The THORPEX Interactive Grand Global Ensemble

Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted a considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l Ensemble (TIGGE) project, a prominent contribution to THORPEX, has been initiated to enable advanced research and demonstration of the multimodel ensemble concept and to pave the way toward operational implementation of such a system at the international level. The objectives of TIGGE are 1) to facilitate closer cooperation between the academic and operational meteorological communities by expanding the availability of operational products for research, and 2) to facilitate exploring the concept and benefits of multimodel probabilistic weather forecasts, with a particular focus on high-impact weather prediction. Ten operational weather forecasting centers producing daily global ensemble ...

[1]  Mio Matsueda,et al.  Can MCGE Outperform the ECMWF Ensemble , 2008 .

[2]  V. Masson,et al.  The AROME-France Convective-Scale Operational Model , 2011 .

[3]  C. Snyder,et al.  Linear and non-linear response to parameter variations in a mesoscale model , 2009 .

[4]  Volker Wulfmeyer,et al.  Streamflow data assimilation for soil moisture analysis , 2009 .

[5]  Yong Wang,et al.  A strategy for perturbing surface initial conditions in LAMEPS , 2010 .

[6]  Sharanya J. Majumdar,et al.  Targeted Observations for Improving Numerical Weather Prediction : An Overview , 2011 .

[7]  Nigel Roberts,et al.  Impact of Data Assimilation on Forecasting Convection over the United Kingdom Using a High-Resolution Version of the Met Office Unified Model , 2009 .

[8]  R. Plant,et al.  A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics , 2008 .

[9]  Neill E. Bowler,et al.  Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model , 2006 .

[10]  Tiziana Paccagnella,et al.  Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges , 2011 .

[11]  Tiziana Paccagnella,et al.  Impact of soil surface moisture initialization on rainfall in a limited area model: a case study of the 1995 South Ticino flash flood , 2002 .

[12]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[13]  Malaquias Peña,et al.  Consolidation of Multimodel Forecasts by Ridge Regression: Application to Pacific Sea Surface Temperature , 2008 .

[14]  Jean-François Mahfouf,et al.  Analysis of Soil Moisture from Near-Surface Parameters: A Feasibility Study , 1991 .

[15]  Fuqing Zhang,et al.  Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments , 2006 .

[16]  P. L. Houtekamer,et al.  Toward Random Sampling of Model Error in the Canadian Ensemble Prediction System , 2010 .

[17]  Reinhold Hess,et al.  Evaluation of the variational soil moisture assimilation scheme at Deutscher Wetterdienst , 2008 .

[18]  A. H. Murphy,et al.  Probabilities, Odds, and Forecasts of Rare Events , 1991 .

[19]  Florian Pappenberger,et al.  Monthly‐, medium‐, and short‐range flood warning: testing the limits of predictability , 2009 .

[20]  S. J. Weiss,et al.  Probabilistic Precipitation Forecast Skill as a Function of Ensemble Size and Spatial Scale in a Convection-Allowing Ensemble , 2011 .

[21]  Stefano Migliorini,et al.  Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics , 2011 .

[22]  Peter Krahe,et al.  Experimental ensemble forecasts of precipitation based on a convection‐resolving model , 2008 .

[23]  U. Germann,et al.  MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine region , 2009 .

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

[25]  Neill E. Bowler,et al.  The local ETKF and SKEB: Upgrades to the MOGREPS short‐range ensemble prediction system , 2009 .

[26]  Munehiko Yamaguchi,et al.  Comparison of initial perturbation methods for the mesoscale ensemble prediction system of the Meteorological Research Institute for the WWRP Beijing 2008 Olympics Research and Development Project (B08RDP) , 2011 .

[27]  Xuguang Wang,et al.  A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes , 2003 .

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

[29]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[30]  Z. Janjic.  A nonhydrostatic model based on a new approach , 2002 .

[31]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[32]  Neill E. Bowler,et al.  Ensemble transform Kalman filter perturbations for a regional ensemble prediction system , 2009 .

[33]  Thomas M. Hamill,et al.  Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application , 2006 .

[34]  R. Treadon,et al.  A Tutorial on Lateral Boundary Conditions as a Basic and Potentially Serious Limitation to Regional Numerical Weather Prediction , 1997 .

[35]  Thomas M. Hamill,et al.  Will Perturbing Soil Moisture Improve Warm-Season Ensemble Forecasts? A Proof of Concept , 2006 .

[36]  T. Iversen,et al.  Evaluation of ‘GLAMEPS’—a proposed multimodel EPS for short range forecasting , 2011 .

[37]  S. Julier,et al.  Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble? , 2004 .

[38]  Kazuo Saito,et al.  A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area , 2011 .

[39]  Roberto Buizza,et al.  TIGGE: Preliminary results on comparing and combining ensembles , 2008 .

[40]  Stefano Migliorini,et al.  Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates , 2011 .

[41]  Ryan D. Torn,et al.  Boundary Conditions for Limited-Area Ensemble Kalman Filters , 2006 .

[42]  David J. Stensrud,et al.  Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems , 2000 .

[43]  Z. B. Bouallègue,et al.  Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries , 2011 .

[44]  AndrÁS HorÁNyi,et al.  Latest developments around the ALADIN operational short-range ensemble prediction system in Hungary , 2011 .

[45]  Luca Delle Monache,et al.  A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone , 2008 .

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

[47]  E. Richard,et al.  Impact of initial condition uncertainties on the predictability of heavy rainfall in the Mediterranean: a case study , 2008 .

[48]  Barbara G. Brown,et al.  Forecast verification: current status and future directions , 2008 .

[49]  Véronique Ducrocq,et al.  Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions , 2011 .

[50]  Chris Snyder,et al.  Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations , 2011 .

[51]  J. M. Sloughter,et al.  Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging , 2007 .

[52]  Martin Leutbecher,et al.  A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System , 2009 .

[53]  Richard Swinbank,et al.  Medium‐range multimodel ensemble combination and calibration , 2009 .

[54]  R. Koster,et al.  Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Scanning Multichannel Microwave Radiometer (SMMR) , 2007 .

[55]  C. Santos,et al.  Predictability of short-range forecasting: a multimodel approach , 2011 .

[56]  Zoltan Toth,et al.  The North American Ensemble Forecast System , 2005 .

[57]  Florian Pappenberger,et al.  New dimensions in early flood warning across the globe using grand‐ensemble weather predictions , 2008 .

[58]  Chris Snyder,et al.  The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results , 2011 .

[59]  Fuqing Zhang,et al.  Limited-Area Ensemble-Based Data Assimilation , 2011 .

[60]  Norbert Kalthoff,et al.  Soil moisture variability and its influence on convective precipitation over complex terrain , 2011 .

[61]  R. Scott,et al.  Measuring soil moisture content non‐invasively at intermediate spatial scale using cosmic‐ray neutrons , 2008 .

[62]  E. Small,et al.  Use of GPS receivers as a soil moisture network for water cycle studies , 2008 .

[63]  R. Stull,et al.  Probabilistic aspects of meteorological and ozone regional ensemble forecasts , 2006 .

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

[65]  Lindsay J. Bennett,et al.  The Convective and Orographically‐induced Precipitation Study (COPS): the scientific strategy, the field phase, and research highlights , 2011 .

[66]  E. Lorenz,et al.  The predictability of a flow which possesses many scales of motion , 1969 .

[67]  David J. Stensrud,et al.  Application of Lateral Boundary Condition Perturbations to Help Restore Dispersion in Limited-Area Ensemble Forecasts , 2004 .

[68]  Binbin Zhou,et al.  Fog Prediction from a Multimodel Mesoscale Ensemble Prediction System , 2010 .

[69]  B. Brown,et al.  The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program , 2009 .

[70]  Carolyn A. Reynolds,et al.  Impact of Stochastic Convection on the Ensemble Transform , 2008 .

[71]  M. Xue,et al.  The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications , 2001 .

[72]  Inger-Lise Frogner,et al.  EuroTEPS— a targeted version of ECMWF EPS for the European area , 2011 .

[73]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[74]  Matthew D. Wilson,et al.  Tracking the uncertainty in flood alerts driven by grand ensemble weather predictions , 2009 .

[75]  Lizzie S. R. Froude.  TIGGE: Comparison of the Prediction of Northern Hemisphere Extratropical Cyclones by Different Ensemble Prediction Systems , 2011 .

[76]  J. Wigneron,et al.  Retrieving near-surface soil moisture from microwave radiometric observations : current status and future plans , 2003 .

[77]  Renate Hagedorn,et al.  Comparing TIGGE multi-model and ECMWF calibrated ensembles , 2010 .

[78]  Kevin Horsburgh,et al.  Development and evaluation of an ensemble forecasting system for coastal storm surges , 2010 .

[79]  Tiziana Paccagnella,et al.  Comparison of calibration techniques for a limited-area ensemble precipitation forecast using reforecasts , 2011 .

[80]  Massimo Bonavita,et al.  Ensemble data assimilation with the CNMCA regional forecasting system , 2010 .