TIGGE: Preliminary results on comparing and combining ensembles

TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts. This report discusses some preliminary results from predictability studies based on the ensemble data exchanged within TIGGE, and available at the TIGGE archive centres. In the first part of this work, the key characteristics of the eight ensemble systems available in the TIGGE database at the time of writing (December 2007) are compared, and the strengths and weaknesses of each system are highlighted. Then, issues related to the generation of multi-model/multi-analysis ensemble products are discussed, and some preliminary results on the potential value of combining different ensembles to generate medium-range products with a grand multi-model/multi-analysis global ensemble are presented. One of the key results documented in this work is the large difference between the performance of the single ensembles: for Z500 over the Northern Hemisphere, in the medium-range (say around forecast day 5), the difference between the worst and the best control or ensemble-mean forecasts is about 2 days of predictability, while the difference between the worst and the best probabilistic predictions can be larger, about 3 days of predictability. Another key result has been the quantification of the difference between the skill of the best ensemble system and a combined ensemble generated considering up to four different ensemble systems. Results have indicated that the difference is very small in areas where the European Centre (EC) ensemble system has a well tuned ensemble spread, equivalent to less than 6 hours of predictability in the medium range, while it is larger and more detectable in areas where the EC system has a too low ensemble spread (e.g. in the Tropics). Copyright © 2008 Royal Meteorological Society

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

[2]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[3]  Yuejian Zhu,et al.  The Use of Ensembles to Identify Forecasts with Small and Large Uncertainty , 2001 .

[4]  Martin Leutbecher,et al.  Scale‐dependent verification of ensemble forecasts , 2008 .

[5]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

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

[7]  Anton H. Westveld,et al.  Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .

[8]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[9]  G. Shutts.  A kinetic energy backscatter algorithm for use in ensemble prediction systems , 2005 .

[10]  R. Buizza,et al.  Performance of the ECMWF and the BoM Ensemble Prediction Systems in the Southern Hemisphere , 2004 .

[11]  Roberto Buizza,et al.  The ECMWF Ensemble Prediction System , 1997 .

[12]  W Bourke,et al.  Performance of the ECMWF and the BoM Ensemble Systems in the Southern Hemisphere , 2004 .

[13]  Roberto Buizza,et al.  The Singular-Vector Structure of the Atmospheric Global Circulation , 1995 .

[14]  Thomas M. Hamill,et al.  Ensemble Calibration of 500-hPa Geopotential Height and 850-hPa and 2-m Temperatures Using Reforecasts , 2007 .

[15]  R. Buizza,et al.  A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems , 2005 .

[16]  Roberto Buizza,et al.  Tropical singular vectors computed with linearized diabatic physics , 2001 .

[17]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[18]  P. Jones,et al.  Hemispheric and Large-Scale Surface Air Temperature Variations: An Extensive Revision and an Update to 2001. , 2003 .

[19]  Neill E. Bowler,et al.  The MOGREPS short‐range ensemble prediction system , 2008 .

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

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

[22]  P. L. Houtekamer,et al.  Using ensemble forecasts for model validation , 1997 .

[23]  T. N. Krishnamurti,et al.  A Perturbation Method for Hurricane Ensemble Predictions , 1999 .

[24]  Nils Wedi,et al.  Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP//NCAR analyses of surface air temperature , 2004 .

[25]  Yuejian Zhu,et al.  Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system , 2008 .

[26]  C. Bishop,et al.  Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP , 2006 .

[27]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

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

[29]  Tim N. Palmer,et al.  Ensemble forecasting , 2008, J. Comput. Phys..

[30]  David S. Richardson,et al.  Effects of observation errors on the statistics for ensemble spread and reliability , 2004 .