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There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INPs affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm -3 and INP concentrations from 0.001 to 10 L -1. Central to our findings is the observation that higher INP concentrations yield smaller ice particles, while an increase in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight oblate-like crystals as dominant between temperatures of −20 and −16 °C. Notably, high-INP scenarios unveiled a significant prevalence of irregular polycrystals. The aspect ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from -11.83 dBZ (low INP, 0.001 L -1) to 4.65 dBZ (high INP, 10 L -1), elucidate the complex dynamics between CCN and INPs in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections. [ABSTRACT FROM AUTHOR]