What is the Machine learning in weather forecasting?

Introduction

Machine learning (ML) has emerged as a powerful tool in weather forecasting, offering potential improvements in prediction accuracy and computational efficiency. This synthesis explores the various applications and benefits of ML in weather forecasting, drawing insights from multiple research papers.

Key Insights

Improving Forecast Accuracy and Efficiency:
- ML models, such as deep learning and artificial neural networks, can predict weather conditions by analyzing historical data, often outperforming traditional methods in terms of computational efficiency and accuracy .
- ML emulators for parameterization schemes in weather forecasting systems can produce stable and accurate results, especially for medium-range forecasts, and can operate significantly faster on GPU hardware compared to traditional CPU-based schemes.
Uncertainty Estimation:
- ML techniques, particularly convolutional neural networks, can estimate the uncertainty of weather forecasts by analyzing past weather data, providing a computationally efficient alternative to ensemble numerical weather simulations.
Probabilistic Forecasting and Hybrid Approaches:
- There is a growing emphasis on shifting towards probabilistic forecasting, which focuses on assessing uncertainties reliably. Combining physics-based models with ML approaches, known as "gray box" models, is seen as a promising direction for improving forecast reliability.
Global and Regional Forecast Models:
- ML-based global atmospheric forecast models, such as those using reservoir computing, can predict realistic atmospheric state variables and outperform traditional models in certain regions and timeframes.
- Deep learning techniques, including RNN, CRBM, and CN models, have been shown to effectively forecast weather by uncovering hidden patterns in large datasets, contributing to various application domains like agriculture and tourism.
Applications in Specific Meteorological Fields:
- ML methods are widely applied in forecasting specific meteorological fields such as wind, precipitation, temperature, pressure, and radiation. These methods include Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost.
Interpretable Machine Learning:
- Interpretable ML models are being developed to not only predict weather but also to provide insights into the relationships between different meteorological variables, enhancing the understanding of weather patterns.

Conclusion

Machine learning is revolutionizing weather forecasting by improving prediction accuracy, computational efficiency, and the ability to estimate uncertainties. The integration of ML with traditional physics-based models, the development of global and regional forecast models, and the application of interpretable ML techniques are key trends driving advancements in this field. As ML methods continue to evolve, they are expected to play an increasingly vital role in weather prediction and climate analysis.

What is the Machine learning in weather forecasting?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Predicting weather forecast uncertainty with machine learning

Machine learning techniques can efficiently predict weather forecast uncertainty from past forecasts, outperforming other methods without numerical forecasts.

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Machine learning can revolutionize Space Weather forecasting, but requires a shift to probabilistic approaches and gray-box techniques for reliable uncertainty assessment.

Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting

Machine learning can improve weather forecasting accuracy by training emulators that simulate gravity wave drag, with potential benefits for medium-range predictions and faster performance on GPU hardware.

A Machine Learning‐Based Global Atmospheric Forecast Model

Machine learning models outperform climatology and persistence in midlatitudes, but not in the tropics, and perform better than numerical models for state variables affected by parameterized processes.

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Machine learning methods will be a key feature in future weather forecasting, with topics like photovoltaic and wind energy, atmospheric physics, and climate change being key areas of interest.

Weather Forecasting using Machine Learning

Machine learning algorithms can improve weather forecasting by addressing multiple collinearity and outliers in historic data.

A Comprehensive Study on Weather Forecasting using Machine Learning

Machine learning techniques, such as linear regression and artificial neural networks, can effectively predict weather variations, aiding outdoor activities and protecting assets and lives.

Smart Weather Forecasting using Machine Learning Approach

Machine learning, using the Time series algorithm with SARIMAX, can improve weather forecasting accuracy by analyzing both structured and unstructured data.

Interpretable Machine Learning for Meteorological Data

Interpretable machine learning can enhance weather forecasting by explaining characteristic variables and domain relationships in meteorological data.

Weather forecasting using deep learning techniques

Deep learning techniques, such as Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN), can improve weather forecasting accuracy for various applications, including flight navigation, agriculture, and tourism.

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