The Central European limited‐area ensemble forecasting system: ALADIN‐LAEF

The Central European limited-area ensemble forecasting system ALADIN-LAEF (Aire Limitee Adaptation Dynamique Developpement InterNational—Limited-Area Ensemble Forecasting) has been developed within the framework of ALADIN international cooperation and the Regional Cooperation for Limited-Area modelling in Central Europe (RC LACE). It was put into pre-operation in March 2007. The main feature of the pre-operational ALADIN-LAEF was the dynamical downscaling of the global ensemble forecast from the European Centre for Medium-range Weather Forecasts (ECMWF). In 2009, ALADIN-LAEF was upgraded with several methods for dealing with the forecast uncertainties to improve the forecast quality. These are: (1) the blending method, which combines the large-scale uncertainty generated by ECMWF singular vectors with the small-scale perturbations resolved by ALADIN breeding into atmospheric initial condition perturbations; (2) the multi-physics approach, wherein different physics schemes are used for different forecast members to account for model uncertainties; and (3) the non-cycling surface breeding technique, which generates surface initial condition perturbations. This article illustrates the technical details of the updated ALADIN-LAEF and investigates its performance. Detailed verification of the upgraded ALADIN-LAEF and a comparison with its first implementation (dynamical downscaling of ECMWF ensemble forecasts) are presented for a two-month period in summer 2007. The results show better performance and skill for the upgraded system due to the better representation of forecast uncertainties. Copyright © 2011 Royal Meteorological Society

[1]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[2]  Hannu Savijärvi,et al.  Fast radiation parameterization schemes for mesoscale and short-range forecast models , 1990 .

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

[4]  H. Savijärvi,et al.  Introducing the effective radius into a fast radiation scheme of a mesoscale model , 1999 .

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

[6]  A. Montani,et al.  A spatial verification method applied to the evaluation of high‐resolution ensemble forecasts , 2008 .

[7]  Inger-Lise Frogner,et al.  Limited‐area ensemble predictions at the Norwegian Meteorological Institute , 2006 .

[8]  E. Bazile,et al.  Implementation of a New Assimilation Scheme for Soil and Surface Variables in a Global NWP Model , 2000 .

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

[10]  Radmila Brožková,et al.  Atmospheric forcing by ALADIN/MFSTEP and MFSTEP oriented tunings , 2006 .

[11]  Jean-François Geleyn,et al.  Some problems of closure assumption and scale dependency in the parameterization of moist deep convection for numerical weather prediction , 1989 .

[12]  Thomas M. Hamill,et al.  Verification of Eta–RSM Short-Range Ensemble Forecasts , 1997 .

[13]  E. Bazile,et al.  A mass‐flux convection scheme for regional and global models , 2001 .

[14]  David S. Richardson,et al.  Current status and future developments of the ECMWF Ensemble Prediction System , 2000 .

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

[16]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[17]  Jf. Geleyn,et al.  Use of a Modified Richardson Number for Parameterizing the Effect of Shallow Convection , 1986 .

[18]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

[19]  H. Kuo Further Studies of the Parameterization of the Influence of Cumulus Convection on Large-Scale Flow , 1974 .

[20]  J. Geleyn,et al.  Interpolation of wind, temperature and humidity values from model levels to the height of measurement , 1988 .

[21]  E. Kessler On the distribution and continuity of water substance in atmospheric circulations , 1969 .

[22]  J. Geleyn,et al.  A statistical approach for sedimentation inside a microphysical precipitation scheme , 2008 .

[23]  Craig H. Bishop,et al.  Ensemble Transformation and Adaptive Observations , 1999 .

[24]  H. Sundqvist Inclusion of ice phase of hydrometeors in cloud parameterization for mesoscale and largescale models , 1993 .

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

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

[27]  P. Bougeault,et al.  A Simple Parameterization of the Large-Scale Effects of Cumulus Convection , 1985 .

[28]  H. Hersbach Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .

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

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

[31]  C. Bishop,et al.  Regional Ensemble Forecasts Using the Ensemble Transform Technique , 2009 .

[32]  Jun Du,et al.  Short-Range Ensemble Forecasting of Quantitative Precipitation , 1997 .

[33]  I. Troen,et al.  A simple model of the atmospheric boundary layer; sensitivity to surface evaporation , 1986 .

[34]  François Lott,et al.  A new subgrid‐scale orographic drag parametrization: Its formulation and testing , 1997 .

[35]  Chen Jing A New Initial Perturbation Method of Ensemble Mesoscale Heavy Rain Prediction , 2005 .

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

[37]  Roberto Buizza,et al.  Quantitative Precipitation Forecasts over the United States by the ECMWF Ensemble Prediction System , 2001 .

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

[39]  Albert A. M. Holtslag,et al.  An updated length‐scale formulation for turbulent mixing in clear and cloudy boundary layers , 2004 .

[40]  O. Talagrand,et al.  On Some Aspects of the Definition of Initial Conditions for Ensemble Prediction , 2007 .

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

[42]  Guillem Candille,et al.  A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations , 2008 .

[43]  R. Laprise,et al.  The Canadian Climate Centre spectral atmospheric general circulation model , 1984 .

[44]  D. Randall,et al.  A Semiempirical Cloudiness Parameterization for Use in Climate Models , 1996 .

[45]  J. Redelsperger,et al.  A turbulence scheme allowing for mesoscale and large‐eddy simulations , 2000 .

[46]  Probabilistic high-resolution forecast of heavy precipitation over Central Europe , 2004 .

[47]  B. Ritter,et al.  A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations , 1992 .

[48]  Clifford F. Mass,et al.  Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting , 2005 .

[49]  Philippe Lopez,et al.  Implementation and validation of a new prognostic large‐scale cloud and precipitation scheme for climate and data‐assimilation purposes , 2002 .

[50]  L. Gerard,et al.  Evolution of a subgrid deep convection parametrization in a limited‐area model with increasing resolution , 2005 .

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

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

[53]  R. Smith A scheme for predicting layer clouds and their water content in a general circulation model , 1990 .

[54]  P. Bénard,et al.  Flux-conservative thermodynamic equations in a mass-weighted framework , 2007 .

[55]  Peter Lynch,et al.  Initialization of the HIRLAM Model Using a Digital Filter , 1992 .

[56]  M. Ehrendorfer Vorhersage der Unsicherheit numerischer Wetterprognosen: eine Übersicht , 1997 .

[57]  Roberto Buizza,et al.  A strategy for high‐resolution ensemble prediction. II: Limited‐area experiments in four Alpine flood events , 2001 .

[58]  P. Houtekamer,et al.  Status of the Global EPS at Environment Canada , 2008 .

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

[60]  J. Geleyn,et al.  Cloud and Precipitation Parameterization in a Meso-Gamma-Scale Operational Weather Prediction Model , 2009 .

[61]  L. Gérard,et al.  An integrated package for subgrid convection, clouds and precipitation compatible with meso‐gamma scales , 2007 .

[62]  F. Molteni,et al.  A strategy for high‐resolution ensemble prediction. I: Definition of representative members and global‐model experiments , 2001 .

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

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

[65]  P. Bougeault,et al.  Modeling the Trade-Wind Cumulus Boundary Layer. Part I: Testing the Ensemble Cloud Relations Against Numerical Data. , 1981 .

[66]  Jeffrey L. Anderson A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations , 1996 .

[67]  Jean-François Geleyn,et al.  An Approach for Convective Parameterization with Memory: Separating Microphysics and Transport in Grid-Scale Equations , 2007 .