A flexible approach to defining weather patterns and their application in weather forecasting over Europe

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.

[1]  K. Briffa,et al.  Lamb weather types derived from reanalysis products , 2013 .

[2]  P. Earnshaw,et al.  The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations , 2011, Geoscientific Model Development.

[3]  Manola Brunet,et al.  Daily Mean Sea Level Pressure Reconstructions for the European–North Atlantic Region for the Period 1850–2003 , 2006 .

[4]  Laura Ferranti,et al.  Flow‐dependent verification of the ECMWF ensemble over the Euro‐Atlantic sector , 2015 .

[5]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[6]  Mats Hamrud,et al.  The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System) , 2007 .

[7]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[8]  B. Efron,et al.  Bootstrap confidence intervals , 1996 .

[9]  Michael Ghil,et al.  Multiple Flow Regimes in the Northern Hemisphere Winter. Part II: Sectorial Regimes and Preferred Transitions , 1993 .

[10]  Mike Hulme,et al.  A COMPARISON OF LAMB CIRCULATION TYPES WITH AN OBJECTIVE CLASSIFICATION SCHEME , 1993 .

[11]  Tim N. Palmer,et al.  Signature of recent climate change in frequencies of natural atmospheric circulation regimes , 1999, Nature.

[12]  J. W. Kidson.  An analysis of New Zealand synoptic types and their use in defining weather regimes , 2000 .

[13]  A. H. Murphy.  A New Vector Partition of the Probability Score , 1973 .

[14]  P. James,et al.  An objective classification method for Hess and Brezowsky Grosswetterlagen over Europe , 2007 .

[15]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[16]  Andreas Philipp,et al.  Cluster Analysis of North Atlantic-European Circulation Types and Links with Tropical Pacific Sea Surface Temperatures , 2008 .

[17]  P. Jones,et al.  Long-Term Variability of Daily North Atlantic–European Pressure Patterns since 1850 Classified by Simulated Annealing Clustering , 2007 .