Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
Evaluating probabilistic forecasts with scoringrules.Journal of Statistical Software, 90(12):1–37
3 Pith papers cite this work. Polarity classification is still indexing.
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Time VQ-VAE models generate daily wind vector series that reproduce diurnal volatility patterns but fail to match the distribution of extreme wind speeds.
Raw IFS forecasts outperform raw AIFS for wind speed at all horizons, but post-processing with EMOS or QR reduces the gap, leaving IFS ahead mainly at short leads.
citing papers explorer
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Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
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Stochastic weather generators for high-frequency wind vector time series
Time VQ-VAE models generate daily wind vector series that reproduce diurnal volatility patterns but fail to match the distribution of extreme wind speeds.
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AI and physics-based weather forecasting: A comparative study
Raw IFS forecasts outperform raw AIFS for wind speed at all horizons, but post-processing with EMOS or QR reduces the gap, leaving IFS ahead mainly at short leads.