Neural networks for post-processing ensemble weather forecasts (1805.09091v1)

Abstract: Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring pre-specified link functions. In a case study of 2-meter temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark post-processing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical post-processing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the post-processing of numerical weather forecasts in the coming decade.

• The paper demonstrates that neural networks can automatically learn complex nonlinear relationships between diverse predictors and forecast distribution parameters.
• It integrates additional meteorological variables and station-specific embeddings, achieving up to a 29% reduction in the Continuous Ranked Probability Score.
• The approach not only improves forecast accuracy but also enhances model interpretability, paving the way for advanced multivariate and spatiotemporal forecasting.

Neural Networks for Post-Processing Ensemble Weather Forecasts

The paper presented by Rasp and Lerch discusses the application of neural networks to improve post-processing of ensemble weather forecasts. Ensemble weather prediction involves generating multiple forecasts using varied initial conditions and model parameters to capture the inherent uncertainty in atmospheric systems. However, systematic biases and errors in raw ensemble forecasts necessitate effective post-processing methods to yield accurate probabilistic forecasts. Traditional methods, such as distributional regression models like Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), depend heavily on predetermined statistical relationships, requiring manual specification of link functions between predictors and distribution parameters. This paper proposes neural networks as a more flexible and data-driven alternative.

Key Contributions and Methodology

The core contribution of the paper is demonstrating how neural networks can be employed to learn nonlinear relationships between arbitrary predictors and forecast distribution parameters. The authors tested this approach through a case paper involving 2-meter temperature forecasts at surface stations in Germany, spanning the years 2007 to 2016.

Neural networks offer several advantages over traditional methods:

1. Flexibility in Model Design: Unlike EMOS, which requires specific link functions to relate input predictors to distribution parameters, neural networks automatically learn these relationships from the data without prior assumptions.
2. Incorporating Additional Predictors: Neural networks easily integrate additional meteorological variables beyond temperature predictions, enhancing model accuracy while mitigating overfitting risks.
3. Utilizing Station-Specific Information: The use of embedding layers for station-specific data allows for capturing location-dependent forecasting characteristics.

The paper contrasts the neural network approach with traditional methods such as global and local EMOS, EMOS with boosting, and Quantile Regression Forests (QRF). The authors demonstrate the superior performance of neural networks through rigorous evaluations, using Continuous Ranked Probability Score (CRPS) as a loss function and comparative metric to ensure accuracy and calibration of probabilistic forecasts.

Numerical Results and Insights

The research reports significant improvements by neural networks over traditional methods, notably when incorporating auxiliary meteorological predictors and station-specific embeddings. For a single-year training dataset from 2015, the network-based approach (NN-aux-emb) achieved a 29% reduction in CRPS compared to raw ensemble outputs, outperforming both global and local EMOS methods, as well as EMOS with boosting. When using a longer training dataset from 2007 to 2015, these improvements were further pronounced, highlighting the model's ability to harness larger datasets effectively.

Beyond advances in forecasting skill, the paper challenges the common perception of neural networks as opaque models. By leveraging permutation importance techniques, the authors elucidate which meteorological variables had the greatest impact on enhancing forecast accuracy, showcasing the neural network's interpretability.

Implications and Future Considerations

The successful application of neural networks in this context demonstrates their potential to transform probabilistic weather forecasting. Practically, this advancement supports decision-makers in fields sensitive to weather conditions, such as agriculture, energy, and disaster management, by providing more accurate and dependable forecasts.

Theoretically, the work opens several avenues for future research. One potential development is expanding the approach to forecast other weather variables that are traditionally more challenging to model, such as precipitation or wind speed. Further, given the model's adaptability, extending these methods to multivariate settings could enhance cross-variable forecast dependencies and improve spatial and temporal coherence in weather predictions.

Additionally, researchers could explore convolutional or recurrent neural network architectures to incorporate spatiotemporal dependencies directly into the forecasting model. Scaling these methods to operational frameworks, especially considering computational efficiency and robustness, remains a promising research direction.

In conclusion, this paper effectively showcases how neural networks can significantly enhance post-processed ensemble weather forecasts, achieving superior accuracy and interpretability, and paving the way for future advancements in meteorological prediction methodologies.