Precipitation processes are among the most complex manifestations of the Earth’s climate system. As such, they are difficult to observe, model, and predict. Properly characterizing precipitation and its variability is a crucial task to improve water resources management, monitoring and prediction of extreme weather events, climate and hydrologic modeling, and infrastructure design.
In recent years, affordable computational resources and ever-evolving open source software solutions have resulted in a rapid expansion of advanced statistical techniques, including artificial intelligence (AI)-based methods, throughout the field. Such approaches have the potential to meet increasing requirements for a wide range of hydrometeorological products. Yet, our ability to accurately monitor, understand, and model precipitation processes is limited.
This Research Topic welcomes contributions on precipitation studies seeking physical implications, model interpretations, visualizations, remote sensing applications, and uncertainty analyses that can identify novel elements in our understanding of precipitation processes. In this Research Topic, we collate studies that link the information content of observational and/or modeled datasets to the physical causes and materializations of storm properties. These include, but are not limited to, studies on radiative-, hydrometeor-, cloud-, and hydrological-responses to precipitation processes.
Furthermore, this collection welcomes contributions that use novel methods, including statistical approaches, AI, machine learning algorithms, to quantify and/or model precipitation and its variability across scales, in current and future climate scenarios.
Contributions focusing on one or more of the following topics are welcome:
• Assessment and modeling of precipitation variability, including extremes, across multiple spatiotemporal scales and utilizing products from different sources (remote sensing, models, in-situ observations);
• Addressing uncertainty of precipitation products/retrievals
• Analysis and/or modeling of precipitation non-stationarity;
• Precipitation studies based on innovative statistical methods, including deep learning;
• Use of climate model outputs and reanalysis products to improve precipitation stochastic models.
Precipitation processes are among the most complex manifestations of the Earth’s climate system. As such, they are difficult to observe, model, and predict. Properly characterizing precipitation and its variability is a crucial task to improve water resources management, monitoring and prediction of extreme weather events, climate and hydrologic modeling, and infrastructure design.
In recent years, affordable computational resources and ever-evolving open source software solutions have resulted in a rapid expansion of advanced statistical techniques, including artificial intelligence (AI)-based methods, throughout the field. Such approaches have the potential to meet increasing requirements for a wide range of hydrometeorological products. Yet, our ability to accurately monitor, understand, and model precipitation processes is limited.
This Research Topic welcomes contributions on precipitation studies seeking physical implications, model interpretations, visualizations, remote sensing applications, and uncertainty analyses that can identify novel elements in our understanding of precipitation processes. In this Research Topic, we collate studies that link the information content of observational and/or modeled datasets to the physical causes and materializations of storm properties. These include, but are not limited to, studies on radiative-, hydrometeor-, cloud-, and hydrological-responses to precipitation processes.
Furthermore, this collection welcomes contributions that use novel methods, including statistical approaches, AI, machine learning algorithms, to quantify and/or model precipitation and its variability across scales, in current and future climate scenarios.
Contributions focusing on one or more of the following topics are welcome:
• Assessment and modeling of precipitation variability, including extremes, across multiple spatiotemporal scales and utilizing products from different sources (remote sensing, models, in-situ observations);
• Addressing uncertainty of precipitation products/retrievals
• Analysis and/or modeling of precipitation non-stationarity;
• Precipitation studies based on innovative statistical methods, including deep learning;
• Use of climate model outputs and reanalysis products to improve precipitation stochastic models.
