The UK has highly ambitious targets for wind development, particularly offshore, where over 30GW of capacity is proposed for development. Integrating such a large amount of variable generation presents enormous challenges. Answering key questions depends on a detailed understanding of the wind resource and its temporal and spatial variability. However, sources of wind speed data, particularly offshore, are relatively sparse: satellite data has low temporal resolution; weather buoys and met stations have low spatial resolution; while the observations from ships and platforms are affected by the structures themselves. This work uses a state-of-the art mesoscale atmospheric model to produce a new high-resolution wind speed dataset over the British Isles and surrounding waters. This covers the whole region at a resolution of 3km for a period of eleven consecutive years, from 2000 to 2010 inclusive, and is thought to be the first high resolution re-analysis to represent a true historic time series, rather than a statistically averaged climatology. The results are validated against observations from met stations, weather buoys, offshore platforms and satellite-derived wind speeds, and model bias is reduced offshore using satellite derived wind speeds. The ability of the dataset to predict power outputs from current wind farms is demonstrated, and the expected patterns of power outputs from future onshore and offshore wind farms are predicted. Patterns of wind production are compared to patterns of electricity demand to provide the first conclusive combined assessment of the ability of future onshore and offshore wind generation meet electricity demand and contribute to secure energy supplies.

Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast simulations. Currently, Numerical Weather Prediction (NWP) is the premier method for weather prediction, which is used by the National Oceanic and Atmospheric Administration (NOAA). It takes the current observations from observed sites as the input for numeric computer models and then produces the final prediction. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying the parameter configuration that leads to most accurate prediction. In this thesis, we develop an Autonomic Workflow Performance Manager (AWPM) for Hurricane Integrated Modeling System (HIMS). AWPM is implemented on top of the Apache Storm and ZooKeeper to handle multiple real-time data streams for weather forecast. AWPM can automatically manage model initialization and execution workflow and achieve better performance and efficiency. In our experiments, AWPM achieves better performance and efficiency for the model initialization and execution processes, by utilizing automatic computing, distributed computing and component-based development. We reduced the timescale of the configuration search workflow by a factor of 10 by using 20 threads with the full search method, and a factor of 20 by with the roofline method when compared to serial workflow execution as it is typically performed by domain scientists.