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Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.
Rational decisions
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Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
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Using Bayesian model averaging to calibrate forecast ensembles
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Quantile regression forests
Meinshausen, N. (2006) · 2006
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Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007) · 2007
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Gneiting, T. and Raftery, A. E. (2007) · 2007
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Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression
Thorarinsdottir, T. L. and Gneiting, T. (2010) · 2010
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Wilks, D. S. (2011) · 2011
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Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012) · 2012
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Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters
Wang, F., Mi, Z., Su, S. and Zhao, H. (2012) · 2012
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Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning
Chu, Y., Pedro, H. T. C. and Coimbra, C. F. M. (2013) · 2013
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Kahle, D. and Wickham, H. (2013) · 2013
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Comparison of non-homogeneous regression models for probabilistic wind speed forecasting
Lerch, S. and Thorarinsdottir, T. L. (2013) · 2013
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Distribution to distribution regression
Oliva, J., Póczos, B. and Schneider, J. (2013) · 2013
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Uncertainty quantification in complex simulation models using ensemble copula coupling
Schefzik, R., Thorarinsdottir, T. L. and Gneiting, T. (2013) · 2013
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Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts
Gagne, D. J., McGovern, A. and Xue, M. (2014) · 2014
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Trends in the predictive performance of raw ensemble weather forecasts
Hemri, S., Scheuerer, M., Pappenberger, F., Bogner, K. and Haiden, T. (2014) · 2014
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Application of deep convolutional neural networks for detecting extreme weather in climate datasets
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Heteroscedastic censored and truncated regression with crch
Messner, J. W., Mayr, G. J. and Zeileis, A. (2016) · 2016
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Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics
Taillardat, M., Mestre, O., Zamo, M. and Naveau, P. (2016) · 2016
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“The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it
Wilks, D. S. (2016) · 2016
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Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles
Gagne, D. J., McGovern, A., Haupt, S. E., Sobash, R. A., Williams, J. K. and Xue, M. (2017) · 2017
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Messner, J. W., Mayr, G. J., Wilks, D. S. and Zeileis, A. (2014) · 2014
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Probabilistic quantitative precipitation forecasting using ensemble model output statistics
Scheuerer, M. (2014) · 2014
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Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting
Baran, S. and Lerch, S. (2015) · 2015
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The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A. and Brunet, G. (2015) · 2015
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Analog-based ensemble model output statistics
Junk, C., Delle Monache, L. and Alessandrini, S. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y. and Hinton, G. (2015) · 2015
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Neural Networks and Deep Learning
Nielsen, M. A. (2015) · 2015
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Deep learning for computational chemistry
Goh, G. B., Hodas, N. O. and Vishnu, A. (2017) · 2017
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Evaluating probabilistic forecasts with scoringRules
Jordan, A., Krüger, F. and Lerch, S. (2017) · 2017
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Machine learning for real-time prediction of damaging straight-line convective wind
Lagerquist, R., McGovern, A. and Smith, T. (2017) · 2017
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Similarity-based semilocal estimation of post-processing models
Lerch, S. and Baran, S. (2017) · 2017
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quantregForest: Quantile Regression Forests
Meinshausen, N. (2017) · 2017
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Nonhomogeneous boosting for predictor selection in ensemble postprocessing
Messner, J. W., Mayr, G. J. and Zeileis, A. (2017) · 2017
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Python Software, Version 3.6.4
Python Software Foundation (2017) · 2017
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R: A Language and Environment for Statistical Computing
R Core Team (2017) · 2017
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Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting
Taillardat, M., Fougères, A.-L., Naveau, P. and Mestre, O. (2017) · 2017
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Combining predictive distributions for the statistical post-processing of ensemble forecasts
Baran, S. and Lerch, S. (2018) · 2018
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