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This study investigated ice cloud formation associated with marine bioaerosols over the Southern Ocean (SO) using a combination of cloud particle sensor (CPS) sonde observations, satellite products, reanalysis data, and backward trajectory analysis. The CPS sonde detected ice clouds at temperatures higher than −10°C in the mid‐troposphere near an atmospheric river at high‐latitudes over the SO. Backward trajectory analyses indicated that a mid‐latitude air mass with a high concentration of atmospheric dimethylsulfide (DMS) in the atmospheric boundary layer (<1 km) arrived at the ice cloud formation layer over the high‐latitudes. The DMS in the boundary layer began to increase under high wave conditions, coincident with the highest chlorophyll‐a concentrations in the ocean. These results suggest that bioaerosols emitted from the ocean over the mid‐latitudes acted as ice‐nucleating particles for ice cloud formation over high‐latitudes. Plain Language Summary Polar region clouds play a key role in Earth's climate. Knowledge of the cloud phase (i.e., liquid water, ice, or mixed) is important for determining the surface heat budget because the reflection of solar radiation at the cloud top depends on cloud phase. Although the development of numerical climate models allows the investigation of clouds globally, there still is a cloud phase (water or ice) bias in the models. Therefore, an observational study is required to investigate cloud formation environments. During a cruise in the SO by a research ship, ice clouds were observed at relatively high temperatures in the mid‐troposphere at high‐latitudes. Analysis of the trajectory of the air mass at the ice formation height indicated that the air mass had traveled from the surface in the mid‐latitudes to the mid‐troposphere over high‐latitudes. Biogenic material emitted from the ocean under strong wind and high wave conditions was transferred to the air mass near the surface in the mid‐latitudes and eventually reached the mid‐troposphere at the observation point. These results suggest that marine biogenic material transported from the mid‐latitudes influenced ice cloud formation under relatively high temperatures. Key Points A cloud particle sensor sonde measured ice clouds at air temperatures higher than −10 °C in the mid‐troposphere near an atmospheric river Mid‐latitude air mass with high dimethylsulfide (DMS) in the boundary layer reached the ice cloud formation layer over high‐latitudes Simulated high surface atmospheric DMS was associated with observed high chlorophyll‐a concentration under simulated high wave conditions

Computational analysis of particle-laden-airflow erosion can help engineers have a better understanding of the erosion process, maintenance and protection of turbomachinery components. We present an integrated method for this class of computational analysis. The main components of the method are the residual-based Variational Multiscale (VMS) method, a finite element particle-cloud tracking (PCT) method with ellipsoidal clouds, an erosion model based on two time scales, and the Solid-Extension Mesh Moving Technique (SEMMT). The turbulent-flow nature of the analysis is addressed with the VMS, the particle-cloud trajectories are calculated based on the time-averaged computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the target-geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated based on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the PCT computation. As the target geometry evolves, the mesh is updated with the SEMMT. We present a computation designed to match the sand-erosion experiment we conducted with an aluminum-alloy target. We show that, despite the problem complexities and model assumptions involved, we have a reasonably good agreement between the computed and experimental data.

We present model simulations with the atmospheric chemistry–climate model ECHAM5/MESSy Atmospheric Chemistry (EMAC) nudged toward European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalyses for the Arctic winters 2009/2010 and 2010/2011. This study is the first to perform an extensive assessment of the performance of the EMAC model for Arctic winters as previous studies have only made limited evaluations of EMAC simulations which also were mainly focused on the Antarctic winter stratosphere. We have chosen the two extreme Arctic winters 2009/2010 and 2010/2011 to evaluate the formation of polar stratospheric clouds (PSCs) and the representation of the chemistry and dynamics of the polar winter stratosphere in EMAC. The EMAC simulations are compared to observations by the Michelson Interferometer for Passive Atmospheric Soundings (Envisat/MIPAS) and the observations from the Aura Microwave Limb Sounder (Aura/MLS). The Arctic winter 2010/2011 was one of the coldest stratospheric winters on record, leading to the strongest depletion of ozone measured in the Arctic. The Arctic winter 2009/2010 was, from the climatological perspective, one of the warmest stratospheric winters on record. However, it was distinguished by an exceptionally cold stratosphere (colder than the climatological mean) from mid-December 2009 to mid-January 2010, leading to prolonged PSC formation and existence. Significant denitrification, the removal of HNO3 from the stratosphere by sedimentation of HNO3-containing polar stratospheric cloud particles, occurred in that winter. In our comparison, we focus on PSC formation and denitrification. The comparisons between EMAC simulations and satellite observations show that model and measurements compare well for these two Arctic winters (differences for HNO3 generally within ±20 %) and thus that EMAC nudged toward ECMWF ERA-Interim reanalyses is capable of giving a realistic representation of the evolution of PSCs and associated sequestration of gas-phase HNO3 in the polar winter stratosphere. However, simulated PSC volume densities are smaller than the ones derived from Envisat/MIPAS observations by a factor of 3–7. Further, PSCs in EMAC are not simulated as high up (in altitude) as they are observed. This underestimation of PSC volume density and vertical extension of the PSCs results in an underestimation of the vertical redistribution of HNO3 due to denitrification/re-nitrification. The differences found here between model simulations and observations stipulate further improvements in the EMAC set-up for simulating PSCs.

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe.

Wind-turbine blade rain and sand erosion, over long periods of time, can degrade the aerodynamic performance and therefore the power production. Computational analysis of the erosion can help engineers have a better understanding of the maintenance and protection requirements. We present an integrated method for this class of computational analysis. The main components of the method are the streamline-upwind/Petrov–Galerkin (SUPG) and pressure-stabilizing/Petrov–Galerkin (PSPG) stabilizations, a finite element particle-cloud tracking method, an erosion model based on two time scales, and the solid-extension mesh moving technique (SEMMT). The turbulent-flow nature of the analysis is handled with a Reynolds-averaged Navier–Stokes model and SUPG/PSPG stabilization, the particle-cloud trajectories are calculated based on the computed flow field and closure models defined for the turbulent dispersion of particles, and one-way dependence is assumed between the flow and particle dynamics. Because the geometry update due to the erosion has a very long time scale compared to the fluid–particle dynamics, the update takes place in a sequence of “evolution steps” representing the impact of the erosion. A scale-up factor, calculated in different ways depending on the update threshold criterion, relates the erosions and particle counts in the evolution steps to those in the fluid–particle simulation. As the blade geometry evolves, the mesh is updated with the SEMMT. We present computational analysis of rain and sand erosion for a wind-turbine blade strip, including a case with actual rainfall data and experimental aerodynamic data for eroded airfoil geometries.

We present an atmospheric retrieval analysis of a pair of highly variable, ∼200 Myr old, early T type planetary-mass exoplanet analogs SIMP J01365662+0933473 and 2MASS J21392676+0220226 using the Brewster retrieval framework. Our analysis, which makes use of archival 1–15 μm spectra, finds almost identical atmospheres for both objects. For both targets, we find that the data is best described by a patchy, high-altitude forsterite (Mg2SiO4) cloud above a deeper, optically thick iron (Fe) cloud. Our model constrains the cloud properties well, including the cloud locations and cloud particle sizes. We find that the patchy forsterite slab cloud inferred from our retrieval may be responsible for the spectral behavior of the observed variability. Our retrieved cloud structure is consistent with the atmospheric structure previously inferred from spectroscopic variability measurements, but clarifies this picture significantly. We find consistent C/O ratios for both objects, which supports their formation within the same molecular cloud in the Carina-Near moving group. Finally, we note some differences in the constrained abundances of H2O and CO, which may be caused by data quality and/or astrophysical processes such as auroral activity and their differing rotation rates. The results presented in this work provide a promising preview of the detail with which we will characterize extrasolar atmospheres with JWST, which will yield higher-quality spectra across a wider wavelength range.

Identifying supercooled liquid water (SLW) in clouds is critical for weather modification, aviation safety, and atmospheric radiation calculations. Currently, aircraft identification in the SLW area mostly depends on empirical estimation of cloud particle number concentration (Nc) in China, and scientific verification and quantitative identification criteria are urgently needed. In this study, the observations are from the Fast Cloud Droplets Probe, Rosemount ice detector (RICE), and Cloud Particle Imager (CPI) onboard a King Air aircraft during seven flights in 2018 and 2019 over central and eastern China. Based on this, the correlation among Nc, the proportion of spherical particles (Ps), and the probability of icing (Pi) in supercooled stratiform and cumulus-stratus clouds is statistically analyzed. Subsequently, this study proposes a method to identify SLW areas using Nc in combination with ambient temperature. The reliability of this method is evaluated through the true skill statistics (TSS) and threat score (TS) methods. Numerous airborne observations during the seven flights reveal a strong correlation among Nc, Ps, and Pi at the temperature from 0 to -18°C. When Nc is greater than a certain threshold of 5 cm-3, there is always the SLW, i.e., Pi and Ps are high. Evaluation results demonstrate that the TSS and TS values for Nc = 5 cm-3 are higher than those for Nc < 5 cm-3, and a larger Nc threshold (> 5 cm-3) corresponds to a higher SLW identification hit rate and a higher SLW content. Therefore, Nc = 5 cm-3 can be used as the minimum criterion for identifying the SLW in clouds at temperature lower than 0°C. The SLW identification method proposed in this study is especially helpful in common situations where aircraft are equipped with only Nc probes and without the CPI and RICE.

The Arctic region is sensitive to climate change and is warming faster than the global average. Aerosol particles change cloud properties by acting as cloud condensation nuclei and ice-nucleating particles, thus influencing the Arctic climate system. Therefore, understanding the aerosol particle properties in the Arctic is needed to interpret and simulate their influences on climate. In this study, we collected ambient aerosol particles using whole-air and PM10 inlets and residual particles of cloud droplets and ice crystals from Arctic low-level clouds (typically, all-liquid or mixed-phase clouds) using a counterflow virtual impactor inlet at the Zeppelin Observatory near Ny-Ålesund, Svalbard, within a time frame of 4 years. We measured the composition and mixing state of individual fine-mode particles in 239 samples using transmission electron microscopy. On the basis of their composition, the aerosol and cloud residual particles were classified as mineral dust, sea salt, K-bearing, sulfate, and carbonaceous particles. The number fraction of aerosol particles showed seasonal changes, with sulfate dominating in summer and sea salt increasing in winter. There was no measurable difference in the fractions between ambient aerosol and cloud residual particles collected at ambient temperatures above 0 ∘C. On the other hand, cloud residual samples collected at ambient temperatures below 0 ∘C had several times more sea salt and mineral dust particles and fewer sulfates than ambient aerosol samples, suggesting that sea spray and mineral dust particles may influence the formation of cloud particles in Arctic mixed-phase clouds. We also found that 43 % of mineral dust particles from cloud residual samples were mixed with sea salt, whereas only 18 % of mineral dust particles in ambient aerosol samples were mixed with sea salt. This study highlights the variety in aerosol compositions and mixing states that influence or are influenced by aerosol–cloud interactions in Arctic low-level clouds.

In this paper, we present YunMa, an exoplanet cloud simulation and retrieval package, which enables the study of cloud microphysics and radiative properties in exoplanetary atmospheres. YunMa simulates the vertical distribution and sizes of cloud particles and their corresponding scattering signature in transit spectra. We validated YunMa against results from the literature. When coupled to the TauREx 3 platform, an open Bayesian framework for spectral retrievals, YunMa enables the retrieval of the cloud properties and parameters from transit spectra of exoplanets. The sedimentation efficiency (f sed), which controls the cloud microphysics, is set as a free parameter in retrievals. We assess the retrieval performances of YunMa through 28 instances of a K2-18 b-like atmosphere with different fractions of H2/He and N2, and assuming water clouds. Our results show a substantial improvement in retrieval performances when using YunMa instead of a simple opaque cloud model and highlight the need to include cloud radiative transfer and microphysics to interpret the next-generation data for exoplanet atmospheres. This work also inspires instrumental development for future flagships by demonstrating retrieval performances with different data quality.

We present a new two-dimensional, bin-scheme microphysical model of cloud formation in the atmospheres of hot Jupiters that includes the effects of longitudinal gas and cloud transport. We predict cloud particle size distributions as a function of planetary longitude and atmospheric height for a grid of hot Jupiters with equilibrium temperatures ranging from 1000 to 2100 K. The predicted 2D cloud distributions vary significantly from models that do not consider horizontal cloud transport and we discuss the microphysical and transport timescales that give rise to the differences in 2D versus 1D models. We find that the horizontal advection of cloud particles increases the cloud formation efficiency for nearly all cloud species and homogenizes cloud distributions across the planets in our model grid. In 2D models, certain cloud species are able to be transported and survive on the daysides of hot Jupiters in cases where 1D models would not predict the existence of clouds. We demonstrate that the depletion of condensible gas species varies as a function of longitude and atmospheric height across the planet, which impacts the resultant gas-phase chemistry. Finally, we discuss various model sensitivities including the sensitivity of cloud properties to microphysical parameters, which we find to be substantially less than the sensitivity to the atmospheric thermal structure and horizontal and vertical transport of condensible material.