A widely linear multichannel Wiener filter for wind prediction

Jethro Dowell, Stephan Weiss, David Infield, Swati Chandna

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)
43 Downloads (Pure)


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.
Original languageEnglish
Number of pages4
Publication statusPublished - Jul 2014
Event2014 IEEE Workshop on Statistical Signal Processing (SSP) - Australia, Gold Coast, United Kingdom
Duration: 29 Jun 20142 Jul 2014


Conference2014 IEEE Workshop on Statistical Signal Processing (SSP)
Country/TerritoryUnited Kingdom
CityGold Coast


  • Wiener filters
  • widely linear processing
  • weather forecasting
  • atmospheric techniques
  • wind


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