# Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts

arXiv: https://arxiv.org/abs/2406.14159 · HTML: https://mlsanity.com/paper/2406.14159/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018)
- [Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison](https://mlsanity.com/paper/2106.09512/) (2021)
- [Generative machine learning methods for multivariate ensemble post-processing](https://mlsanity.com/paper/2211.01345/) (2022)
- [Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling](https://mlsanity.com/paper/1302.7149/) (2013)
- [Assessing the calibration of high-dimensional ensemble forecasts using rank histograms](https://mlsanity.com/paper/1310.0236/) (2013)
- [Score-based calibration testing for multivariate forecast distributions](https://mlsanity.com/paper/2211.16362/) (2022)
- [Assessing the calibration of multivariate probabilistic forecasts](https://mlsanity.com/paper/2307.05846/) (2023)
- [Machine learning for total cloud cover prediction](https://mlsanity.com/paper/2001.05948/) (2020)
- [Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging](https://mlsanity.com/paper/1404.3681/) (2014)
- [Parametric model for post-processing visibility ensemble forecasts](https://mlsanity.com/paper/2310.16824/) (2023)
- [Statistical post-processing of visibility ensemble forecasts](https://mlsanity.com/paper/2305.15325/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts](https://mlsanity.com/paper/2509.02784/) (2025)
- [Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts](https://mlsanity.com/paper/2609.07512/) (2026)

## Abstract (arXiv)

In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility - a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable falls short of the predictive accuracy achieved for other quantities issued by meteorological centers. Therefore, statistical post-processing is recommended to enhance the reliability and accuracy of predictions. By estimating the predictive distributions of the variables with the aid of historical observations and forecasts, one can achieve statistical consistency between true observations and ensemble predictions. Visibility observations, following the recommendation of the World Meteorological Organization, are typically reported in discrete values; hence, the predictive distribution of the weather quantity takes the form of a discrete parametric law. Recent studies demonstrated that the application of classification algorithms can successfully improve the skill of such discrete forecasts; however, a frequently emerging issue is that certain spatial and/or temporal dependencies could be lost between marginals. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts for 30 locations in Central Europe, we investigate whether the inclusion of Copernicus Atmosphere Monitoring Service (CAMS) predictions of the same weather quantity as an additional covariate could enhance the skill of the post-processing methods and whether it contributes to the successful integration of spatial dependence between marginals. Our study confirms that post-processed forecasts are substantially superior to raw and climatological predictions, and the utilization of CAMS forecasts provides a further significant enhancement both in the univariate and multivariate setup.