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Abstract: Physical damage to property and crops caused by unanticipated wind gusts is a well understood phenomenon. Predicting its occurrence continues to be a challenge for meteorologists and climatologists. Various approaches to gust occurrence model building have been proposed. The very nature of the event is problematic because of its brief duration following a rapid change of state in wind velocity that immediately precedes it. Events classified as wind gusts have a typical duration of less than 20s and are often much shorter. The rapidly accelerating wind velocity preceding them is often not apparent until the gust occurs. They come quickly, occur suddenly, and then end as abruptly as they began. Observations of 2000 gust events were made during the research to which this paper refers. These observations indicated a mean interval of 3.2min between the beginning and end of wind velocity change and a noticeable linear progression in the acceleration pattern. It was also noted that state changes regularly occur, often over only seconds in time. In combination, these factors pose both a sampling and a data interpretation challenge, making reliable prediction difficult. This paper describes some new research undertaken to investigate methods of wind gust measurement and prediction. In particular, a machine-learning approach is taken to determine a satisfactory analytical process and to produce meaningful and useful results. An algorithm for use with real-time climate data collection and analysis is proposed, with a description of its implementation. Real-time data sampling provides input for this study using terrestrial sensor telemetry. Near-ground truth data are recorded independent of geostrophic upper atmosphere conditions. [Copyright &y& Elsevier]

Operating anaerobic reactors at high organic loading rates during start-up can lead to instability, accumulation of volatile fatty acids and low pH, such problems being exacerbated in reactors that exhibit plug-flow characteristics. Moreover, plug-flow conditions increase the exposure of biomass to any toxic components in the feed. To overcome these limitations, an anaerobic baffled reactor (ABR), a reactor exhibiting partial plug-flow characteristics, was modified by splitting the feed between the individual compartments to produce the split-feed ABR (SFABR). Consequently, more favourable conditions were created in the initial compartments, such as lower, longer hydraulic retention time and longer cell retention time; conditions in the final compartments were also improved by the increased food availability for microorganisms. Other benefits included better gas mixing characteristics as a result of the more balanced gas production across the reactor.Granule development was compared in SFABR and normally fed ABR by analysing sludge samples, taken during start-up and continuous operation, using scanning electron microscopy. Photomicrographs allowed tentative conclusions to be made concerning the effect of split-feeding on the distribution of bacterial populations within the granule architecture and the role of extracellular polymers on granule formation. [Copyright &y& Elsevier]

Introduces the Split Feed Anaerobic Baffled Reactor (SFABR) and describes the investigations into the use of modified seed material. Occurrence of granulation processes as revealed by scanning electron photo-micrographs; Advantages of the use of polymer-conditioned anaerobic sludge and granular sludge as seed.