Browsing by Author "Bigg, Grant R."

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    A combined control systems and machine learning approach to forecasting iceberg flux off Newfoundland
    (MDPI, 2021-07-09) Ross, Jennifer B.; Bigg, Grant R.; Zhao, Yifan; Hanna, Edward
    Icebergs have long been a threat to shipping in the NW Atlantic and the iceberg season of February to late summer is monitored closely by the International Ice Patrol. However, reliable predictions of the severity of a season several months in advance would be useful for planning monitoring strategies and also for shipping companies in designing optimal routes across the North Atlantic for specific years. A seasonal forecast model of the build-up of seasonal iceberg numbers has recently become available, beginning to enable this longer-term planning of marine operations. Here we discuss extension of this control systems model to include more recent years within the trial ensemble sample set and also increasing the number of measures of the iceberg season that are considered within the forecast. These new measures include the seasonal iceberg total, the rate of change of the seasonal increase, the number of peaks in iceberg numbers experienced within a given season, and the timing of the peak(s). They are predicted by a range of machine learning tools. The skill levels of the new measures are tested, as is the impact of the extensions to the existing seasonal forecast model. We present a forecast for the 2021 iceberg season, predicting a medium iceberg year.
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    Forecasting the severity of the Newfoundland iceberg season using a control systems model
    (Taylor and Francis, 2019-06-20) Bigg, Grant R.; Zhao, Yifan; Hanna, Edward
    The iceberg hazard for the Grand Banks area to the east of Newfoundland varies dramatically from one year to the next. In some years no icebergs penetrate south of 48°N, while in others well over 1000 icebergs enter the main shipping lanes between Europe and NE North America. Advance knowledge of this seasonal hazard would have major implications for ship routing, as well as the resources required for maintaining an effective ice hazard service. Here, a Windowed Error Reduction Ratio control system identification approach is used to forecast the severity of the 2018 iceberg season off Newfoundland, in terms of the predicted number of icebergs crossing 48°N, as well as to hindcast iceberg numbers for 2017. The best estimates are for 766 ± 297 icebergs crossing 48°N before the end of September 2017 and 685 ± 207 for 2018. These are both above the recent observed average of 592 icebergs for that date, and substantially so for 2017. Given the bimodal nature of the annual iceberg number, this means that our predictions for both 2017 and 2018 are for a high iceberg season, with a 71% level of confidence. However, it is most likely that the 2018 iceberg numbers will be somewhat less than 1000, while our higher hindcast for 2017 is consistent with the observed level of 1008. Our verification analysis, covering the 20-year period up to 2016, shows our model's correspondence to the high or low nature of the 48°N iceberg numbers is statistically robust to the 0.05% level, with a skill level of 80%.
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    Inferring the variation of climatic and glaciological contributions to West Greenland iceberg discharge in the twentieth century
    (Elsevier, 2016-08-17) Zhao, Yifan; Bigg, Grant R.; Billings, Steve A.; Hanna, Edward; Sole, Andrew J.; Wei, Hua-Liang; Kadirkamanathan, Visakan; Wilton, David J.
    Iceberg discharge is a major component of the mass balance of the Greenland Ice Sheet (GrIS). While bulk estimates of discharge variation over time exist, inferred remotely from measurements of grounding line ice velocities or surface mass balance calculations, few detailed measurements of discharge itself from individual marine-terminating glaciers existed until recent years. Recently, it has been shown, through a combination of ocean–iceberg modelling and non-linear system identification, that the century-long record of iceberg numbers crossing 48oN in the West Atlantic is a good first-order proxy for discharge from at least south and west Greenland. Here, we explore the varying relative importance of ice sheet, oceanic and climatic forcing of iceberg discharge from these areas over the twentieth century, by carrying out sensitivity studies of a non-linear auto-regressive mathematical model of the 48oN time series. We find that the relationships are mainly non-linear, with the contribution of the GrIS surface mass balance to iceberg discharge likely to be dominant in the first half of the century. This period is followed by several decades where oceanic temperature effects are most important in determining the model variation in iceberg discharge. In recent decades, all physical processes play a non-negligible part in explaining the iceberg discharge and the model suggests that the glacial response time to environmental changes may have decreased
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    Tracking nonlinear correlation for complex dynamic systems using a windowed error reduction ratio method
    (Hindawi / Wiley, 2017-11-06) Zhao, Yifan; Hanna, Edward; Bigg, Grant R.; Zhao, Yitian
    Studying complex dynamic systems is usually very challenging due to limited prior knowledge and high complexity of relationships between interconnected components. Current methods either are like a “black box” that is difficult to understand and relate back to the underlying system or have limited universality and applicability due to too many assumptions. This paper proposes a time-varying Nonlinear Finite Impulse Response model to estimate the multiple features of correlation among measurements including direction, strength, significance, latency, correlation type, and nonlinearity. The dynamic behaviours of correlation are tracked through a sliding window approach based on the Blackman window rather than the simple truncation by a Rectangular window. This method is particularly useful for a system that has very little prior knowledge and the interaction between measurements is nonlinear, time-varying, rapidly changing, or of short duration. Simulation results suggest that the proposed tracking approach significantly reduces the sensitivity of correlation estimation against the window size. Such a method will improve the applicability and robustness of correlation analysis for complex systems. A real application to environmental changing data demonstrates the potential of the proposed method by revealing and characterising hidden information contained within measurements, which is usually “invisible” for conventional methods.