Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO,
Irrigation and drainage paper 56, FAO, Rome, 300, p. D05109, 1998.

Archambeau, C., Lee, J. A., and Verleysen, M.: On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures, in:
ESANN'2003 proceedings – European Symposium on Artificial Neural Networks,
23–25 April 2003, Bruges, Belgium, 99–106, ISBN 2-930307-03-X, 2003.

Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M. and Reinhardt, T.: Operational convective-scale numerical
weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, 2011.

Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, 2015.

Bentzien, S. and Friederichs, P.: Generating and calibrating probabilistic quantitative precipitation forecasts from the high-resolution
NWP model COSMO-DE, Weather Forecast., 27, 988–1002, 2012.

Beran, R. and Hall, P.: Interpolated nonparametric prediction intervals and confidence intervals, J. Roy. Stat. Soc. B, 55, 643–652,
1993.

Bremnes, J. B.: Probabilistic Wind Power Forecasts Using Local Quantile Regression, Wind Energy, 7, 47–54, 2004.

Bröcker, J. and Smith, L. A.: From ensemble forecasts to predictive distribution functions, Tellus A, 60, 663–678, 2008.

Buizza, R., Houtekamer, P. L., Pellerin, G., Toth, Z., Zhu, Y., and Wei, M.: A comparison of the ECMWF, MSC, and NCEP global ensemble
prediction systems, Mon. Weather Rev., 133, 1076–1097, 2005.

Casella, G. and Berger, R. L.: Statistical inference (Vol. 2), Duxbury, Pacific Grove, CA, 2002.

Castro, F. X., Tudela, A., and Sebastià, M. T.: Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain),
Agr. Forest Meteorol., 116, 49–59, 2003.

Chirico, G. B., Pelosi, A., De Michele, C., Bolognesi, S. F., and D'Urso, G.: Forecasting potential evapotranspiration by combining
numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy, J. Agric. Sci., 156, 702–710,
https://doi.org/10.1017/S0021859618000084, 2018.

Davò, F., Alessandrini, S., Sperati, S., Delle Monache, L., Airoldi, D., and Vespucci, M. T.: Post-processing techniques and
principal component analysis for regional wind power and solar irradiance forecasting, Solar Energy, 134, 327–338, 2016.

Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., and Searight, K.: Probabilistic weather prediction with an analog ensemble,
Mon. Weather Rev., 141, 3498–3516, 2013.

Fraley, C., Raftery, A. E., and Gneiting, T.: Calibrating multimodelmulti-model forecast ensembles with exchangeable and missing members using Bayesian model averaging, Mon. Weather Rev., 138, 190–202, 2010.

Fraley, C., Raftery, A. E., Sloughter, J. M., and Gneiting T.: EnsembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging, R package version 5.1.3, available at: https://CRAN.R-project.org/package=ensembleBMA (last access: 27 February 2020), 2016.

Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203–1211, 1972.

Glahn, H. R. and Ruth, D. P.: The new digital forecast database of the National Weather Service, B. Am. Meteorol. Soc., 84, 195–202,
2003.

Gneiting, T.: Calibration of medium-range weather forecasts, European Centre for Medium-Range Weather Forecasts, Technical Memorandum No. 719, Reading, UK, 30 pp., 2014.

Gneiting, T., Raftery, A. E., Westveld III, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output
statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118, 2005.

Hagedorn, R.: Using the ECMWF reforecast data set to calibrate EPS forecasts, ECMWF Newslett., 117, 8–13, 2008.

Hagedorn, R., Hamill, T. M., and Whitaker, J. S.: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I:
Two-meter temperatures, Mon. Weather Rev., 136, 2608–2619, 2008.

Hagedorn, R., Buizza, R., Hamill, T. M., Leutbecher, M., and Palmer, T. N.: Comparing TIGGE multimodelmulti-model forecasts with
reforecast-calibrated ECMWF ensemble forecasts, Q. J. Roy. Meteorol. Soc., 138, 1814–1827, 2012.

Hamill, T. M. and Colucci, S. J.: Verification of Eta–RSM short-range ensemble forecasts, Mon. Weather Rev., 125, 1312–1327, 1997.

Hamill, T. M. and Whitaker, J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and
application, Mon. Weather Rev., 134, 3209–3229, 2006.

Hamill, T. M., Bates, G. T., Whitaker, J. S., Murray, D. R., Fiorino, M., Galarneau Jr., T. J., Zhu, Y., and Lapenta, W.: Noaa's Second-Generation Global Medium-Range Ensemble Reforecast
Dataset, B. Am. Meteorol. Soc., 94, 1553–1565, 2013.

Hersbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570,
2000.

Hobbins, M., McEvoy, D., and Hain, C.: Evapotranspiration, evaporative demand, and drought, in: Drought and Water Crises: Science,
Technology, and Management Issues, edited by: Wilhite, D. and Pulwarty, R., CRC Press, Boca Raton, USA, pp. 259–288, 2017.

Hong, S. Y. and Dudhia, J.: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies,
B. Am. Meteorol. Soc., 93, ES6–ES9, 2012.

Ishak, A. M., Bray, M., Remesan, R., and Han, D.: Estimating reference evapotranspiration using numerical weather modelling,
Hydrol. Process., 24, 3490–3509, 2010.

Kang, T. H., Kim, Y. O., and Hong, I. P.: Comparison of pre- and post-processors for ensemble streamflow prediction, Atmos. Sci. Lett.,
11, 153–159, 2010.

Kann, A., Wittmann, C., Wang, Y., and Ma, X.: Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution
analysis, Mon. Weather Rev., 137, 3373–3387, 2009.

Kann, A., Haiden, T., and Wittmann, C.: Combining 2-m temperature nowcasting and short-range ensemble forecasting, Nonlinear
Proc. Geoph., 18, 903–910, 2011.

Klein, W. H. and Glahn, H. R.: Forecasting local weather by means of model output statistics, B. Am. Meteorol. Soc., 55, 1217–1227,
1974.

Landeras, G., Ortiz-Barredo, A., and López, J. J.: Forecasting weekly evapotranspiration with ARIMA and artificial neural network models, J. Irrig. Drain. Eng., 135, 323–334, 2009.

Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys., 227, 3515–3539, 2008.

Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved post-processing of hydrologic forecast ensembles, Hydrol. Process., 28,
104–122, 2014.

Mase, A. S. and Prokopy, L. S.: Unrealized potential: A review of perceptions and use of weather and climate information in agricultural
decision making, Weather Clim. Soc., 6, 47–61, 2014.

Medina, H. and Tian, D.: Post-processed reference crop evapotranspiration forecasts, https://doi.org/10.17605/OSF.IO/NG6WA, 2020.

Medina, H., Tian, D., Srivastava, P., Pelosi, A., and Chirico, G. B.: Medium-range reference evapotranspiration forecasts for the
contiguous United States based on multimodelmulti-model numerical weather predictions, J. Hydrol., 562, 502–517, 2018.

Medina, H., Tian, D., Marin, F. R., and Chirico, G. B.: Comparing GEFS, ECMWF, and Postprocessing Methods for Ensemble Precipitation Forecasts over Brazil, J. Hydrometeorol., 20, 773–790, 2019.

Messner, J. W., Mayr, G. J., Zeileis, A., and Wilks, D. S.: Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance, Mon. Weather Rev., 142, 448–456, https://doi.org/10.1175/MWR-D-13-00271.1, 2014.

Mohan, S. and Arumugam, N.: Forecasting weekly reference crop evapotranspiration series, Hydrol. Sci. J., 40, 689–702, 1995.

Møller, J. K., Nielsen, H. A., and Madsen, H.: Time-Adaptive Quantile Regression, Comput. Stat. Data Anal., 52, 1292–1303, 2008.

National Research Council of the National Academies: Completing the Forecast: Characterizing and Communicating Uncertainty for Better
Decisions Using Weather and Climate Forecasts, The National Academies Press, Washington, D.C., 124 pp., 2006.

Pelosi, A., Medina, H., Villani, P., D'Urso, G., and Chirico, G. B.: Probabilistic forecasting of reference evapotranspiration with a
limited area ensemble prediction system, Agr. Water Manage., 178, 106–118, 2016.

Pelosi, A., Medina, H., Van den Bergh, J., Vannitsem, S., and Chirico, G. B.: Adaptive Kalman filtering for post-processing ensemble
numerical weather predictions, Mon. Weather Rev., 145, 4837–4854, https://doi.org/10.1175/MWR-D-17-0084.1, 2017.

Perera, K. C., Western, A. W., Nawarathna, B., and George, B.: Forecasting daily reference evapotranspiration for Australia using
numerical weather prediction outputs, Agr. Forest Meteorol., 194, 50–63, 2014.

Pinson, P. and Madsen, H.: Ensemble-Based Probabilistic Forecasting at Horns Rev, Wind Energy, 12, 137–155, 2009.

Prokopy, L. S., Haigh, T., Mase, A. S., Angel, J., Hart, C., Knutson, C., Lemos, M. C., Lo, Y. J., McGuire, J., Morton, L. W., and
Perron, J.: Agricultural advisors: a receptive audience for weather and climate information?, Weather Clim. Soc., 5, 162–167, 2013.

Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles,
Mon. Weather Rev., 133, 1155–1174, 2005.

R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria,
available at: http://www.R-project.org/ (last access: 27 February 2020), 2014.

Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V., and Coxi, D. R.: Probabilistic modelling of water balance at a point: the
role of climate, soil and vegetation, P. Roy. Soc. Lond. A, 455, 3789–3805, 1999.

Roulston, M. S. and Smith, L. A.: Combining dynamical and statistical ensembles, Tellus A, 55, 16–30, 2003.

Scheuerer, M. and Büermann, L.: Spatially adaptive post-processing of ensemble forecasts for temperature, J. Roy. Stat. Soc. C, 63, 405–422, 2014.

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

Siegert, S.: SpecsVerification: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate, R package version 0.5-2, available at: https://cran.r-project.org/web/packages/SpecsVerification/ (last access: 27 February 2020), 2017.

Silva, D., Meza, F. J., and Varas, E.: Estimating reference evapotranspiration (ET_{0}) using numerical weather forecast data in central Chile, J. Hydrol., 382, 64–71, 2010.

Sloughter, J. M., Gneiting, T., and Raftery, A. E.: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, J. Am. Stat. Assoc., 105, 25–35, 2010.

Swinbank, R., Kyouda, M., Buchanan, P., Froude, L., Hamill, T. M., Hewson, T. D., Keller, J. H., Matsueda, M., Methven, J., Pappenberger, F., and Scheuerer, M.: The Tigge Project and Its Achievements, B. Am. Meteorol. Soc., 97, 49–67, 2016.

Tian, D. and Martinez, C. J.: Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts,
J. Hydrol., 475, 350–364, 2012a.

Tian, D. and Martinez, C. J.: Forecasting Reference Evapotranspiration Using Retrospective Forecast Analogs in the Southeastern United States, J. Hydrometeorol., 13, 1874–1892, 2012b.

Tian, D. and Martinez, C. J.: The GEFS-based daily reference evapotranspiration (ET_{0}) forecast and its implication for water management in the southeastern United States, J. Hydrometeorol., 15, 1152–1165, 2014.

Tian, X., Xie, Z., Wang, A., and Yang, X.: A new approach for Bayesian model averaging, Sci. China Earth Sci., 55, 1336–1344, 2012.

Toth, Z., Talagrand, O., Candille, G., and Zhu, Y.: Probability and ensemble forecasts, Forecast Verification: A Practitioner's Guide in
Atmospheric Science, John Wiley & Sons Ltd., England, 137–163, 2003.

van Osnabrugge, B., Uijlenhoet, R., and Weerts, A.: Contribution of potential evaporation forecasts to 10-day streamflow forecast skill
for the Rhine River, Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, 2019.

Vanvyve, E., Delle Monache, L., Monaghan, A. J., and Pinto, J. O.: Wind resource estimates with an analog ensemble approach,
Renew. Energ., 74, 761–773, 2015.

Verkade, J. S., Brown, J. D., Reggiani, P., and Weerts, A. H.: Post-processing ECMWF precipitation and temperature ensemble reforecasts
for operational hydrologic forecasting at various spatial scales, J. Hydrol., 501, 73–91, https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013.

Verzijlbergh, R. A., Heijnen, P. W., de Roode, S. R., Los, A., and Jonker, H. J.: Improved model output statistics of numerical weather
prediction based irradiance forecasts for solar power applications, Solar Energy, 118, 634–645, 2015.

Vrugt, J. A., Diks, C. G., and Clark, M. P.: Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling, Environ. Fluid
Mech., 8, 579–595, 2008.

Wang, X. and Bishop, C. H.: Improvement of ensemble reliability with a new dressing kernel, Q. J. Roy. Meteorol. Soc., 131, 965–986, 2005.

Whan, K. and Schmeits, M: Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning
Statistical Postprocessing Methods, Mon. Weather Rev., 146, 3651–3673, https://doi.org/10.1175/MWR-D-17-0290.1, 2018.

Wilks, D. S.: Comparison of ensemble-MOS methods in the Lorenz'96 setting, Meteorol. Appl., 13, 243–256, 2006.

Wilks, D. S.: Extending logistic regression to provide full probability distribution MOS forecasts, Meteorol. Appl., 16, 361–368, 2009.

Wilks, D. S.: Sampling distributions of the Brier score and Brier skill score under serial dependence, Q. J. Roy. Meteor. Soc., 136,
2109–2118, 2010.

Wilks, D. S.: Multivariate ensemble Model Output Statistics using empirical copulas, Q. J. Roy. Meteor. Soc., 141, 945–952, 2015.

Wilks, D. S. and Hamill, T. M.: Comparison of ensemble-MOS methods using GFS reforecasts, Mon. Weather Rev., 135, 2379–2390, 2007.

Williams, R. M., Ferro, C. A. T., and Kwasniok, F.: A comparison of ensemble post-processing methods for extreme events,
Q. J. Roy. Meteor. Soc., 140, 1112–1120, 2014.

Wilson, L. J., Beauregard, S., Raftery, A. E., and Verret, R.: Calibrated surface temperature forecasts from the Canadian ensemble
prediction system using Bayesian model averaging, Mon. Weather Rev., 135, 1364–1385, 2007.

Yuen, R., Baran, S., Fraley, C., Gneiting, T., Lerch, S., Scheuerer, M., and Thorarinsdottir, T.: ensembleMOS: Ensemble Model Output Statistics, R package version 0.8.2, available at: https://CRAN.R-project.org/package=ensembleMOS (last access: 27 February 2020) 2018.

Zhang, J., Draxl, C., Hopson, T., Delle Monache, L., Vanvyve, E., and Hodge, B. M.: Comparison of numerical weather prediction based
deterministic and probabilistic wind resource assessment methods, Appl. Energy, 156, 528–541, 2015.

Zhao, T., Wang, Q. J., and Schepen, A.: A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from
GCM outputs, Agr. Forest Meteorol., 269, 88–101, 2019.