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Handbook of Hydrometeorological Ensemble Forecasting pp 99–149Cite as

Ensemble Methods for Meteorological Predictions

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Abstract

Since the atmospheric system is a nonlinear chaotic system, its numerical prediction is bound by a predictability limit due to imperfect initial conditions and models. Ensemble forecasting is a dynamical approach to quantify the predictability of weather, climate, and water forecasts. This chapter introduces various methods to create an ensemble of forecasts based on three aspects: perturbing initial conditions (IC), perturbing a model, and building a virtual ensemble. For generating IC perturbations, methods include (1) random, (2) time-lagged, (3) bred vector, (4) ensemble transform (ET), (5) singular vector (SV), (6) conditional nonlinear optimal perturbation (CNOP), (7) ensemble transform Kalman filter (ETKF), (8) ensemble Kalman filter (EnKF), and (9) perturbations in boundaries including land surface and topography. For generating model perturbations, methods include (1) multi-model and multi-physics, (2) stochastically perturbed parametrization tendency (SPPT), (3) stochastically kinetic energy backscatter (SKEB), (4) convection triggering, (5) stochastic boundary-layer humidity (SHUM), (6) stochastic total tendency perturbation (STTP), and (7) vorticity confinement. A method to create a spatially correlated random pattern (mask) needed by SPPT, SKEB, etc. is introduced based on the Markov process; a factor separation method is introduced to estimate the relative impact of various physics schemes and their interactions. A method of perturbing a dynamic core to create an ensemble is also mentioned. Quantitative forecast uncertainty information and ensemble products can also be generated from “virtual ensembles” based on existing deterministic forecasts through at least five different approaches including (1) time-lagged, (2) poor-man’s, (3) hybrid, (4) neighborhood, and (5) analog ensembles. Generally speaking, the selection of perturbation methods in constructing an EPS is more important for smaller-scale and shorter-range forecasts and less critical for larger-scale and longer-range forecasts. Finally, the frequently asked question about the trade-off between ensemble size and model resolution is discussed. By introducing these methods, we hope to help readers who are interested in ensemble forecasting but not familiar with these approaches to build their own EPS or produce ensemble products as well as for students to learn the subject of ensemble forecasting.

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Acknowledgments

Ms. Mary Hart is appreciated for her help to improve the readability of the manuscript. We thank Jack Kain and Binbin Zhou for their reviews.

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Authors and Affiliations

  1. Environmental Modeling Center/National Centers for Environmental Prediction (NCEP), NOAA, College Park, MD, USA

    Jun Du & Dingchen Hou

  2. European Centre for Medium Range Weather Forecasts, Reading, UK

    Roberto Buizza

  3. Institute of Atmospheric Sciences, Fudan University, Shanghai, China

    Mu Mu

  4. Cooperative Institute for Research in the Atmosphere, Earth System Research Lab (ESRL)/NOAA, Boulder, CO, USA

    Isidora Jankov

  5. Oceanography Division, Navy Research Laboratory, Stennis Space Center, MS, USA

    Mozheng Wei

  6. School of Atmospheric Sciences and Key Laboratory of Mesoscale Severe Weather, Ministry of Education, Nanjing University, Nanjing, China

    Huiling Yuan

  7. National Centers for Atmospheric Research, Boulder, CO, USA

    Judith Berner

  8. Canadian Meteorological Center, Environmental Canada, Montreal, Canada

    Martin Charron & Peter Houtekamer

  9. School of Meteorology, The University of Oklahoma, Norman, OK, USA

    Xuguang Wang

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  1. Jun Du
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  7. Isidora Jankov
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  8. Mu Mu
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  9. Xuguang Wang
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  10. Mozheng Wei
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  11. Huiling Yuan
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Corresponding author

Correspondence to Jun Du .

Editor information

Editors and Affiliations

  1. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, UK

    Florian Pappenberger

  3. National Center for Atmospheric Research, Boulder, CO, USA

    Andy Wood

  4. Department of Meteorology, Reading University, Reading, UK

    Hannah L. Cloke

  5. U.S. National Weather Service (retired), Annapolis, MD, USA

    John C. Schaake

Section Editor information

  1. School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China

    Huiling Yuan

  2. Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA

    Zoltan Toth

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Du, J. et al. (2019). Ensemble Methods for Meteorological Predictions. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_13

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