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Cloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano‐ and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm‐rain bulk schemes using high‐fidelity super‐droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super‐droplet simulations. We demonstrate the framework's effectiveness by calibrating a single‐moment bulk scheme, resulting in a reduction of data‐model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling. Plain Language Summary: Cloud microphysics is a complex set of processes that determine the formation and evolution of particles in clouds, which affects the Earth's climate by regulating precipitation and cloud cover. However, the vast difference in scale between the microphysics and large‐scale atmospheric flows makes it impossible to simulate these processes in climate models directly. Instead, climate models use simplified methods to represent cloud microphysics, which can result in inaccuracies. In this study, we focus on calibrating the simplified models with more detailed simulations of cloud microphysics using the super‐droplet method. We demonstrate a framework for calibrating the simplified models using high‐fidelity simulations, which improves the accuracy of these models. Key Points: A calibration framework for warm‐rain bulk microphysics parameterizations is presentedThe framework relies on a library of super‐droplet simulations of a rain shaftCalibrating a single‐moment microphysics scheme with the calibration framework substantially reduces the model‐data mismatch [ABSTRACT FROM AUTHOR]

A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called "superdroplet" method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisionalcoalescence scheme and samples from a fragment-size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment-size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. This representation of collisional breakup is able to produce a stationary particle-size distribution, in which breakup and coalescence rates are approximately equal, and it recovers expected behavior such as precipitation suppression in the column model. Furthermore, representing breakup has potential benefits that extend beyond warm rain processes, such as the ability to capture mechanisms of secondary ice production in the superdroplet method. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, 'PySDM', which will facilitate future research using particle-based microphysics. [ABSTRACT FROM AUTHOR]

A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called 'superdroplet' method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisional-coalescence scheme and samples from a fragment-size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment-size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. This representation of collisional breakup is able to produce a stationary particle-size distribution, in which breakup and coalescence rates are approximately equal, and it recovers expected behavior such as precipitation suppression in the column model. Furthermore, representing breakup has potential benefits that extend beyond warm rain processes, such as the ability to capture mechanisms of secondary ice production in the superdroplet method. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, 'PySDM', which will facilitate future research using particle-based microphysics. [ABSTRACT FROM AUTHOR]

This paper introduces a new scheme available in the library of algorithms for representing cloud microphysics in numerical models named libcloudph++. The scheme extends the Lagrangian microphysics scheme available in libcloudph++ to the aqueous phase chemical processes occurring within cloud droplets. The representation of chemical processes focuses on the aqueous phase oxidation of the dissolved SO2 by O3 and H2O2. The Lagrangian Microphysics and Chemistry (LMC) scheme allows tracking the changes in the cloud condensation nuclei (CCN) distribution caused by both collisions between cloud droplets and aqueous phase oxidation. The scheme is implemented in C++ and equipped with bindings to Python which allow reusing the created scheme from models implemented in other programming languages. The scheme can be used on either CPU or GPU, and is distributed under the GPL3 license. Here, the LMC scheme is tested in a simple 0-dimensional adiabatic parcel model and then used in a 2-dimensional prescribed flow framework. The results are discussed with the focus on changes to the CCN sizes and compared with other model simulations discussed in the literature. [ABSTRACT FROM AUTHOR]