Article

Drew, D. R., Cannon, D. J., Barlow, J. F., Coker, P. J. and Frame, T. ORCID: https://orcid.org/0000-0001-6542-2173 (2017) The importance of forecasting regional wind power ramping: a case study for the UK. Renewable Energy, 114. pp. 1201-1208. ISSN 0960-1481 doi: 10.1016/j.renene.2017.07.069

Cannon, D., Brayshaw, D. ORCID: https://orcid.org/0000-0002-3927-4362, Methven, J. ORCID: https://orcid.org/0000-0002-7636-6872 and Drew, D. (2017) Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation. Meteorologische Zeitschrift, 26 (3). pp. 239-252. ISSN 0941-2948 doi: 10.1127/metz/2016/0751

Cannon, D.J., Brayshaw, D.J. ORCID: https://orcid.org/0000-0002-3927-4362, Methven, J. ORCID: https://orcid.org/0000-0002-7636-6872, Coker, P.J. and Lenaghan, D. (2015) Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75. pp. 767-778. ISSN 0960-1481 doi: 10.1016/j.renene.2014.10.024

Drew, D., Cannon, D., Brayshaw, D. ORCID: https://orcid.org/0000-0002-3927-4362, Barlow, J. and Coker, P. (2015) The impact of future offshore wind farms on wind power generation in Great Britain. Resources, 4 (1). pp. 155-171. ISSN 2079-9276 doi: 10.3390/resources4010155

Cannon, D., Kirshbaum, D. J. and Gray, S. L. ORCID: https://orcid.org/0000-0001-8658-362X (2013) A mixed-phase bulk orographic precipitation model with embedded convection. Quarterly Journal of the Royal Meteorological Society, 140 (683). pp. 1997-2012. ISSN 1477-870X doi: 10.1002/qj.2269

Cannon, D. J., Kirshbaum, D. J. and Gray, S. L. ORCID: https://orcid.org/0000-0001-8658-362X (2012) Under what conditions does embedded convection enhance orographic precipitation? Quarterly Journal of the Royal Meteorological Society, 138 (663). pp. 391-406. ISSN 0035-9009 doi: 10.1002/qj.926

Other

Cannon, D. and Brayshaw, D. ORCID: https://orcid.org/0000-0002-3927-4362 (2014) 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. University of Reading.