A flexible approach to defining weather patterns and their application in weather forecasting over Europe. (15th April 2016)

• Record Type: Journal Article
• Authors: Neal, Robert; Fereday, David; Crocker, Ric; Comer, Ruth E.
• Is Part Of: Meteorological applications. Volume 23:Number 3(2016)
• Page Start: 389
• Page End: 400
• Publication Date: 2016-04-15
• Subjects: Clustering -- Ensemble forecasting -- Probabilistic verification -- Weather patterns; Meteorology -- Periodicals; Meteorological services -- Periodicals; 551.5
• DOI: 10.1002/met.1563
• Languages: English
• ISSNs: 1350-4827

Abstract

ABSTRACT: A method is presented for deriving weather patterns objectively over an area of interest, in this case the UK and surrounding European area. A set of 30 and eight patterns are derived through k‐means clustering of daily mean sea level pressure (MSLP) data (1850–2003). These patterns have been designed for the purpose of post‐processing forecast output from ensemble prediction systems and understanding how forecast models perform under different circulation types. The 30 weather patterns are designed for use in the medium‐range and the eight weather patterns are designed for use in the monthly and seasonal timescales, or when there is low forecast confidence in the medium‐range. Weather patterns are numbered according to their annual historic occurrences, with lower numbered patterns occurring most often. Lower numbered patterns occur more in summer (with weak MSLP anomalies) and higher numbered patterns occur more in winter (with strong MSLP anomalies). Weather patterns have been applied in a weather forecasting context, whereby ensemble members are assigned to the closest matching pattern definition. This provides a probabilistic insight into which patterns are most likely within the forecast range and summarises key aspects from the large volumes of data which ensembles provide. Verification of European Centre for Medium‐Range Weather Forecasts medium‐range ensemble forecasts for the set of eight weather patterns shows small forecast biases annually with some large variations seasonally. The most prominent seasonal variation shows the westerly (NAO+) pattern to over‐forecast in summer and under‐forecast in winter. Forecast skill was found to be better in winter than summer for most patterns.