Lagrangian analysis of low level anthropogenic plume processing across the North Atlantic

The photochemical evolution of an anthropogenic plume from the New-York/Boston region during its transport at low altitudes over the North Atlantic to the European west coast has been studied using a Lagrangian framework. This plume, originally strongly polluted, was sampled by research aircraft just off the North American east coast on 3 successive days, and 3 days downwind off the west coast of Ireland where another aircraft re-sampled a weakly polluted plume. Changes in trace gas concentrations during transport were reproduced using a photochemical trajectory model including deposition and mixing effects. Chemical and wet deposition processing dominated the evolution of all pollutants in the plume. The mean net O3 production was evaluated to be -5 ppbv/day leading to low values of O3 by the time the plume reached Europe. Wet deposition of nitric acid was responsible for an 80% reduction in this O3 production. If the plume had not encountered precipitation, it would have reached the Europe with O3 levels up to 80-90 ppbv, and CO levels between 120 and 140 ppbv. Photochemical destruction also played a more important role than mixing in the evolution of plume CO due to high levels of both O3 and water vapour showing that CO cannot always be used as a tracer for polluted air masses, especially for plumes transported at low altitudes. The results also show that, in this case, an important increase in the O3/CO slope can be attributed to chemical destruction of CO and not to photochemical O3 production as is often assumed.

Age relationships beteen low level rock platforms and the high coastal slopes in Jersey and the surrounding area.

Recent research along the coastal cliffs and embayments of Jersey has revealed new aspects of the geomorphology of the rocky shore platform and its relationship with the steep slopes that link it to the island plateau above. Specifically, a rockhead platform meets a 10-30 m high, near vertical cliff at approximately 8-10 m above Jersey Datum (J.D.= ±0 m Ordnance Datum; likewise Guernsey Datum: G.D.), slopes down-towards mid-tide levels becoming ever more deeply dissected. Generalised contours of this platform show it to be distinct from a lower tidal rockhead platform which is comparatively smooth over large areas as it undergoes continuing contemporary abrasion. This lower platform is generally separated from the higher one by low cliffs, less than a metre high at mid-tidal levels, but two to three metres at the base of the backing cliffs. Both of these platforms are shown to antedate the Last Cold Stage (Devensian) head at a number of localities and this relationship is taken to represent the general situation, not only in Jersey, but throughout the other Channel Islands and adjacent coasts of Armorica. Whether either, or both, of these two platforms are older than Marine Oxygen Isotope Substage (MOIS) 5e (Ipswichian) as well is not known. However the considerable age of the numerous and wide intertidal shore platforms of the Channel Islands and adjacent coasts of Amorica makes a greater age quite possible.

Motion detection and motion verbs: language affects low-level visual perception

Recent theories propose that semantic representation and sensorimotor processing have a common substrate via simulation. We tested the prediction that comprehension interacts with perception, using a standard psychophysics methodology.While passively listening to verbs that referred to upward or downward motion, and to control verbs that did not refer to motion, 20 subjects performed a motion-detection task, indicating whether or not they saw motion in visual stimuli containing threshold levels of coherent vertical motion. A signal detection analysis revealed that when verbs were directionally incongruent with the motion signal, perceptual sensitivity was impaired. Word comprehension also affected decision criteria and reaction times, but in different ways. The results are discussed with reference to existing explanations of embodied processing and the potential of psychophysical methods for assessing interactions between language and perception.

Meteorological factors controlling low-level continental pollutant outflow across a coast

Coastal outflow describes the horizontal advection of pollutants from the continental boundary layer across a coastline into a layer above the marine boundary layer. This process can ventilate polluted continental boundary layers and thus regulate air quality in highly populated coastal regions. This paper investigates the factors controlling coastal outflow and quantifies its importance as a ventilation mechanism. Tracers in the Met Office Unified Model (MetUM) are used to examine the magnitude and variability of coastal outflow over the eastern United States for a 4 week period during summer 2004. Over the 4 week period, ventilation of tracer from the continental boundary layer via coastal outflow occurs with the same magnitude as vertical ventilation via convection and advection. The relative importance of tracer decay rate, cross-coastal advection rate, and a parameter based on the relative continental and marine boundary layer heights, on coastal outflow is assessed by reducing the problem to a time-dependent box-model. The ratio of the advection rate and decay rate is a dimensionless parameter which determines whether tracers are long-lived or short-lived. Long- and short-lived tracers exhibit different behaviours with respect to coastal outflow. For short-lived tracers, increasing the advection rate increases the diurnally averaged magnitude of coastal outflow, but has the opposite effect for very long-lived tracers. Short-lived tracers exhibit large diurnal variability in coastal outflow but long-lived tracers do not. By combining the MetUM and box-model simulations a landwidth is determined which represents the distance inland over which emissions contribute significantly to coastal outflow. A landwidth of between 100 and 400 km is found to be representative for a tracer with a lifetime of 24 h.

Faces and awareness: low-level, not emotional factors determine perceptual dominance

Threat-relevant stimuli such as fear faces are prioritized by the human visual system. Recent research suggests that this prioritization begins during unconscious processing: A specialized (possibly subcortical) pathway evaluates the threat relevance of visual input, resulting in preferential access to awareness for threat stimuli. Our data challenge this claim. We used a continuous flash suppression (CFS) paradigm to present emotional face stimuli outside of awareness. It has been shown using CFS that salient (e.g., high contrast) and recognizable stimuli (faces, words) become visible more quickly than less salient or less recognizable stimuli. We found that although fearful faces emerge from suppression faster than other faces, this was wholly explained by their low-level visual properties, rather than their emotional content. We conclude that, in the competition for visual awareness, the visual system prefers and promotes unconscious stimuli that are more “face-like,” but the emotional content of a face has no effect on stimulus salience.

Low-level mixing height detection in coastal locations with a scanning Doppler lidar

Mixing layer height (MLH) is one of the key parameters in describing lower tropospheric dynamics and capturing its diurnal variability is crucial, especially for interpreting surface observations. In this paper we introduce a method for identifying MLH below the minimum range of a scanning Doppler lidar when operated at vertical. The method we propose is based on velocity variance in low-elevation-angle conical scanning and is applied to measurements in two very different coastal environments: Limassol, Cyprus, during summer and Loviisa, Finland, during winter. At both locations, the new method agrees well with MLH derived from turbulent kinetic energy dissipation rate profiles obtained from vertically pointing measurements. The low-level scanning routine frequently indicated non-zero MLH less than 100 m above the surface. Such low MLHs were more common in wintertime Loviisa on the Baltic Sea coast than during summertime in Mediterranean Limassol.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

A micro-level simulation for the prediction of intention and behaviour

In this contribution we aim at anchoring Agent-Based Modeling (ABM) simulations in actual models of human psychology. More specifically, we apply unidirectional ABM to social psychological models using low level agents (i.e., intra-individual) to examine whether they generate better predictions, in comparison to standard statistical approaches, concerning the intentions of performing a behavior and the behavior. Moreover, this contribution tests to what extent the predictive validity of models of attitude such as the Theory of Planned Behavior (TPB) or Model of Goal-directed Behavior (MGB) depends on the assumption that peoples’ decisions and actions are purely rational. Simulations were therefore run by considering different deviations from rationality of the agents with a trembling hand method. Two data sets concerning respectively the consumption of soft drinks and physical activity were used. Three key findings emerged from the simulations. First, compared to standard statistical approach the agent-based simulation generally improves the prediction of behavior from intention. Second, the improvement in prediction is inversely proportional to the complexity of the underlying theoretical model. Finally, the introduction of varying degrees of deviation from rationality in agents’ behavior can lead to an improvement in the goodness of fit of the simulations. By demonstrating the potential of ABM as a complementary perspective to evaluating social psychological models, this contribution underlines the necessity of better defining agents in terms of psychological processes before examining higher levels such as the interactions between individuals.

Wind pressure on a single building immersed in a low-jet wind profile

This paper for the first time discuss the wind pressure distribution on the building surface immersed in wind profile of low-level jet rather than a logarithmic boundary-layer profile. Two types of building models are considered, low-rise and high-rise building, relative to the low-level jet height. CFD simulation is carried out. The simulation results show that the wind pressure distribution immersed in a low-jet wine profile is very different from the typical uniform and boundary-layer flow. For the low-rise building, the stagnation point is located at the upper level of windward façade for the low-level jet wind case, and the separation zone above the roof top is not as obvious as the uniform case. For the high-rise building model, the height of stagnation point is almost as high as the low-level jet height.

The interaction of moist convection and mid-level dry air in the advance of the onset of the Indian monsoon

The advance of the onset of the Indian monsoon is here explained in terms of a balance between the low-level monsoon flow and an over-running intrusion of mid-tropospheric dry air. The monsoon advances, over a period of about 6 weeks, from the south of the country to the northwest. Given that the low-level monsoon winds are westerly or southwesterly, and the midlevel winds northwesterly, the monsoon onset propagates upwind relative to midlevel flow, and perpendicular to the low-level flow, and is not directly caused by moisture flux toward the northwest. Lacking a conceptual model for the advance means that it has been hard to understand and correct known biases in weather and climate prediction models. The mid-level northwesterlies form a wedge of dry air that is deep in the far northwest of India and over-runs the monsoon flow. The dry layer is moistened from below by shallow cumulus and congestus clouds, so that the profile becomes much closer to moist adiabatic, and the dry layer is much shallower in the vertical, toward the southeast of India. The profiles associated with this dry air show how the most favourable environment for deep convection occurs in the south, and onset occurs here first. As the onset advances across India, the advection of moisture from the Arabian Sea becomes stronger, and the mid-level dry air is increasingly moistened from below. This increased moistening makes the wedge of dry air shallower throughout its horizontal extent, and forces the northern limit of moist convection to move toward the northwest. Wetting of the land surface by rainfall will further reinforce the north-westward progression, by sustaining the supply of boundary layer moisture and shallow cumulus. The local advance of the monsoon onset is coincident with weakening of the mid-level northwesterlies, and therefore weakened mid-level dry advection.

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.