11 citations as recorded by crossref.

1. Extended-Range Probabilistic Fire-Weather Forecasting Based on Ensemble Model Output Statistics and Ensemble Copula Coupling R. Worsnop et al. https://doi.org/10.1175/MWR-D-19-0217.1
2. Stratified rank histograms for ensemble forecast verification under serial dependence J. Bröcker & Z. Ben Bouallègue https://doi.org/10.1002/qj.3778
3. Three recommendations for evaluating climate predictions T. Fricker et al. https://doi.org/10.1002/met.1409
4. Are we misdiagnosing ensemble forecast reliability? On the insufficiency of spread–error and rank‐based reliability metrics A. Dirkson & M. Buehner https://doi.org/10.1002/qj.70186
5. Uniform calibration tests for forecasting systems with small lead time J. Bröcker https://doi.org/10.1007/s11222-022-10144-9
6. Quantifying driving ensemble influence on operational convection‐permitting ensemble spread A. Gainford et al. https://doi.org/10.1002/qj.70125
7. Gradient‐Boosted Mixture Regression Models for Postprocessing Ensemble Weather Forecasts D. Jobst https://doi.org/10.1002/env.70145
8. Fair scores for ensemble forecasts C. Ferro https://doi.org/10.1002/qj.2270
9. Evaluating ensemble post‐processing for wind power forecasts K. Phipps et al. https://doi.org/10.1002/we.2736
10. Improving sub-seasonal hydrological forecasts utilizing the randomness in Deep Learning models K. Bogner & R. Padrón https://doi.org/10.1007/s00477-025-03138-2
11. Verification of AI–based environmental forecasting systems: What can we do, what do we need to do, and what are the challenges? J. Bröcker et al. https://doi.org/10.1016/j.jemets.2026.100032