymerase chain reaction (PCR), can be successfully applied for the differentiation of Hammondia-like oocysts. In this paper, we describe a rapid and simple method for the identification of H. heydorni oocysts among other members of the Toxoplasmatinae sub-family, using a heminested-PCR (hnPCR-AP10) based on a H. heydorni RAPD fragment available in molecular database. DNA of oocysts of H. heydorni yielded a specific fragment of 289-290 bp in the heminested-PCR assay. No product was yielded when the primers were used for the amplification of DNA extracted from T. gondii, N. caninum, N. hughesi and H. hammondi, thus allowing the differentiation of H. heydorni among other members of the Toxoplasmatinae sub-family. The hnPCR-AP10 was capable of detecting H. heydorni genetic sequences from suspensions with at least 10 oocysts. In conclusion, the hnPCR-AP10 proved to be a reliable method to be used in the identification of H. heydorni oocysts from feces of dogs. (C) 2010 Elsevier B.V. All rights reserved.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.