Projects per year
The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modelled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel Wiener prediction filter. Building on a C-linear cyclo-stationary predictor, a new widely linear filter is developed and tested on hourly mean wind speed and direction measurements made at 13 locations in the UK over 6 years. The new predictor shows a reduction in mean squared error at all locations. Furthermore it is found that the scale of that reduction strongly depends on conditions local to the measurement site.
|Number of pages||4|
|Publication status||Published - Jul 2014|
|Event||2014 IEEE Workshop on Statistical Signal Processing (SSP) - Australia, Gold Coast, United Kingdom|
Duration: 29 Jun 2014 → 2 Jul 2014
|Conference||2014 IEEE Workshop on Statistical Signal Processing (SSP)|
|Period||29/06/14 → 2/07/14|
- Wiener filters
- widely linear processing
- weather forecasting
- atmospheric techniques
Soraghan, J. & Weiss, S.
1/04/13 → 31/03/18
1/10/09 → 31/03/18
Project: Research - Studentship