# Log-normal distribution based EMOS models for probabilistic wind speed forecasting

arXiv: https://arxiv.org/abs/1407.3252 · HTML: https://mlsanity.com/paper/1407.3252/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2014.

## Built on by (most cited first, 10 of 10)

- [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)
- [Calibration of wind speed ensemble forecasts for power generation](https://mlsanity.com/paper/2104.14910/) (2021)
- [A two-step machine learning approach to statistical post-processing of weather forecasts for power generation](https://mlsanity.com/paper/2207.07589/) (2022)
- [Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning](https://mlsanity.com/paper/2406.04424/) (2024)
- [Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules](https://mlsanity.com/paper/2407.15900/) (2024)
- [Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration](https://mlsanity.com/paper/2201.12234/) (2022)
- [Dynamic Line Rating using Hyper-local Weather Predictions: A Machine Learning Approach](https://mlsanity.com/paper/2405.12319/) (2024)
- [Scoring rule nets: beyond mean target prediction in multivariate regression](https://mlsanity.com/paper/2409.14456/) (2024)
- [Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts](https://mlsanity.com/paper/2606.08587/) (2026)

## Abstract (arXiv)

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN-GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.