Evaluation of ‘GLAMEPS’—a proposed multimodel EPS for short range forecasting

References

• ALADIN International Team. 1997. The ALADIN project: mesoscale modelling seen as a basic tool for weather forecasting and atmospheric research. WMO bull. 46, 317–324.
   Google Scholar
• Aspelien, T., Iversen, T., Bremnes, J. B. and Frogner, I.-L. 2011. Short-range probabilistic forecasts from the Norwegian Limited Area EPS. Long-term validation and a polar low study. Tellus 63A, this issue. Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system. Q. J. R. MeteoroL Soc. 125, 2333–2351.
   Google Scholar
• Barkmeijer, J., Iversen, T. and Palmer, T. N. 2003. Forcing singular vectors and other sensitive model structures. Q. J. R. MeteoroL Soc. 129, 2401–2423.
   Web of Science ®Google Scholar
• Barnston A. G. and Livezey, R. E. 1987. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev. 115, 1083–1126.
   Web of Science ®Google Scholar
• Bishop, C. H., Etherton, B. J. and Majundar, S. J. 2001. Adaptive sam-pling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev. 129, 420–436.
   Web of Science ®Google Scholar
• Bojarova, J., Gustafsson, N., Johansson, A. and Vignes, O. 2011. The ETKF resealing scheme in HIRLAM. Tellus 63A, this issue.
   Google Scholar
• Bougeault, P. 1985. A simple parameterization of the large-scale effects of cumulus convection. Mon. Wea. Rev. 113, 2108–2121.
   Web of Science ®Google Scholar
• Bougeault, P. and Geleyn, J.-F. 1989. Some problems of closure as-sumption and scale dependency in the parameterization of moist deep convection for numerical weather prediction. Meteorol. Atmos. Phys. 40, 123–135.
   Web of Science ®Google Scholar
• Bowler, N. E. 2006. Comparison of error breeding, singular vectors, ran-dom perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus 58A, 538–548.
   Google Scholar
• Bowler, N. E. 2008. Accounting for the effect of observation errors on verification of MOGREPS. Meteorol. AppL 15, 199–205.
   Web of Science ®Google Scholar
• Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS ensemble prediction system. Q. J. R. Meteorol. Soc. 134, 703–722.
   Web of Science ®Google Scholar
• Bowler, N. E. and Mylne, K. R. 2009. Ensemble transform Kalman filter perturbations for a regional ensemble prediction system. Q. J. R. Meteorol. Soc. 135, 757–766.
   Web of Science ®Google Scholar
• de Bruijn, C. and van Meijgaard, E. 2005. Verification of HIRLAM with ECMWF physics compared with HIRLAM reference versions. HIRLAM Technical Report No. 63. Available at: hirlam.org.
   Google Scholar
• Buizza, R. 1994. Localization of optimal perturbations using a projection operator. Q. J. R. Meteor Soc. 120, 1647–1682.
   Web of Science ®Google Scholar
• Buizza, R., Barkmeijer, J., Palmer, T. N. and Richardson, D. 2000. Current status and future developments of the ECMWF ensemble prediction system. Meteorol. AppL 6, 1–14.
   Google Scholar
• Buizza, R., Leutbecher, M. and Isalcsen L. 2008. Potential use of analyses in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 134,2051–2066, 10.1002/qj.346.
   Web of Science ®Google Scholar
• Buizza, R., Miller, M. and Palmer, T. N. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125, 2887–2908.
   Web of Science ®Google Scholar
• Buizza, R., Tribbia, J., Molteni, E and Palmer, T. N. 1993. Computation of optimal unstable structures for a numerical weather prediction model. Tellus 45A, 388-407.
   Google Scholar
• Calvo, J. 2007. Kain-Fritsch convection in HIRLAM. Present sta-tus and prospects. HIRLAIVI Newslett. 52, 57-64. Available at: http://hirlam.org/index.php?option=com_content&view=arti-cle&id=64&Itemid=101.
   Google Scholar
• Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F. and Rochas, M. 1991. The ARPEGE project at Meteo-France. In: Proceedings of the 1991 ECMWF seminar on numerical methods in atmo-spheric models. Vol. II, 193-231. ECMWF, Shinfield Park, Reading, England.
   Google Scholar
• Doblas-Reyes, F. J., Hagedorn, R. and Palmer, T. N. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting II. Calibration and combination. Tellus 57A, 234–252.
   Google Scholar
• Du, J., DiMego, G., Tracton, M. S. and Thou, B. 2003. NCEP short-range ensemble forecasting (SREF) system: multi-IC, multi-model and multi-physics approach. Research Activities in At-mospheric and Oceanic Modelling (edited by J. Cote), Re-port 33, CAS/JSC Working Group Numerical Experimenta-tion (VVGNE), WMO/T’D-No. 1161, 5.09-5.10. Available at: http://www.emc.ncep.noaa.gov/mmb/SREF/reference.html.
   Google Scholar
• Du, J., Mullen, S. L. and Sanders, F. 1997. Short-range ensemble forecasting of quantitive precipitation. Mon. Wea. Rev. 125, 2427–2459.
   Web of Science ®Google Scholar
• Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statis-tics. J. Geophys. Res. Oceans 99, 10143–10162.
   Web of Science ®Google Scholar
• Evensen, G. and van Leeuwen, P. 1996. Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev. 124, 85–96.
   Google Scholar
• Fisher, M. and Courtier, P. 1995. Estimating the covariance matrices of analysis and forecast error in variational data assimilation. ECMWF Tech. Memo. 220. ECMWF, Shinfield Park, Reading, RG2 9AX, UK. Fischer, M. and Andersson, E. 2001. Developments in 4D-Var and Kalman filtering. ECMWF Tech. Memo. 347. ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
   Google Scholar
• Frogner, I.-L., Haakenstad, H. and Iversen, T. 2006. Limited Area en-semble predictions at the Norwegian Meteorological Institute. Q. J. R. Meteorol. Soc. 132, 2785–2808. 10.1256/qj.04.178
   Web of Science ®Google Scholar
• Frogner, I.-L. and Iversen, T. 2011. EuroTEPS-a targeted version of ECMWF EPS for the European area. Tellus, 63A, this issue.
   Google Scholar
• Garcia-Moya, J. A., Callado, A., Santos, C., Santos, D. and Simarro, J. 2007. Multi-model ensemble for short-range predictability. In: Proceedings of the 3rd International Verification Methods Workshop, ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
   Google Scholar
• Geleyn, J.-E, Bazile, E., Bougeault, R, Deque, M., Ivanovici, V. and co-authors 1995. Atmospheric parametrization schemes in Meteo-France’s ARPEGE N.W.P. model. In: Proceedings of the 1994 ECMWF Seminar on Physical Parametrizations in Numerical Models, ECMWF, 385-400. Shinfield Park, Reading, England.
   Google Scholar
• Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M. and co-authors 2001. Three-dimensional variational data assimilation for a limited area model. Part I: general formulation and the background error constraint. Tellus 53A, 425-446.
   Google Scholar
• Hagedorn, R., Doblas-Reyes, F. J. and Palmer, T. N. 2005. The ra-tionale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concepts. Tellus 57A, 219–233.
   Google Scholar
• Hagel, E. and Horányi, A. 2007. The ARPEGE/ALADIN limited area ensemble prediction system: the impact of global targeted singular vectors. Meteorologische Zeitschrift 16, 653–663.
   Web of Science ®Google Scholar
• Hamill, T. M. 2001. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev. 129, 550–560.
   Google Scholar
• Hamill, T. M. and Colucci, S. J. 1997. Verification of eta-RSM short-range forecasts. Mon. Wea. Rev. 125, 1312–1327.
   Google Scholar
• Hamill, T. M., Mullen, S. L., Snyder, S., Toth, Z. and Braumhefner, D. P. 2000. Ensemble forecasting in the short to medium range. Report from a workshop. Bull. Am. Meteor. Soc. 81, 2653–2664.
   Google Scholar
• Hamill, T. M., Whitaker, J. S. and Mullen, S. L. 2006. Reforecasts, an important dataset for improving weather predictions. Bull. Am. Meteorol. Soc. 87, 33–46.
   Web of Science ®Google Scholar
• Haugen, J. E. and Machenhauer, B. 1993. A spectral Limited Area model formulation with time-dependent boundary conditions applied to the shallow-water equations. Mon. Wea. Rev. 121,2618–2630.
   Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796–811.
   Web of Science ®Google Scholar
• Ivarsson, K. I. 2007. The Rasch Kristjansson large scale condensation. Present status and prospects. HIRLAIVI Newslett. 52, 50-56. Avail-able at: http://hirlam.org/index.php?option=com_content&view=article&id=64&Itemid=101.
   Google Scholar
• Johnson, C. and Swinbank, R. 2009. Medium-range multimodel ensem-ble combination and calibration. Q. J. R. Meteorol. Soc. 135, 777–794.
   Web of Science ®Google Scholar
• Kain, J. S. 2004. The Kain-Fritsch Convective Parameterization. An update. J. AppL Meteorol. 43, 170–181.
   Google Scholar
• Kain, J. S. and Fritsch, J. M. 1990. A one-dimensional entrain-ing/detraining plume model and its application in convective param-eterization. J. Atmos. Sci., 47, 2784–2802.
   Web of Science ®Google Scholar
• Kann, A., Wittmann, C., Wang, Y. and Ma, X. 2009. Cali-brating 2-m temperature of limited-area ensemble forecasts us-ing high-resolution analysis. Mon. Wea. Rev. 137, 3373–3387, 10.1175/2009MWR2793.
   Web of Science ®Google Scholar
• Leith, C. E. 1974. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev. 102, 409–418.
   Web of Science ®Google Scholar
• Leutbecher, M. 2007. On the representation of initial uncertainties with multiple sets of singular vectors optimized for different criteria. Q. J. R. Meteorol. Soc. 133, 2045–2056, 10.1002/qj.174.
   Web of Science ®Google Scholar
• Leutbecher, M. and Palmer, T. N. 2008. Ensemble forecasting. J. Comp. Phys. 227, 3515–3539.
   Web of Science ®Google Scholar
• Lewis, J. M. 2005. Roots of ensemble forecasting. Mon. Wea. Rev. 133, 1865–1885.
   Google Scholar
• Lindskog, M., Gustafsson, N., Navascu’e Ns, B., Mogensen, K. S., Huang, X.-Y. and co-authors. 2001. Three-dimensional variational data assimilation for a limited area model. Part IL observation han-dling and assimilation experiments. Tellus 53A, 447-468.
   Google Scholar
• Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141.
   Web of Science ®Google Scholar
• Lorenz, E. N. 1982. Atmospheric predictability experiments with a large numerical model. Tellus 34, 505–513.
   Google Scholar
• Louis, J.-F., Tiedke, M. and Geleyn, J.-F. 1981. A short history of the op-erational PBL-parameterization at ECMWF. In: Workshop Proceed-ings on Planetary Boundary Layer Parameterizations, Nov. 1981, pp. 59-79.ECMWF, Shinfield Park, Reading, RG2 9AX, UK.
   Google Scholar
• Magnusson, L., Nycander, J. and Källen, E. 2009. Flow-dependent ver-sus flow-independent perturbations for ensemble prediction. Tellus 61A, 194–209, 10.1111/j.1600-0870.2008.00385.x.
   Google Scholar
• Marsigli, C., Boccanera, F., Montani, A. and Paccagnella, T. 2005. The COSMO-LEPS mesoscale ensemble prediction system: validation of the methodology and verification. NonL Proc. Geophys. 12, 527–536.
   Web of Science ®Google Scholar
• Molteni, F., Buizza, R., Palmer, T. N. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc. 125, 73–119.
   Google Scholar
• Noilhan, J. and S. Planton. 1989. A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev. 117, 536–549.
   Web of Science ®Google Scholar
• Orrell, D., Smith, L., Barkmeijer, J. and Palmer T. N. 2001. Model error in weather forecasting. NonL Process. Geophys. 8, 357–371.
   Web of Science ®Google Scholar
• Osborn, T. J. 2006. Recent variations in the winter North Atlantic Os-cillation. Weather 61, 353–355.
   Google Scholar
• Palmer, T. N. 2000. Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys. 63, 71–116.
   Web of Science ®Google Scholar
• Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M. 2005. Using bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev. 133, 1155–1174.
   Google Scholar
• Rasch, P. J. and Kristjánsson, J. E. 1998. A comparison of the CCM3 model climate using diagnosed and predicted condensate parameter-izations. J. Clim. 11, 1587–1614.
   Web of Science ®Google Scholar
• Saetra, O., Hersbach, H., Bidlot, J. R. and Richardson, D. S. 2004. Effects of observation errors on the statistics for ensemble spread and reliability. Mon. Wea. Rev. 132, 1487-1501.
   Google Scholar
• Sass, B. H., Nielsen, N. W., Jorgensen, J. U. and Amstrup, B. 1999. The Operational HIRLAM System at DMI. DMI Tech Rep. no 99-21. Danmarks Meteorologiske Institutt, Lyngby v. 100, Copenhagen, Denmark.
   Google Scholar
• Seko, H., Saito, K., Kunii, M., Hara, T., Kyouda, M. and Yamaguchi, M. 2007. Japan area mesoscale ensemble experiments using .IMANHM. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling. 37, 5.31-5.32.
   Google Scholar
• Simmons, A. and Hollingsworth A. 2002. Some aspects of the improve-ments in skill of numerical weather prediction. Q. J. R. Meteorol. Soc. 128, 647–677.
   Web of Science ®Google Scholar
• Stappers, R.J.J. and Barkmeijer, J. 2011. Properties of singular vectors using convective available potential energy as final time norm. Tellus 63A, this issue.
   Google Scholar
• Stensrud, D. J., Brooks, H. E., Du, J., Tracton, M. S., Rogers, E. 1999. Using ensembles for short-range forecasting. Mon. Wea. Rev. 127, 433–446.
   Google Scholar
• Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 74, 2317–2330.
   Web of Science ®Google Scholar
• Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297–3319.
   Web of Science ®Google Scholar
• Unden, R, Rontu, L., Järvinen, H., Lynch, P., Calvo, J. and co-authors. 2002: HIRLAM-5 Scientific Documentation HIRLAM-5 Project. Available from SMHI, S-601767 Norrkoping, Sweden.
   Google Scholar
• Wang, X. G. and Bishop, G. H. 2003. A comparison of breeding and ensemble transform Kalman filter forecast schemes. J. Atmos. Sci. 60, 1140–1158.
   Web of Science ®Google Scholar
• Weigel, A. P. and Bowler, N. E. 2009. Comment on ‘Can multi-model combination really enhance the prediction skill of proba-bilistic ensemble forecasts?’. Q. J. R. Meteorol. Soc. 135, 535–539, 10.1002/qj.381
   Web of Science ®Google Scholar
• Weigel, A. P., Liniger, M. A. and Appenzeller, C. 2008. Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?. Q. J. R. Meteorol. Soc. 134, 241–260.
   Web of Science ®Google Scholar
• Yang, X. 2007. Status update on operational HIRLAM. HIRLAM Newslett. 52, 114-122. (Can be downloaded from http://Hirlam.org).
   Google Scholar
• Yang, X. 2008. Status of the Reference System. HIRLAM Newslett 55, 202-203. (Can be downloaded from http://Hirlam.org).
   Google Scholar