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Abstract We present the first Spectropolarimeter for Planetary EXploration ‐ one (SPEXone) aerosol retrieval results over land and ocean using the Remote sensing of Trace gas and Aerosol Products algorithm, covering the period 23 February–31 August 2024. We validate the retrieved Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and Single Scattering Albedo (SSA) with AErosol RObotic NETwork (AERONET) data. The validation results show that SPEXone provides products of good quality, with comparable performance over land and ocean. For AOD, the Root‐Mean‐Square Error (RMSE) is 0.053 over land and 0.043 over ocean, while respectively 77% and 75% of the retrievals are within the requirement formulated by the Global Climate Observing System (GCOS). For AE, the RMSE is 0.25 over land and 0.26 over ocean. For SSA, the RMSE is 0.036 over land and 0.036 over ocean, with respectively 69% and 69.4% within the GCOS requirement. After a gap of more than 10 years, this is the first new global Multi‐Angle Polarimeter aerosol dataset.

Abstract A better understanding of the many interacting processes governing the evaluation of ice in natural clouds is required to improve the representation of ice clouds in global circulation models. Recent studies suggest a dominant role of vapor growth processes in determining the temperature dependence of cloud top ice sizes and shapes. Using airborne cloud remote sensing along with reanalysis data, here we show that observed cloud top ice effective radii and estimated normalized growth rates at cloud top highly correlate with an approximately linear relationship, which is consistent with a conceptual model also presented. Furthermore, significant differences in crystal shape characteristics and scattering asymmetry parameters are found between sub‐ and super‐saturated cloud tops over ocean, although not over land. These results provide valuable observational targets for studying ice formation and evolution processes using models, while also helping interpretation of satellite observations of ice microphysical properties at cloud tops.

Abstract In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.