Baran, S., Hemri, S., and El Ayari, M.: Statistical post-processing of water level forecasts using Bayesian model averaging with doubly-truncated normal components, Water Resour. Res., 55, 3997–4013,
https://doi.org/10.1029/2018WR024028, 2019.
a
Baran, S., Baran, Á., Pappenberger, F., and Ben Bouallègue, Z.: Statistical post-processing of heat index ensemble forecasts: is there a royal road? Q. J. Roy. Meteor. Soc., 146, 3416–3434,
https://doi.org/10.1002/qj.3853, 2020.
a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55,
https://doi.org/10.1038/nature14956, 2015.
a
Bhend, J., Dabernig, M., Demaeyer, J., Mestre, O., and Taillardat, M.: EUPPBench postprocessing benchmark dataset – station data (v1.0), Zenodo [data set],
https://doi.org/10.5281/zenodo.7708362, 2023.
a
Bremnes, J. B.: Ensemble postprocessing using quantile function regression based on neural networks and Bernstein polynomials, Mon. Weather Rev., 148, 403–414,
https://doi.org/10.1175/MWR-D-19-0227.1, 2020.
a
Buizza, R.: Introduction to the special issue on “25 years of ensemble forecasting”, Q. J. Roy. Meteor. Soc., 145, 1–11,
https://doi.org/10.1002/qj.3370, 2018a.
a
Buizza, R.: Ensemble forecasting and the need for calibration, in: Statistical Postprocessing of Ensemble Forecasts, edited by: Vannitsem, S., Wilks, D. S., and Messner, J. W., Elsevier, Amsterdam, 15–48, ISBN: 978-0-12-812372-0, 2018b. a
Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M. and Zhu, Y.: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems, Mon. Weather Rev., 133, 1076–1097,
https://doi.org/10.1175/MWR2905.1, 2005
a
Chmielecki, R. M. and Raftery, A. E.: Probabilistic visibility forecasting using Bayesian model averaging, Mon. Weather Rev., 139, 1626–1636,
https://doi.org/10.1175/2010MWR3516.1, 2011.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k
Dabernig, M., Mayr, G. J., Messner, J. W., and Zeileis, A.: Spatial ensemble post-processing with standardized anomalies, Q. J. Roy. Meteor. Soc., 143, 909–916,
https://doi.org/10.1002/qj.2975, 2017.
a
Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data, 15, 2635–2653,
https://doi.org/10.5194/essd-15-2635-2023, 2023.
a
Fraley, C., Raftery, A. E., and Gneiting, T.: Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging, Mon. Weather Rev., 138, 190–202,
https://doi.org/10.1175/2009MWR3046.1, 2010.
a,
b
Friederichs, P. and Hense, A.: Statistical downscaling of extreme precipitation events using censored quantile regression, Mon. Weather Rev., 135, 2365–2378,
https://doi.org/10.1175/MWR3403.1, 2007.
a
Fundel, V. J., Fleischhut, N., Herzog, S. M., Göber, M., and Hagedorn, R.: Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end-users, Q. J. Roy. Meteor. Soc., 145, 210–231,
https://doi.org/10.1002/qj.3482, 2019.
a
Ghazvinian, M., Zhang, Y., Seo, D-J., He, M., and Fernando, N.: A novel hybrid artificial neural network - parametric scheme for postprocessing medium-range precipitation forecasts, Adv. Water Resour., 151, 103907,
https://doi.org/10.1016/j.advwatres.2021.103907, 2021.
a,
b
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118,
https://doi.org/10.1175/MWR2904.1, 2005.
a
Gultepe, I., Müller, M. D., and Boybeyi, Z.: A new visibility parameterization for warm-fog applications in numerical weather prediction models, J. Appl. Meteorol. Climatol., 45, 1469–1480,
https://doi.org/10.1175/JAM2423.1, 2006.
a
Hemri, S., Scheuerer, M., Pappenberger, F., Bogner, K., and Haiden, T.: Trends in the predictive performance of raw ensemble weather forecasts, Geophys. Res. Lett., 41, 9197–9205,
https://doi.org/10.1002/2014GL062472, 2014.
a,
b
Hemri, S., Haiden, T., and Pappenberger, F.: Discrete postprocessing of total cloud cover ensemble forecasts, Mon. Weather Rev., 144, 2565–2577,
https://doi.org/10.1175/MWR-D-15-0426.1, 2016.
a,
b
Jordan, A., Krüger, F., and Lerch, S.: Evaluating probabilistic forecasts with scoringRules, J. Stat. Softw., 90, 1–37,
https://doi.org/10.18637/jss.v090.i12, 2019.
a
Krüger, F., Lerch, S., Thorarinsdottir, T. L., and Gneiting, T.: Predictive inference based on Markov chain Monte Carlo output, Int. Stat. Rev., 89, 215–433,
https://doi.org/10.1111/insr.12405, 2021.
a
Lakatos, M., Lerch, S., Hemri, S., and Baran, S.: Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts, Q. J. Roy. Meteor. Soc., 149, 856–877,
https://doi.org/10.1002/qj.4436, 2023.
a
Lerch, S., Baran, S., Möller, A., Groß, J., Schefzik, R., Hemri, S., and Graeter, M.: Simulation-based comparison of multivariate ensemble post-processing methods, Nonlin. Processes Geophys., 27, 349–371,
https://doi.org/10.5194/npg-27-349-2020, 2020.
a
Pahlavan, R., Moradi, M., Tajbakhsh, S., Azadi, M., and Rahnama, M.: Fog probabilistic forecasting using an ensemble prediction system at six airports in Iran for 10 fog events, Meteorol. Appl., 28, e2033,
https://doi.org/10.1002/met.2033, 2021.
a
Parde, A. N., Ghude, S. D., Dhangar, N. G., Lonkar, P., Wagh, S., Govardhan, G., Biswas, M., and Jenamani, R. K.: Operational probabilistic fog prediction based on ensemble forecast system: A decision support system for fog, Atmosphere, 13, 1608,
https://doi.org/10.3390/atmos13101608, 2022.
a
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174,
https://doi.org/10.1175/MWR2906.1, 2005.
a
Ryerson, W. R. and Hacker, J. P.: A nonparametric ensemble postprocessing approach for short-range visibility predictions in data-sparse areas, Weather Forecast., 33, 835–855,
https://doi.org/10.1175/WAF-D-17-0066.1, 2018.
a
Schultz, B. and Lerch, S.: Machine learning methods for postprocessing ensemble forecasts of wind gusts: a systematic comparison, Mon. Weather Rev., 150, 235–257,
https://doi.org/10.1175/MWR-D-21-0150.1, 2022.
a,
b,
c
Sloughter, J. M., Raftery, A. E., Gneiting, T., and Fraley, C.: Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Mon. Weather Rev., 135, 3209–3220,
https://doi.org/10.1175/MWR3441.1, 2007.
a
Thorarinsdottir, T. L. and Gneiting, T.: Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression, J. R. Stat. Soc. Ser. A Stat. Soc., 173, 371–388,
https://doi.org/10.1111/j.1467-985X.2009.00616.x, 2010.
a
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Ben Boualègue, Z., Bhend, J., Dabernig, M., De Cruz, L., Hieta, L., Mestre, O., Moret, L., Odak Plenkovič, I., Schmeits, M., Taillardat, M., Van den Bergh, J., Van Schaeybroeck, B., Whan, K., and Ylhaisi, J.: Statistical postprocessing for weather forecasts – review, challenges and avenues in a big data world, B. Am. Meteorol. Soc., 102, E681–E699,
https://doi.org/10.1175/BAMS-D-19-0308.1, 2021.
a,
b
Wagh, S., Kulkarni, R., Lonkar, P., Parde, A. N., Dhangar, N. G., Govardhan, G., Sajjan, V., Debnath, S., Gultepe, I., Rajeevan, M., and Ghude, S. D.: Development of visibility equation based on fog microphysical observations and its verification using the WRF model, Model. Earth Syst. Environ., 9, 195–211,
https://doi.org/10.1007/s40808-022-01492-6, 2023.
a
Wilks, D. S.: Univariate ensemble postprocessing, in: Statistical Postprocessing of Ensemble Forecasts, edited by: Vannitsem, S., Wilks, D. S., and Messner, J. W., Elsevier, Amsterdam, 49–89, ISBN: 978-0-12-812372-0, 2018.
a,
b
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, 4th edn., Elsevier, Amsterdam, ISBN 978-0-12-815823-4, 2019.
a,
b,
c,
d,
e,
f
Zhou, B., Du, J., McQueen, J., and Dimego, G.: Ensemble forecast of ceiling, visibility, and fog with NCEP Short-Range Ensemble Forecast system (SREF), Aviation, Range, and Aerospace Meteorology Special Symposium on Weather–Air Traffic Management Integration, Phoenix, AZ, American Meteorological Society, extended abstract 4.5.,
https://ams.confex.com/ams/89annual/techprogram/paper_142255.htm (last access: 12 July 2024), 2009. a
Zhou, B., Du, J., Gultepe, I., and Dimego, G.: Forecast of low visibility and fog from NCEP: Current status and efforts, Pure Appl. Geophys., 169, 895–909,
https://doi.org/10.1007/s00024-011-0327-x, 2012.
a
