Titlebook: Understanding Atmospheric Rivers Using Machine Learning; Manish Kumar Goyal,Shivam Singh Book 2024 The Author(s), under exclusive license

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Manish Kumar Goyal,Shivam Singhality of life for millions of individuals. While drug discovery of biotherapeutics and biosimilars was originally dominated by small biotechnology companies, today nearly every major pharmaceutical company in the world is engaged in this effort. Biotherapeutic programs present unique challenges to d

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Manish Kumar Goyal,Shivam Singhions and recommendations that pertain to chemical substances. The RCD? is designed to be the first reference book to consult when beginning compliance efforts. Every regulatory or advisory list used in the RCD? is keyed to its source, to help readers who need more detailed information on regulations

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Understanding Atmospheric Rivers and Exploring Their Role as Climate Extremes,nt 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-

NIL

Characterization and Impacts of Atmospheric Rivers, 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

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Key Characteristics of Atmospheric Rivers and Associated Precipitation,ence 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 E

Desert

Major Large-Scale Climate Oscillations and Their Interactions with Atmospheric Rivers,rns 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

GROWL

Role of Machine Learning in Understanding and Managing Atmospheric Rivers,d 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 (