The original data is available at dx.doi.org/10.35097/EOv.... We also provide code for matching forecasts and observations, and for an exemplary comparions of ML-based post-processing models.

Forecast are available for 55 meteorological variables mapped to station locations and spatially aggregated forecasts from surrounding grid points, for NWP models initialized at 00 and 12 UTC, in hourly lead times up to 21h. Observations of 6 variables are available at 170 stations.

All details are available in the preprint at arxiv.org/abs/2506.03744. Python code is available at github.com/tobiasbieger....

The potential CRPS of the HRES forecast aligns well with the CRPS of the operational IFS ensemble.

AIWP models show skillful forecasts for lead times of up to 10 days when compared to the ERA5 climatology in terms of the potential CRPS.

Results on WeatherBench 2 data confirm fast-paced progress, with AIWP models, in particular GraphCast, showing improvements in the potential CRPS over the HRES model

Step 2: We then compute the CRPS on the test dataset. The resulting "potential CRPS" quantifies potential probabilistic predictive performance and serves as a proxy for the mean CRPS of real-time, operational
probabilistic products.

We propose a new measure for fair and meaningful comparisons of deterministic AIWP and NWP models:

Step 1: We subject the deterministic backbone of AIWP and NWP models post hoc to the same
postprocessing technique (isotonic distributional regression) on the test dataset.

There has been fast-paced progress in AI-based weather prediction. However, fair comparisons to physics-based NWP models are challenging:
- AI models are trained on the MSE, and might have an advantage in MSE-based comparison
- Comparisons may use different ground truth data (ERA5 vs IFS analysis)

In addition to forecast evaluation via proper scoring rules, we also evaluate the forecasts from an economic perspective by considering trading strategies that utilize the multivariate probabilistic information.

We propose a generative ML model for multivariate, probabilistic forecasting of time series of electricity prices, and compare to state-of-the-art statistical benchmark models.

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All details and links to all datasets are available in the paper (rdcu.be/d462L). Code is available at github.com/HoratN/pp-mo....

We further compare the post-processing approaches to a NN-based direct forecasting model, which predicts PV power based on the weather inputs without the intermediate conversion via the model chain, and achieves almost the same performance.

Applying post-processing to the PV power predictions obtained as the output of the model chain is the most important contributor to improving the forecasts, whereas the effects of post-processing the weather inputs are negligible.

In a case study on a benchmark dataset from the Jacumba solar plant in the US, we find that post-processing generally improves the GHI and PV power forecasts. Neural network-based methods achieve slightly better performance than statistical approaches.

We investigate the use of post-processing and ML in model chain approaches, where different strategies are possible: Post-processing only the weather inputs, post-processing only the PV power predictions, or applying post-processing in both steps (or none at all).

Probabilistic PV power forecasts are often based on model chain approaches, where conversion models estimate PV generation based on weather predictions. However, weather prediction models make systematic errors and require post-processing.