A comparison of space weather analysis techniques used to predict the arrival of the Earth-directed CME and its shockwave launched on 8 April 2010

The Earth-directed coronal mass ejection (CME) of 8 April 2010 provided an opportunity for space weather predictions from both established and developmental techniques to be made from near–real time data received from the SOHO and STEREO spacecraft; the STEREO spacecraft provide a unique view of Earth-directed events from outside the Sun-Earth line. Although the near–real time data transmitted by the STEREO Space Weather Beacon are significantly poorer in quality than the subsequently downlinked science data, the use of these data has the advantage that near–real time analysis is possible, allowing actual forecasts to be made. The fact that such forecasts cannot be biased by any prior knowledge of the actual arrival time at Earth provides an opportunity for an unbiased comparison between several established and developmental forecasting techniques. We conclude that for forecasts based on the STEREO coronagraph data, it is important to take account of the subsequent acceleration/deceleration of each CME through interaction with the solar wind, while predictions based on measurements of CMEs made by the STEREO Heliospheric Imagers would benefit from higher temporal and spatial resolution. Space weather forecasting tools must work with near–real time data; such data, when provided by science missions, is usually highly compressed and/or reduced in temporal/spatial resolution and may also have significant gaps in coverage, making such forecasts more challenging.

A study of the arrival over the United Kingdom in April 2010 of the Eyjafjallajökull ash cloud using ground-based lidar and numerical simulations

We make a qualitative and quantitative comparison of numericalsimulations of the ashcloud generated by the eruption of Eyjafjallajökull in April2010 with ground-basedlidar measurements at Exeter and Cardington in southern England. The numericalsimulations are performed using the Met Office’s dispersion model, NAME (Numerical Atmospheric-dispersion Modelling Environment). The results show that NAME captures many of the features of the observed ashcloud. The comparison enables us to estimate the fraction of material which survives the near-source fallout processes and enters into the distal plume. A number of simulations are performed which show that both the structure of the ashcloudover southern England and the concentration of ash within it are particularly sensitive to the height of the eruption column (and the consequent estimated mass emission rate), to the shape of the vertical source profile and the level of prescribed ‘turbulent diffusion’ (representing the mixing by the unresolved eddies) in the free troposphere with less sensitivity to the timing of the start of the eruption and the sedimentation of particulates in the distal plume.

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/