Tubing: An Alternative to Clustering for the Classification of Ensemble Forecasts

Abstract Tubing is a method of classification of meteorological forecasts. The method has been designed to facilitate a human interpretation of the distribution of forecasts produced by an ensemble prediction system (EPS). This interpretation aims to complement probabilistic forecasts generated from EPS weather parameter probability distributions. Ensemble forecasts are generally classified according to their similarities. On the other hand, ensemble distributions rarely prove multimodal in practice. The tubing method disregards possible modes and gives instead more emphasis to the central part of the distribution where the ensemble mean is located. Ensemble forecasts are then classified according to the way they differ from the ensemble mean. The tubing algorithm first groups the members lying around the ensemble mean into the central cluster. Remaining ensemble members are classified into a number of tubes. A tube is a cylinder ranging from the central cluster to an extreme member of the distribution. T...

[1]  Mats Hamrud,et al.  Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System , 1998 .

[2]  David B. Stephenson,et al.  Correlation of spatial climate/weather maps and the advantages of using the Mahalanobis metric in predictions , 1997 .

[3]  T. Hamill,et al.  Evaluation of Eta-RSM Ensemble Probabilistic Precipitation Forecasts , 1998 .

[4]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[5]  L. Kalkstein,et al.  An Evaluation of Three Clustering Procedures for Use in Synoptic Climatological Classification , 1987 .

[6]  Eugenia Kalnay,et al.  Operational Ensemble Prediction at the National Meteorological Center: Practical Aspects , 1993 .

[7]  Roberto Buizza,et al.  Impact of Ensemble Size on Ensemble Prediction , 1998 .

[8]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[9]  Jeffrey L. Anderson,et al.  The Impact of Dynamical Constraints on the Selection of Initial Conditions for Ensemble Predictions: Low-Order Perfect Model Results , 1997 .

[10]  T. Palmer.  Extended-range atmospheric prediction and the Lorenz model , 1993 .

[11]  Tim N. Palmer,et al.  Extended‐range predictions with ECMWF models: Time‐lagged ensemble forecasting , 1990 .

[12]  P. L. Houtekamer,et al.  A System Simulation Approach to Ensemble Prediction , 1996 .

[13]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

[14]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[15]  A. Simmons,et al.  The ECMWF medium-range prediction models development of the numerical formulations and the impact of increased resolution , 1989 .

[16]  Zoltan Toth,et al.  Circulation Patterns in Phase Space: A Multinormal Distribution? , 1991 .

[17]  Robert Vautard,et al.  On the Source of Midlatitude Low-Frequency Variability. Part II: Nonlinear Equilibration of Weather Regimes , 1988 .

[18]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .