Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-7bb8b95d7b-nptnm Total loading time: 0 Render date: 2024-09-11T06:40:56.549Z Has data issue: false hasContentIssue false

7 - Forecasting high-impact weather using ensemble prediction systems

from Part II - High-impact weather in mid latitudes

Published online by Cambridge University Press:  05 March 2016

By
Richard Swinbank ,
Petra Friederichs  and
Sabrina Wahl
Edited by
Jianping Li ,
Richard Swinbank ,
Richard Grotjahn  and
Hans Volkert
Jianping Li
Affiliation:
Beijing Normal University
Richard Swinbank
Affiliation:
Met Office, Exeter
Richard Grotjahn
Affiliation:
University of California, Davis
Hans Volkert
Affiliation:
Deutsche Zentrum für Luft- und Raumfahrt eV (DLR)
Get access
Type
Chapter
Information
Dynamics and Predictability of Large-Scale, High-Impact Weather and Climate Events , pp. 95 - 112
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. L. (1996). A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate, 9, 1518–1530.2.0.CO;2>CrossRefGoogle Scholar
Baldauf, M., Seifert, A., Förstner, J., et al. (2011). Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mon. Wea. Rev., 139, 3887–3905.CrossRefGoogle Scholar
Ben Bouallègue, Z., Theis, S. E., and Gebhardt, C. (2013). Enhancing COSMO-DE ensemble forecasts by inexpensive techniques. Meteor. Z., 22, 49–59..CrossRefGoogle Scholar
Bentzien, S. and Friederichs, P. ( 2012 ). Generating and calibrating probabilistic quantitative precipitation forecasts from the high-resolution NWP model COSMO-DE. Wea. Forecasting, 27, 988–1002.CrossRefGoogle Scholar
Bentzien, S. and Friederichs, P. (2014). Decomposition and graphical portrayal of the quantile score. Quart. J. Roy. Meteor. Soc., 40, 1924–1934.CrossRefGoogle Scholar
Bouttier, F., Vie, B., Nussier, O., and Raynaud, L. (2012). Impact of stochastic physics in a convection-permitting ensemble. Mon. Wea. Rev., 140, 3706–3721.CrossRefGoogle Scholar
Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B., and Beare, S. E. (2008). The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703–722,CrossRefGoogle Scholar
Bremnes, J. B. (2004). Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Mon. Wea. Rev., 132, 338–347.2.0.CO;2>CrossRefGoogle Scholar
Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 1–3.2.0.CO;2>CrossRefGoogle Scholar
Broecker, J. (2012). Probability forecasts. In Forecast Verification: A Practitioner’s Guide in Atmospheric Science, eds. Jolliffe, I. T. and Stephenson, D. B., 2nd edn., Wiley.Google Scholar
Buizza, R., Miller, M., and Palmer, T. N. (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. R. Meteorol. Soc., 125, 2887–2908.CrossRefGoogle Scholar
Candille, G. and Talagrand, O. (2005). Evaluation of probabilistic prediction systems for a scalar variable, Quart. J. Roy. Meteor. Soc., 131, 2131–2150.CrossRefGoogle Scholar
Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer.CrossRefGoogle Scholar
Cooley, D., Nychka, D., and Naveau, P. (2007). Bayesian spatial modeling of extreme precipitation return levels. J. Amer. Stat. Assoc., 102, 824–840.CrossRefGoogle Scholar
Cooley, D. and Sain, S. R. (2010). Spatial hierarchical modeling of precipitation extremes from a regional climate model. Journal of Agricultural, Biological, and Environmental Statistics, 15, 381–402.CrossRefGoogle Scholar
Di Narzo, A. F. and Cocchi, D. (2010). A Bayesian hierarchical approach to ensemble weather forecasting. J. Roy. Stat. Soc. Ser. C, 59, 405–422.CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.CrossRefGoogle Scholar
Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99(C5), 10143–10162.CrossRefGoogle Scholar
Fahrmeir, L. and Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. Springer.CrossRefGoogle Scholar
Ferro, C. A. T. (2013). Fair scores for ensemble forecasts. Quart. J. Roy. Meteor. Soc., doi: 10.1002/qj.2270, 2013CrossRefGoogle Scholar
Fricker, T.E., Ferro, C. A. T., and Stephenson, D. B. (2013). Three recommendations for evaluating climate predictions, Meteorol. Appl., 20, 246–255.Google Scholar
Friederichs, P. and Hense, A. (2007). Statistical downscaling of extreme precipitation events using censored quantile regression. Mon. Wea. Rev., 135, 2365–2378.CrossRefGoogle Scholar
Friederichs, P. (2010). Statistical downscaling of extreme precipitation using extreme value theory. Extremes, 13, 109–132.CrossRefGoogle Scholar
Frigessi, A., Haug, O., and Rue, H. (2003). A dynamic mixture model for unsupervised tail estimation without threshold selection. Extremes, 5, 219–236.CrossRefGoogle Scholar
Gebhardt, C., Theis, S. E., Paulat, M., and Ben Bouallègue, Z. (2011). Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variations of lateral boundaries. Atmospheric Research, 100, 168–177.CrossRefGoogle Scholar
Gneiting, T., Balabdaoui, F., and Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society. Series B (Methodological), 69, 243–268.CrossRefGoogle Scholar
Gneiting, T. and Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and its Application, 1, 125151.CrossRefGoogle Scholar
Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359–378.CrossRefGoogle Scholar
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS Estimation. Mon. Wea. Rev., 133, 1098–1118.CrossRefGoogle Scholar
Gneiting, T. and Ranjan, R. (2013). Combining predictive distributions. Electronic Journal of Statistics, 7, 1747–1782.CrossRefGoogle Scholar
Golding, B. W., Ballard, S. P., Mylne, K., et al. (2014). Forecasting capabilities for the London 2012 Olympics. Bull. Amer. Meteor. Soc., 95, 883–896.CrossRefGoogle Scholar
Hagedorn, R, Hamill, T. M., and Whitaker, J. S. (2008). Probabilistic forecast calibration using ECMWF and GFS ensemble forecasts. Part I: 2-meter temperature. Mon. Wea. Rev., 136, 2608–2619.CrossRefGoogle Scholar
Hamill, T. M. (2001). Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550–560.2.0.CO;2>CrossRefGoogle Scholar
Hamill, T. M., Hagedorn, R., and Whitaker, J. S. (2008). Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: precipitation. Mon. Wea. Rev., 136, 2620–2632.CrossRefGoogle Scholar
Hamill, T. M. and Colucci, S. J. (1997). Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312–1327.2.0.CO;2>CrossRefGoogle Scholar
Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559–570.2.0.CO;2>CrossRefGoogle Scholar
Houtekamer, P. L. and Mitchell, H. L. (2005). Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269–3289.CrossRefGoogle Scholar
Jolliffe, I. T. and Stephenson, D. B. (eds.) (2010). Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd edn. Wiley.Google Scholar
Joslyn, S. and Savelli, S. (2010). Communicating forecast uncertainty: Public perception of weather forecast uncertainty. Meteor. Appl., 17, 180–195.CrossRefGoogle Scholar
Joslyn, S., Nadav-Greenberg, L., and Nichols, R. M. (2009). Probability of precipitation: Assessment and enhancement of end-user understanding. Bull. Amer. Meteor. Soc., 90, 185–193.CrossRefGoogle Scholar
Kalnay, E. (2003). Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, Cambridge.Google Scholar
Koenker, R. (2005). Quantile regression. Econometric Society Monographs, 38, Cambridge University Press.Google Scholar
Koenker, R. and Machado, J. A. F. (1999). Goodness of fit and related inference processes for quantile regression. J. Amer. Stat. Assoc., 94, 1296–1310.CrossRefGoogle Scholar
Lean, H. W., Clark, P. A., Dixon, M., et al. (2008). Characteristics of high-resolution versions of the Met Office unified model for forecasting convection over the United Kingdom. Mon. Wea. Rev., 136, 3408–3424.CrossRefGoogle Scholar
Lorenz, E. N. (1969). The predictability of a flow which possesses many scales of motion. Tellus, 21, 289–307.CrossRefGoogle Scholar
Matheson, J. E. and Winkler, R. L. (1976). Scoring rules for continuous probability distributions. Management Science, 22, 1087–1096.CrossRefGoogle Scholar
Matsueda, M. and Nakazawa, T. (2014). Early warning products for severe weather events derived from operational medium-range ensemble forecasts. Meteor. Appl., doi: 10.1002/met.1444CrossRefGoogle Scholar
Molteni, F., Buizza, R., Palmer, T. N., and Petroliagis, T. (1996). The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119.CrossRefGoogle Scholar
Murphy, A. H. (1973). A new vector partition of the probability score. Journal of Applied Meteorology, 12, 595–600.2.0.CO;2>CrossRefGoogle Scholar
Murphy, A. H. and Winkler, R. L. (1987). A general framework for forecast verification. Mon. Wea. Rev., 115, 1330–1338.2.0.CO;2>CrossRefGoogle Scholar
Murphy, J. M. and Palmer, T. N. (1986). Experimental monthly long-range forecasts for the United Kingdom. 2. A Real-time long-range forecast by an ensemble of numerical integrations. Meteorol. Mag., 115, 337–349.Google Scholar
Neal, R., Boyle, P., Grahame, N., Mylne, K., and Sharpe, M. (2014). Ensemble based first guess support towards a risk based national severe weather warning service, Meteorol. Apps., 21, 563–577.CrossRefGoogle Scholar
NCAR – Research Applications Laboratory (2013). Verification: Weather Forecast Verification Utilities. R package version 1.36.Google Scholar
Nussier, O., Joly, B., Vie, B., and Ducrocq, V. (2012). Uncertainty of lateral boundary conditions in a convective-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events. Nat. Hazards Earth Syst. Sci., 12, 2993–3011.CrossRefGoogle Scholar
Oesting, M., Schlather, M., and Friederichs, P. (2013). Conditional modelling of extreme wind gusts by bivariate Brown-Resnick processes. arXiv:1312.4584 [stat.ME].Google Scholar
Peralta, C., Ben Bouallègue, Z., Theis, S. E., Gebhardt, C., and Buchhold, M. (2012). Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res., 117, D07108.CrossRefGoogle Scholar
R Core Team, R (2013). A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.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.CrossRefGoogle Scholar
Roberts, N. M. and Lean, H. W. (2008). Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78–97.CrossRefGoogle Scholar
Roulston, M. S. and Smith, L. A. (2002). Evaluating probabilistic forecasts using information theory. Mon. Wea. Rev., 130, 1653–1660.2.0.CO;2>CrossRefGoogle Scholar
Sang, H. and Gelfand, A. E. (2009). Hierarchical modeling for extreme values observed over space and time. Environmental and Ecological Statistics, 16, 407–426.CrossRefGoogle Scholar
Schmeits, M. J. and Kok, K. J. (2010). A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Mon. Wea. Rev., 138, 4199–4211.CrossRefGoogle Scholar
Shutts, G. (2005). A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079–3102.CrossRefGoogle Scholar
Stephan, K., Klink, S., and Schraff, C. (2008). Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Quart. J. Roy. Meteor. Soc., 134, 1315–1326.CrossRefGoogle Scholar
Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F. J., and Balmaseda, M. (2005). Forecast assimilation: a unified framework for the combination of multi-model weather and climate predictions. Tellus A, 57, 253–264.CrossRefGoogle Scholar
Tang, Y., Lean, H. W., and Bornemann, J., (2012). The benefits of the Met Office variable resolution NWP model for forecasting convection. Meteor. Appl., 20, 417–426.CrossRefGoogle Scholar
Tennant, W. J., Shutts, G. J., Arribas, A., and Thompson, S. A. (2011). Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 1190–1206.CrossRefGoogle Scholar
Theis, S. E., Hense, A., and Damrath, U. (2005). Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257–268.CrossRefGoogle Scholar
Tibshirani, R. J. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.CrossRefGoogle Scholar
Toth, Z., and Kalnay, E. (1993). Ensemble forecasting at NMC – the generation of perturbations. Bull. Amer. Meteorol. Soc., 74, 2317–2330.2.0.CO;2>CrossRefGoogle Scholar
Van der Grijn, G., Paulsen, J. E., Lalarette, F., and Leutbecher, M. (2004). Early medium-range forecasts of tropical cyclones. ECMWF newsletter, 102, 7–14.Google Scholar
WMO (2005). THORPEX International Research Implementation Plan, World Meteorological Organization WMO/TD no 1258, WWRP/THORPEX no. 4.Google Scholar
Wang, X. and Bishop, C. H. (2003). A comparison of breeding and Ensemble Transform Kalman Filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140–1158.2.0.CO;2>CrossRefGoogle Scholar
Wei, M., Toth, Z., Wobus, R., and Zhu, Y. (2008). Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus A, 60(1).CrossRefGoogle Scholar
Wilks, D. S. (2009). Extending logistic regression to provide full-probability-distribution MOS forecasts, Meteor. Appl., 16, 361–368.CrossRefGoogle Scholar
Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences, 3rd edn. Elsevier.Google Scholar
Yamaguchi, M., Nakazawa, T., and Hoshino, S. (2012). On the relative benefits of a multi-centre grand ensemble for tropical cyclone track prediction in the western North Pacific. Q. J. Roy. Meteorol. Soc., 138, 2019–2029.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×