Skip to main content
SpringerLink
Log in

Ensemble Methods in Meteorological Modelling

  • Conference paper
  • First Online:
Mathematical Problems in Meteorological Modelling

Part of the book series: Mathematics in Industry ((TECMI,volume 24))

  • 990 Accesses

Abstract

Numerical modelling is a continuously developing discipline in meteorology, which provides meteorological forecasts and climate change projections based on the numerical solutions of the set of equations describing the processes in the atmosphere and the related spheres. The progress in numerical weather prediction (NWP) and climate modelling has been enormous in the last few decades thanks to the improved theoretical understanding of the meteorological processes, the growing number of observations and the increasing available computer power. In spite of the steady progress, meteorological forecasts cannot be fully perfect due to the intrinsic characteristics of the atmosphere and the climate system. Weather forecast uncertainties exist in initial conditions and in the model formulations themselves and evolve rapidly with lead time. In climate change projections the initial conditions have negligible role, but the internal climate variability and the unknown future evolution of the anthropogenic activity are additional sources of uncertainties. Since they cannot be avoided (just minimized), their representation and quantification are essential tasks both in numerical weather prediction and climate research. Currently the only feasible way to challenge this problem is the ensemble approach, which delivers probabilistic information and attributes uncertainty information to the numerical weather forecasts and climate projections. This additional uncertainty estimation is a valuable bonus for the users and can be efficiently applied in decision-making.

MTA-ELTE Numerical Analysis and Large Networks Research Group Budapest, Hungary

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    World Climate Research Programme

  2. 2.

    First phase of Coupled Model Intercomparison Project

  3. 3.

    Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects

  4. 4.

    5th Framework Programme of European Union

References

  1. Bishop, C.H., Etherton, B.J., Majumdar, S.: Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Weather Rev. 129, 420–436 (2001)

    Article  Google Scholar 

  2. Boberg, F., Berg, P., Thejll, P., Gutowski, W.J., Christensen, J.H.: Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Clim. Dyn. 35, 1509–1520 (2010)

    Article  Google Scholar 

  3. Bouttier, F., Vié, B., Nuissier, O., Raynaud, L.: Impact of stochastic physics in a convection-permitting ensemble. Mon. Weather Rev. 140, 3706–3721 (2012)

    Article  Google Scholar 

  4. Bray, D., von Storch, H.: ‘Prediction’ or ‘projection’? The nomenclature of climate science. Sci. Commun. 30, 534–543 (2009). doi:10.1177/1075547009333698

    Article  Google Scholar 

  5. Buizza, R., Miller, M., Palmer, T.N.: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc. 125, 2887–2908 (1999)

    Article  Google Scholar 

  6. Buizza, R., Palmer, T.N.: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci. 52, 1434–1456 (1995)

    Article  Google Scholar 

  7. Christensen, J.H., Carter, T.R., Rummukainen, M., Amanatidis, G.: Evaluating the performance and utility of climate models: the PRUDENCE project. Clim. Chang 81 (PRUDENCE Special Issue), 1–6 (2007)

    Google Scholar 

  8. Christensen, H.M., Moroz, I.M., Palmer, T.N.: Stochastic and perturbed parameter representations of model uncertainty in convection parameterization. J. Atmos. Sci. 72, 2525–2544 (2015)

    Article  Google Scholar 

  9. Covey, C., Achuta Rao, K.M., Cubasch, U., Jones, P., Lambert, S.J., Mann, M.E., Phillips, T.J., Taylor, K.E.: An overview of results from the Coupled Model Intercomparison Project (CMIP). Global Planet. Change 37, 103–133 (2003)

    Article  Google Scholar 

  10. García-Moya, J.-A., Callado, A., Escribá, P., Santos, C., Santos-Munoz, D., Simarro, J.: Predictability of short-range forecasting: a multimodel approach. Tellus A 63, 550–563 (2011)

    Article  Google Scholar 

  11. Descamps, L., Labadie, C., Joly, A., Bazile, E., Arbogast, P., Cébron, P.: PEARP, the Météo-France short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 140, 846–854 (2014). doi:10.1002/qj.2469

    Article  Google Scholar 

  12. Gebhardt, C., Theis, S., Krahe, P., Renner, V.: Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett. 9, 67–72 (2008)

    Article  Google Scholar 

  13. Giorgi, F., Bates, G.: The climatological skill of a regional model over complex terrain. Mon. Weather Rev. 117, 2325–2347 (1989)

    Article  Google Scholar 

  14. Hagedorn, R., Buizza, R., Hamill, T.M., Leutbecher, M., Palmer, T.N.: Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts. Q. J. R. Meteorol. Soc. 138, 1814–1827 (2012). doi:10.1002/qj.1895

    Article  Google Scholar 

  15. Hágel, E., Horányi, A.: The ARPEGE/ALADIN limited area ensemble prediction system: the impact of global targeted singular vectors. Meteorol. Z. 16(6), 653–663 (2007)

    Article  Google Scholar 

  16. Hawkins, E., Sutton, R.: The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009)

    Article  Google Scholar 

  17. Hawkins, E., Sutton, R.: The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 37, 407–418 (2011)

    Article  Google Scholar 

  18. Horányi, A., Kertész, S., Kullmann, L., Radnóti, G.: The ARPEGE/ALADIN mesoscale numerical modeling system and its application at the Hungarian Meteorological Service. Időjárás 110, 203–227 (2006)

    Google Scholar 

  19. Horányi, A., Mile, M., Szűcs, M.: Latest developments around the ALADIN operational short-range ensemble prediction system in Hungary. Tellus 63A, 642–651 (2011)

    Article  Google Scholar 

  20. Houtekamer, P.L., Mitchell, H.L.: Ensemble Kalman filtering. Q. J. R. Meteorol. Soc. 131, 3269–3289 (2005)

    Article  Google Scholar 

  21. Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A. (eds.): IPCC TAR WGI: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New~York, NY, USA, 881 p. (2001)

    Google Scholar 

  22. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (eds.): IPCC AR4 WGI: Climate Change 2007: The Scientific Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 946 p. (2007)

    Google Scholar 

  23. Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (eds.): IPCC AR5 WGI: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 p. (2013)

    Google Scholar 

  24. Isaksen, L., Bonavita, M., Buizza, R., Fisher, M., Haseler, J., Leutbecher, M., Raynaud, L.: Ensemble of Data Assimilations at ECMWF. Tech. Rep. ECMWF RD Tech. Memo. 636, 45 p. (2010)

    Google Scholar 

  25. Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Nikulin, G., Haens-ler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.F., Teich-mann, C., Valentini, R., Vautard, R., Weber, B.: 2014: EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg. Environ. Chang. 14, 563–578 (2013)

    Article  Google Scholar 

  26. Jones, C., Giorgi, F., Asrar, G.: The Coordinated Regional Downscaling Experiment: CORDEX. An international downscaling link to CMIP5. CLIVAR Exchanges 56, 16 (2), 34–40 (2011)

    Google Scholar 

  27. Kotlarski, S., Keuler, K., Christensen, O.B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., Wulfmeyer, V.: Regional climate modelling on European scales: A joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci. Model Dev. 7, 1297–1333 (2014)

    Article  Google Scholar 

  28. Lambert, S.J., Boer, G.J.: CMIP1 evaluation and intercomparison of coupled climate models. Clim. Dyn. 17(2–3), 83–106 (2001)

    Article  Google Scholar 

  29. Lanczos, C.: Applied Analysis. Prentice-Hall, Englewood Cliffs, NJ, USA, 539 p. (1956). Reprinted by Dover New York, 1988, ISBN 0-486-65656-X

    Google Scholar 

  30. Lorenz, E.: Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–142 (1963)

    Article  Google Scholar 

  31. Manabe, S., Wetherald, R.T.: The effects of doubling the CO2-concentration on the climate of a general circulation model. J. Atmos. Sci. 32, 3–15 (1975)

    Article  Google Scholar 

  32. Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J., Taylor, K.E.: The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007)

    Article  Google Scholar 

  33. Meehl, G.A., Moss, R., Taylor, K.E., Eyring, V., Stouffer, R.J., Bony, S., Stevens, B.: Climate model intercomparison: preparing for the next phase. EOS Trans. Am. Geophys. Union 95, 77–78 (2014)

    Article  Google Scholar 

  34. Migliorini, S., Dixon, M., Bannister, R., Ballard, S.: Ensemble prediction for nowcasting with a convection-permitting model. I: description of the system and the impact of radar-derived surface precipitation rates. Tellus 63A, 468–496 (2011)

    Article  Google Scholar 

  35. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J.: The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010)

    Article  Google Scholar 

  36. Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Grübler, A., Jung, T.Y., Kram, T., La Rovere, E.L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Raihi, K., Roehrl, A., Rogner, H. H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N., Dadi, Z.: IPCC Special Report on Emissions Scenarios. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  37. Ollinaho, P., Leutbecher, M., Beljaars, A., Sandu, I.: Stochastic parametrization of boundary layer processes in ENS. EMS Annual Meeting Abstracts 12, EMS2015-224 (2015)

    Google Scholar 

  38. Palmer, T.N., Buizza, R., Doblas-Reyes, F., Jung, T., Leutbecher, M., Shutts, G., Steinheimer, M., Weisheimer, A.: Stochastic parametrization and model uncertainty. Tech. Rep., ECMWF Tech. Memo. 598, 42 p. (2009)

    Google Scholar 

  39. Palmer, T.N., Tibaldi, S.: On the prediction of forecast skill. Mon. Weather Rev. 116, 2453–2480 (1988)

    Article  Google Scholar 

  40. Puri, K., Barkmeijer, J. Palmer, T.N.: Ensemble prediction of tropical cyclones using targeted diabatic singular vectors. ECMWF Tech. Memo. 298, 31 p. (1999)

    Google Scholar 

  41. Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., Masson, V.: The AROME-France convective-scale operational model. Mon. Weather Rev. 139, 976–991 (2011)

    Article  Google Scholar 

  42. Stappers, R., Barkmeijer, J.: HIRLAM CAPE singular vectors. HIRLAM Newsl. 54, 76–80 (2008)

    Google Scholar 

  43. Szabó, P., Szépszó, G.: Quantifying sources of uncertainty in temperature and precipitation projections over different parts of Europe. In: Mathematical problems in meteorological modelling, pp.~. Springer, Heidelberg (2016)

    Google Scholar 

  44. Szintai, B., Szűcs, M., Randriamampianina, R., Kullmann, L.: Application of the AROME non-hydrostatic model at the Hungarian Meteorological Service: physical parameterizations and ensemble forecasting. Időjárás 119, 241–266 (2015)

    Google Scholar 

  45. Taylor, K.E., Stouffer, R.J., Meehl, G.A.: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012). doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  46. Toth, Z., Kalnay, E.: Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 74, 2317–2330 (1993)

    Article  Google Scholar 

  47. Toth, Z., Kalnay, E.: Ensemble forecasting at NCEP and the breeding method. Mon. Weather Rev. 125, 3297–3319 (1997)

    Article  Google Scholar 

  48. van der Linden P., Mitchell, J.F.B. (eds.): ENSEMBLES: Climate Change and Its Impacts: Summary of Research and Results from the ENSEMBLES Project. Met Office Hadley Centre, Exeter, United Kingdom, 160 p. (2009)

    Google Scholar 

  49. Vialard, J., Vitart, F., Balmaseda, M., Stockdale, T., Anderson, D.: An ensemble generation method for seasonal forecasting with an ocean-atmosphere coupled model. Mon. Weather Rev. 133, 441–453 (2005)

    Article  Google Scholar 

  50. Vié, B., Nuissier, O., Ducrocq, V.: Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: uncertainty on initial conditions and lateral boundary conditions. Mon. Weather Rev. 139, 403–423 (2011)

    Article  Google Scholar 

  51. Wang, Y., Bellus, M., Wittmann, C., Steinheimer, M., Weidle, F., Kann, A., Ivatek-Šahdan, S., Tian, W., Ma, X., Tascu, S., Bazile, E.: The Central European limited-area ensemble forecasting system: ALADIN-LAEF. Q. J. R. Meteorol. Soc. 137, 483–502 (2011). doi:10.1002/qj.751

    Article  Google Scholar 

Download references

Acknowledgments

We were delighted to have a possibility to contribute in the present volume of European Consortium for Mathematics in Industry. We are very thankful to the editors for their patient cooperation. We appreciate the report and suggestions of the reviewer of the chapter. We are very grateful to our colleagues at the Hungarian Meteorological Service and especially to Péter Szabó for his careful English review.

Author information

Authors and Affiliations

  1. Hungarian Meteorological Service, Kitaibel Pál street 1, 1024, Budapest, Hungary

    Mihály Szűcs & Gabriella Szépszó

  2. Erdőalja utca 1/C, 2083, Solymár, Hungary

    András Horányi

Authors
  1. Mihály Szűcs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. András Horányi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriella Szépszó
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mihály Szűcs .

Editor information

Editors and Affiliations

  1. Institute of Mathematics, Eötvös Lorand University, Institute of Mathematics, Budapest, Hungary

    András Bátkai

  2. Dept. of Appl.Analysis & Comp.Math., Eötvös Loránd University Institute of Mathematics, Budapest, Hungary

    Petra Csomós

  3. Institute of Mathematics, Eötvös Lorand University, Budapest, Hungary

    István Faragó

  4. Solymár, Hungary

    András Horányi

  5. Hungarian Meteorological Service, Budapest, Hungary

    Gabriella Szépszó

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Szűcs, M., Horányi, A., Szépszó, G. (2016). Ensemble Methods in Meteorological Modelling. In: Bátkai, A., Csomós, P., Faragó, I., Horányi, A., Szépszó, G. (eds) Mathematical Problems in Meteorological Modelling. Mathematics in Industry(), vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-40157-7_11

Download citation

Publish with us

Policies and ethics

Search

Navigation