Information gain that convective-scale models bring to probabilistic weather forecasts

Ensemble prediction systems, run nowadays at convective-scale by several operational and research forecasting centres, undoubtedly provide a large amount of data. Whether and how the utilisation of these data can lead to additional valuable information for the probabilistic prediction of specific weather phenomena is an open challenge. Here, the information gain of the Met Office convective-scale ensemble relative to its lower resolution global model counterpart (33 km) is quantified for the sea breeze phenomenon. It occurs on small spatio-temporal scale but influenced by the large-scale conditions, well represented by the global ensembles. Sea breeze is also an atmospheric counterpart of the so called density currents and could lead to high impact weather, especially when colliding with other sea breezes or mesoscale flows. In the first part of the thesis, a new set of numerical simulations of colliding density currents is presented, with the aim of understanding the dynamics of collision. A novel parametric formula for predicting the collision angle is shown to agree well with numerical data. This can be useful for future parametrizations of these processes. Probabilistic forecasts of the occurrence of sea breezes have been then produced, compared and verified over an extended period. This motivated the development of a novel method for the automatic identification of the sea breeze from convective-scale ensembles. This method can be in principle applied to every coastline. A Bayesian approach is instead used to extract information from the coarser resolution ensemble. It is trained on paired high/low resolution ensemble member, but it can be trained also on observations. This method creates a statistical forecast of the high-resolution ensemble member, given the knowledge of the global ensemble predictors alone. In the last part of the thesis, the same methodology has been applied to the prediction of wind gusts. Comparison of the two forecasting methods, using a variety of well established verification metrics all lead to the same conclusion: although the Bayesian forecasts have potential skill for the prediction of event occurrence, the convective-scale ensemble is shown to be more skillful

Interactive Causal Structure Discovery in Earth System Sciences

Peer reviewe

Interactive Causal Structure Discovery

Multiple algorithms exist for the detection of causal relations from observational data but they are limited by their required assumptions regarding the data or by available computational resources. Only limited amount of information can be extracted from finite data but domain experts often have some knowledge of the underlying processes. We propose combining an expert’s prior knowledge with data likelihood to find models with high posterior probability. Our high-level procedure for interactive causal structure discovery contains three modules: discovery of initial models, navigation in the space of causal structures, and validation for model selection and evaluation. We present one manner of formulating the problem and implementing the approach assuming a rational, Bayesian expert which assumption we use to model the user in simulated experiments. The expert navigates greedily in the structure space using their prior information and the structures’ fit to data to find a local maximum a posteriori structure. Existing algorithms provide initial models for the navigation. Through simulated user experiments with synthetic data and use cases with real-world data, we find that the results of causal analysis can be improved by adding prior knowledge. Additionally, different initial models can lead to the expert finding different causal models and model validation helps detect overfitting and concept drift

Situation recognition using soft computing techniques

Includes bibliographical references.The last decades have witnessed the emergence of a large number of devices pervasively launched into our daily lives as systems producing and collecting data from a variety of information sources to provide different services to different users via a variety of applications. These include infrastructure management, business process monitoring, crisis management and many other system-monitoring activities. Being processed in real-time, these information production/collection activities raise an interest for live performance monitoring, analysis and reporting, and call for data-mining methods in the recognition, prediction, reasoning and controlling of the performance of these systems by controlling changes in the system and/or deviations from normal operation. In recent years, soft computing methods and algorithms have been applied to data mining to identify patterns and provide new insight into data. This thesis revisits the issue of situation recognition for systems producing massive datasets by assessing the relevance of using soft computing techniques for finding hidden pattern in these systems

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function yn=(√(1.96+ ye )−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last three hours, so a third order AR model was used to generate hourly 50m wind speed residuals. The MERRA daily average 50m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data

Uncertainty analysis in product service system: Bayesian network modelling for availability contract

There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks

Learning Bayesian network equivalence classes using ant colony optimisation

Bayesian networks have become an indispensable tool in the modelling of uncertain knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the structure, and conditional probability distributions attached to each node known as the parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated, when faced with an uncertain problem domain. However, a recurring problem persists in specifying a Bayesian network. Both the structure and parameters can be difficult for experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure and parameters of Bayesian networks from data. Whilst there are simple methods for learning the parameters, learning the structure has proved harder. Part ofthis stems from the NP-hardness of the problem and the super-exponential space of possible structures. To help solve this task, this thesis seeks to employ a relatively new technique, that has had much success in tackling NP-hard problems. This technique is called ant colony optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants acting together in a colony. It uses the stochastic activity of artificial ants to find good solutions to combinatorial optimisation problems. In the current work, this method is applied to the problem of searching through the space of equivalence classes of Bayesian networks, in order to find a good match against a set of data. The system uses operators that evaluate potential modifications to a current state. Each of the modifications is scored and the results used to inform the search. In order to facilitate these steps, other techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural similarity metrics and Bayesian scoring metrics. The results are compared in depth against systems that also use ant colony optimisation and other methods, including evolutionary programming and greedy heuristics. Also, comparisons are made to well known state-of-the-art algorithms and a study performed on a real-life data set. The results show favourable performance compared to the other methods and on modelling the real-life data

Progress in operational modeling in support of oil spill response

Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies