[Paper Review] Deep Learning Techniques in Extreme Weather Events: A Review

Shikha Verma, Kuldeep Srivastava+2 more|arXiv (Cornell University)|Aug 18, 2023

Meteorological Phenomena and Simulations9 citations

TL;DR

This paper surveys the state-of-the-art deep learning approaches for extreme weather events, covering thunderstorm, precipitation, hail, snow, drought, heatwaves, and tropical cyclones, and discusses challenges and future directions.

ABSTRACT

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.

Motivation & Objective

• Motivate the need for accurate forecasting of extreme weather events and mitigate their impacts.
• Survey deep learning architectures used for various extreme weather phenomena (thunderstorms, precipitation, hail, snow, cyclones).
• Highlight data sources, integration approaches, and the potential of hybrid models to improve forecast accuracy.
• Identify current limitations (data, interpretability, scalability) and propose directions for future research in meteorology.

Proposed method

• Review of deep learning models applied to extreme weather domains (thunderstorm, precipitation, hail, snow, cyclones).
• Discussion of data sources (satellites, radars, weather stations) and data assimilation concepts.
• Analysis of model types (CNNs, RNNs, LSTMs, ConvLSTM, U-Net, DeepLab variants) and hybrid approaches.
• Synthesis of challenges, limitations, and potential future directions in deep learning for meteorology.

Experimental results

Research questions

• RQ1 What deep learning techniques have been applied to extreme weather events and with what datasets?
• RQ2 What are the main performance improvements and limitations of these deep learning approaches across different weather phenomena?
• RQ3 How do hybrid models and data fusion influence forecast accuracy for extreme weather events?
• RQ4 What challenges (data quality, interpretability, computational demands) hinder deployment in meteorology?
• RQ5 What future research directions are most promising for advancing deep learning in extreme weather forecasting?

Key findings

• Deep learning enables exploitation of multi-source data (satellite, radar, stations) for enhanced forecasting of extreme events.
• Hybrid models (e.g., EEMD-ANN/SVM, ADSNet) show improvements over traditional ML in certain thunderstorm and lightning tasks.
• DL approaches are applied across a wide range of phenomena: thunderstorms, precipitation, hail, snow, and tropical cyclones, with varying architectures (CNNs, LSTMs, U-Nets, DeepLab).
• Techniques like downscaling, bias correction, and spatiotemporal feature extraction are common to improve resolution and accuracy.
• The review identifies data-related and interpretability challenges that limit real-world deployment and suggests future research avenues.