Reply to “Comments on ‘Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods”’

Kirien Whan R&D Weather and Climate Modeling, Royal Netherlands Meteorological Institute, De Bilt, Netherlands

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Maurice Schmeits R&D Weather and Climate Modeling, Royal Netherlands Meteorological Institute, De Bilt, Netherlands

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© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kirien Whan, whan@knmi.nl

The original article that was the subject of this comment/reply can be found at http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-17-0290.1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kirien Whan, whan@knmi.nl

The original article that was the subject of this comment/reply can be found at http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-17-0290.1.

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  • Ben Bouallègue, Z., 2013: Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515524, https://doi.org/10.1175/WAF-D-12-00062.1.

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  • Glahn, B., 2019: Comments on “Comparing area probability forecasts of (extreme) local precipitation using parametric and machine learning statistical postprocessing methods.” Mon. Wea. Rev., 147, 34953496, https://doi.org/10.1175/MWR-D-19-0089.1.

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  • Lugt, D., 2013: Improving GLAMEPS wind speed forecasts by statistical postprocessing. Intern Rep. IR-2013-03, KNMI, De Bilt, Netherlands, 18 pp., http://bibliotheek.knmi.nl/knmipubIR/IR2013-03.pdf.

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  • Messner, J. W., G. J. Mayr, A. Zeileis, and D. S. Wilks, 2014b: Heteroscedastic extended logistic regression for postprocessing of ensemble guidance. Mon. Wea. Rev., 142, 448456, https://doi.org/10.1175/MWR-D-13-00271.1.

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  • Ruiz, J. J., C. Saulo, and E. Kalnay, 2012: How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part II: Sensitivity to ensemble generation method. Meteor. Appl., 19, 314–324, https://doi.org/10.1002/met.262.

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  • Schmeits, M. J., and K. J. Kok, 2010: A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Mon. Wea. Rev., 138, 41994211, https://doi.org/10.1175/2010MWR3285.1.

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  • Whan, K., and M. Schmeits, 2018: Comparing area probability forecasts of (extreme) local precipitation using parametric and machine learning statistical postprocessing methods. Mon. Wea. Rev., 146, 36513673, https://doi.org/10.1175/MWR-D-17-0290.1.

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  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361–368, https://doi.org/10.1002/met.134.

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