I would like to thank the authors for thoroughly addressing my comments in a previous review. The new version is much better connected with the literature, the methodology and results are clearly presented, and the conclusions are nicely summarized. I have a final set of minor suggestions for the authors.
Minor comments
1. Please check the use of the words “significant” and “significantly” throughout the manuscript, since readers may associate them with “statistically significant” – even if you are not meaning that.
2. P6, L7-8: I think that an explanation on how you obtain deterministic forecasts from ensemble forecasts – although provided later in the manuscript – should go here.
3. P10, L8-11: The authors might want to consider putting these results into a table, for both calibration and validation periods.
4. P12, L10-11: The think the ensemble sizes (51 and 1000) should be specified in the Method section.
5. Conclusions: I suggest the authors connecting their future work with existing literature on data assimilation for hydrological forecasting (e.g., Clark et al. 2006; McMillan et al. 2013; Dechant and Moradkhani 2011; DeChant and Moradkhani 2014; Huang et al. 2016) and weather forecast post-processing – also referred to as pre-processing (e.g., Hamill et al. 2004, 2008; Fraley et al. 2010; Schmeits and Kok 2010; Verkade et al. 2013; Crochemore et al. 2016)
References
Clark, M. P., A. G. Slater, A. P. Barrett, L. E. Hay, G. J. McCabe, B. Rajagopalan, and G. H. Leavesley, 2006: Assimilation of snow covered area information into hydrologic and land-surface models. Adv. Water Resour., 29, 1209–1221, doi:10.1016/j.advwatres.2005.10.001.
Crochemore, L., M.-H. Ramos, and F. Pappenberger, 2016: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Hydrol. Earth Syst. Sci., 20, 3601–3618, doi:10.5194/hess-20-3601-2016.
Dechant, C. M., and H. Moradkhani, 2011: Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation. Hydrol. Earth Syst. Sci., 15, 3399–3410, doi:10.5194/hess-15-3399-2011.
DeChant, C. M., and H. Moradkhani, 2014: Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination. J. Hydrol., 519, 2967–2977, doi:10.1016/j.jhydrol.2014.05.045.
Fraley, C., A. E. Raftery, and T. Gneiting, 2010: Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging. Mon. Weather Rev., 138, 190–202, doi:10.1175/2009MWR3046.1.
Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble Reforecasting: Improving Medium-Range Forecast Skill Using Retrospective Forecasts. Mon. Weather Rev., 132, 1434–1447, doi:10.1175/1520-0493(2004)132<1434:ERIMFS>2.0.CO;2.
——, R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter Temperatures. Mon. Weather Rev., 136, 2608–2619, doi:10.1175/2007MWR2410.1.
Huang, C., A. J. Newman, M. P. Clark, A. W. Wood, and X. Zheng, 2016: Evaluation of snow data assimilation using the Ensemble Kalman Filter for seasonal streamflow prediction in the Western United States. Hydrol. Earth Syst. Sci. Discuss., 1–29, doi:10.5194/hess-2016-185.
McMillan, H. K., E. Ö. Hreinsson, M. P. Clark, S. K. Singh, C. Zammit, and M. J. Uddstrom, 2013: Operational hydrological data assimilation with the recursive ensemble Kalman filter. Hydrol. Earth Syst. Sci., 17, 21–38, doi:10.5194/hess-17-21-2013. http://www.hydrol-earth-syst-sci.net/17/21/2013/.
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. Weather Rev., 138, 4199–4211, doi:10.1175/2010MWR3285.1.
Verkade, J. S., J. D. Brown, P. Reggiani, and A. H. Weerts, 2013: Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales. J. Hydrol., 501, 73–91, doi:10.1016/j.jhydrol.2013.07.039. http://dx.doi.org/10.1016/j.jhydrol.2013.07.039. |