Understanding Atmospheric Rivers Using Machine Learning

Atmospheric rivers (ARs), often referred to as “rivers in the sky,” are long, narrow, and intense water vapor transport features that play a significant role in climate extremes. The chapter provides a comprehensive overview of ARs, their historical background, formation mechanisms, and characterization in the atmosphere. Tracing the historical evolution of AR research, from foundational studies on “tropospheric rivers” to contemporary satellite-based advancements, it unveils pivotal moments and technological strides. The “seeder-feeder” mechanism emerges as a key player in elucidating heavy precipitation episodes associated with ARs. Meticulous case studies from diverse regions, including Kerala (August 2018), Quebec (May 2017), and Mumbai (July 2005), underscore the real-world consequences of AR-associated floods, emphasizing the importance of robust algorithms and early warning systems. The synthesis of AR impacts, climatology, and interdisciplinary collaborations offers a holistic understanding of their role in triggering extreme weather events. Through compelling narratives and empirical evidence, the chapter unravels the devastating impacts of ARs, providing valuable insights for assessing vulnerabilities, managing risks, and fostering resilience.

The study of ARs has been facilitated by various datasets, including satellite-based observations, in situ observations, and reanalysis data, and thus resulted in several AR identification techniques across the globe. Observing the impact of ARs and the interest of climate communities across the globe, an international collaborative program Atmospheric River Tracking Method Intercomparison Project (ARTMIP) has been launched to develop a holistic framework to assess the impact of various AR identification methods on climatology, hydrology, and extreme events, quantifying disparities and advancing understanding of future AR changes and associated impacts. Global and regional perspectives reveal the diverse and far-reaching influence of ARs, with notable examples including ARs along the North American West Coast, over the Western United States, Southeastern United States, Europe, Southern South America, and Polar Regions. The relationship between ARs and LSCOs (ENSO, MJO, PDO, etc.) can provide valuable insights into the predictability and variability of AR events. The impacts of ARs are multifaceted, encompassing both beneficial and detrimental effects, such as flooding, drought, and extreme precipitation events. As climate change continues to alter the global landscape, the study of ARs will remain a critical component in predicting and mitigating the effects of extreme weather events.

Atmospheric rivers (ARs) are significant features of Earth’s atmospheric circulation, acting as concentrated conduits of moisture transport that influence regional precipitation patterns. This study explores the spatial and temporal distribution of AR events, their impacts on hydrological cycles, and their association with various atmospheric and oceanic processes. Regions prone to AR influence, like the West Coast of North America and parts of Europe, experience high AR frequencies during specific seasons conducive to their formation. While ARs contribute substantially to annual precipitation totals, they also pose challenges such as flooding due to intense rainfall. The intensity of AR events, measured by integrated moisture transport, plays a critical role in determining the severity of associated impacts, particularly in regions vulnerable to heavy rainfall and flooding. Understanding the characteristics and landfall patterns of ARs is essential for effective flood risk assessment, water resource management, and disaster preparedness planning. Integrating meteorological data, hydrological models, and risk assessment frameworks is crucial for developing proactive strategies to mitigate AR-induced flooding impacts and enhance community resilience. Further research is needed to advance our understanding of AR behavior and improve forecasting capabilities for extreme weather events associated with these atmospheric phenomena.

Climate oscillations and atmospheric rivers (ARs) are fundamental components of Earth's climate system, with a profound impact on global weather patterns and regional hydrology. Understanding the intricate interactions between these phenomena is crucial for enhancing climate resilience and managing extreme weather risks. We analyzed the correlations between large-scale climate oscillations (LSCOs) and precipitation extremes potentially influenced by ARs using statistical modeling techniques. We compared the relative influence of LSCOs (ENSO, NAO, AO, and PDO) to identify key drivers in spatiotemporal variability of precipitation extremes. Our analysis revealed significant associations between LSCOs and ARs, particularly in regions like North America and Europe. Notably, the Pacific Decadal Oscillation (PDO) emerged as a key factor influencing extreme precipitation patterns, with seasonal variations capturing these variations more effectively during certain time scales. These findings have important implications for climate forecasting, water resource management, and adaptation strategies. By understanding and leveraging the connections between LSCOs, ARs, and precipitation extremes, we can improve our capacity to forecast and lessen the consequences of climate fluctuations and shifts.

ARs are observed highly correlated with floods in almost all major continents. Robust and accurate forecasting of landfalling ARs at a substantial lead time can help in mitigating harmful impacts of these ARs. ARs, characterized by their long and narrow corridors of concentrated moisture transport, present challenges in accurate prediction and understanding due to their intricate spatiotemporal features. Traditional Numerical Weather Prediction (NWP) models, while foundational, encounter limitations in precisely capturing AR behaviors, especially over extended lead times. Motivated by the capability of Artificial Intelligence (AI) to handle complex datasets and discern intricate patterns, this study delves into exploring possible applications of AI to model AR characteristics without explicitly encoding physical processes. By leveraging AI techniques such as deep neural networks and convolutional architectures, this chapter aims to present AI as a tool to improve the prediction, classification, and tracking of ARs. This paper reviews the potential and challenges associated with AI applications in AR analysis and management, highlighting its pivotal role in enhancing our understanding and preparedness in dealing with these significant meteorological events.