Atmospheric Aerosols: Origins, Composition, and Optical Properties: Origins, Make-Up, and Optical Characteristics of Atmospheric Aerosols Contributions examining the origins, composition, and light-absorbing properties of atmospheric aerosols are welcome for this collection. We encourage research on source allocation, regional variability, primary and secondary aerosol formation, and the radiative effects of black and brown carbon. Interdisciplinary research that combines field observations, lab tests, and modeling to comprehend the role of aerosols in air quality and climate dynamics is of particular interest.

Analytical Techniques for Environmental and Smoke Aerosol Characterization: Analytical Methods for Characterizing Smoke and Environmental Aerosols Recent developments in analytical techniques for characterizing aerosols related to combustion and the environment are the focus of this collection. Method development or application of GC-MS, HPLC-MS/MS, ICP-MS, HR-ToF-AMS, and other platforms for the analysis of particulate matter, volatile and semi-volatile compounds, and trace elements are examples of submissions. The focus is on innovations in sample preparation, quantification accuracy, and approaches for dealing with complex matrices like smoke, air, and biological samples.

Keywords: Atmospheric Aerosols; Source Allocation; Radiative Effects; Analytical Techniques; Sample Preparation

Precipitation is a fundamental component of the Earth's water cycle, influencing weather patterns, climate variability, ecosystems, agriculture, and water resource management. Understanding the underlying processes of precipitation formation, distribution, and intensity is crucial for addressing challenges such as extreme weather events, flood forecasting, drought mitigation, and climate change adaptation. Traditional approaches relying on observational data and numerical modeling have provided significant insights, yet persistent uncertainties remain due to the complex interactions between atmospheric dynamics, thermodynamics, and microphysical processes.

In recent years, advancements in machine learning (ML) and deep learning (DL) techniques have opened new avenues for enhancing our understanding of precipitation processes. These data-driven methods can integrate vast datasets from satellite observations, ground-based radars, weather stations, and high-resolution models to uncover patterns, improve predictions, and refine parameterizations that are difficult to capture through conventional physics-based approaches alone. This Special Issue aims to bring together cutting-edge research that bridges observational studies, numerical modeling, and ML/DL methodologies to advance the science of precipitation. We seek contributions that explore innovative integrations of these approaches to better characterize precipitation mechanisms, from cloud microphysics to large-scale atmospheric circulation.

By fostering interdisciplinary collaboration, this collection will highlight novel techniques, datasets, and findings that contribute to more accurate forecasting, risk assessment, and policy-making in a changing climate.

We welcome original research articles, review papers, case studies, and methodological advancements related to the following themes (but not limited to):

• Observational analyses of precipitation processes using remote sensing, in-situ measurements, or reanalysis datasets.

• Numerical modeling of precipitation, including improvements in parameterization schemes for convection, cloud-resolving models, and ensemble simulations.

• Applications of ML/DL for precipitation nowcasting, downscaling, bias correction, or pattern recognition in observational and modeled data.

• Hybrid approaches combining physics-based models with ML/DL for enhanced simulation and prediction of extreme precipitation events.

• Studies on the impacts of climate change on precipitation processes using integrated observational-modeling-ML frameworks.

• Validation and uncertainty quantification in precipitation studies across scales, from local to global.

• Societal and environmental implications, such as hydrological modeling, disaster risk reduction, and water resource planning informed by these approaches.