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A long-standing and pervasive problem in climate modeling is the proper representation of cloud albedo over the Southern Ocean (SO). In this study, we investigate the causes of SO cloud albedo biases using phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations and a cloud-controlling factor approach on daily time scales. Cloud albedo, computed from upwelling and downwelling shortwave radiation at the surface and top of the atmosphere, is averaged into bins of vertical velocity, surface wind, and sea surface temperature. The performance of 15 models in both atmosphere-only and ocean-coupled configurations is evaluated against Clouds and the Earth's Radiant Energy System (CERES) satellite retrievals in combination with ERA5 reanalysis for the 2000–14 period. We find that the SO cloud biases maximize in the 50°–65° oceanic band and that models tend to underestimate SO cloud reflectivity for cold conditions and weak surface winds. In turn, descent (ascent) conditions are consistently underestimated (overestimated) across models for both hemispheres in the same latitude band. With a very similar approach, we evaluate how models represent the observed cloud albedo hemispheric asymmetry over oceans, which also maximizes in the 50°–65° band. The sign of the asymmetry is consistently predicted by all models, many of which also predict a similar magnitude to observations. However, this is also a consequence of compensating global biases as individually most models tend to either overpredict or underpredict cloud albedo in both hemispheres. We propose that surface temperatures less than 4°C are important in explaining SO bias in cloud albedo and that they also partly explain the observed hemispheric asymmetry. Further, we hypothesize that the 4°C threshold sets a hemispheric asymmetry in cloud phase. [ABSTRACT FROM AUTHOR]

This study improved the first lightning flash (FLF) warning method of isolated thunderstorm cells in the warm season over South China. The S-band polarimetric radar and three independent lightning location systems provided 57 thunderstorm and 39 nonthunderstorm cells. The results indicated the median value of horizontal reflectivity below the melting layer when the cloud detected by the radar first was a good indicator for forecasting the FLF with the longest lead time (threshold value ≤ 15 dBZ in this study). The algorithm produced 0.96–1 probability of detection (POD), 0.32–0.38 false alarm ratio (FAR), 0.63–0.66 critical skill index (CSI), and 12–21-min average lead time. Methods of minimum echo (20 dBZ) top height extending to −10°C and the 35 dBZ at −10°C were more accurately for warning the FLF but with a shorter lead time; the former algorithm resulted in 1 POD, 0.08–0.29 FAR, 0.71–0.92 CSI, and 5–9-min average lead time. The latter algorithm resulted in 1 POD, 0–0.06 FAR, 0.94–1 CSI, and 3–5.5-min average lead time. The fluctuation of lead time depended on the FLF occurrence time. The differential reflectivity ZDR column and the height of graupel were not as effective as the reflectivity information on warning the FLF in this study. The results are expected to improve the FLF forecasting in considering accuracy and lead time within warm-season thunderstorm cells over South China based on the radar. Significance Statement: It is difficult to forecast the first lightning flash (FLF) occurrence. While many methods are provided based on weather radars, the threshold values for hitting the start of lightning activity are dominated by mixed-phase ice microphysics, especially associated with graupel particles; therefore, to improve the lead time of the FLF nowcasting, usage of knowledge of warm-phase microphysics is possible. Fortunately, the latest discovery indicates the difference is recognizable in warm-phase microphysics during the first radar echo between thunderstorms and nonthunderstorms based on the weather radar. This study utilized the finding to construct a method for the FLF warning. The early lead time and accuracy are considered comprehensively in this method. It is important for the FLF warning. [ABSTRACT FROM AUTHOR]

The effective radius of cloud droplets ( r eff ), representing the ratio of the third to the second moment of the droplet size distribution, is a pivotal factor in inferring the cloud optical properties, such as cloud optical thickness, and single scattering albedo, and consequently plays a vital role in the calculation of radiative properties. Effective radius holds significance not only in assessing the global radiation budget but also in incorporating the aerosol indirect effect within climate models. Hence, refining the parameterization of cloud droplet’s effective radius in earth system models (ESMs) has emerged as a critical endeavor for assessing climate change. Understanding the relationship between the gradient (k) or effective radius ratio (β ), a dimensionless parameter, and mean diameter and relative dispersion (ϵ ) is essential for the parameterization of effective radius. Fundamental theoretical studies using the Eulerian–Lagrangian particle-based model, guided by airborne observations, yield a spectrum of effective radius ratios (β ) aimed at improving the parameterization of r eff . Notably, β or the gradient (k) exhibit significant variations across shallow and convective clouds, with k ranging from 0.61 to 0.84 (mean of 0.72 at a relative dispersion of 0.35). The analysis of CMIP6 models indicate that adopting appropriate β value in the effective radius parameterization is crucial to mitigate the bias in r eff . Thus, better parameterization of the cloud effective radius remains essential for elucidating the radiative impacts in ESMs. [ABSTRACT FROM AUTHOR]

Precipitation efficiency (PE) relates cloud condensation to precipitation and thus reflects how much of the total atmospheric condensate reaches the surface as precipitation. Because the PE in convective storms is directly linked to their updraft and downdraft dynamics, it is a helpful metric to identify convective processes that influence precipitation. However, km‐scale model simulations do not properly resolve convective processes such as individual updrafts and entrainment, which raises the question if such simulations can accurately represent PE. Here, we present two methods to derive PE from standard model output because condensation is usually not available as an output variable. The first method estimates PE from the state variables vertical velocity, temperature, and pressure, whereas the second method estimates PE from ice water path (IWP) and precipitation. We validate the proposed methods with the explicitly calculated PE using a set of idealized Weather Research and Forecast model simulations of organized midlatitude convective storms at different horizontal grid spacings. We show that PE can be reliably estimated from state variables with an error of less than 5%, partly due to error cancellation effects. Additionally, PE can be simulated by km‐scale models within ∼15% accuracy compared to large‐eddy simulations (LESs). The IWP method is slightly less accurate with a stronger grid spacing dependency of the error, but since it is based on observable quantities, it allows for a validation of simulated PE with satellite observations. Finally, we analyze the grid spacing dependency of the climate change signal of PE and find that future decreases in PE in LESs are robustly captured by km‐scale models. Plain Language Summary: Precipitation efficiency (PE) describes how much of the liquid and ice water in clouds reaches the surface in the form of precipitation. PE can help to better understand the physical processes of large storm systems which is crucial to identify what influences the surface precipitation produced by these storms. Here, we present two new methods to calculate PE based on weather and climate model output data. We use simplified simulations of large storm systems to validate the proposed methods and to study how PE and related physical processes depend on the spatial resolution of the model. The key findings are that both methods work well to estimate PE and that storm simulations with a spatial resolution of a few kilometers, which is the frontier for regional climate model simulations, show similar PEs compared to higher resolution simulations. Additionally, we study how PE changes with global warming and find that there will be a future decrease in PE, implying that condensation changes at a higher rate than surface precipitation. However, this result needs to be confirmed by more complex climate model simulations, and the proposed methods in this paper can help future studies to robustly quantify the effects of climate change on PE. Key Points: Precipitation efficiency (PE) can be estimated from vertical velocity, pressure, and temperature or from the observable quantities ice water path and precipitation with a marginal error of less than 5% for km‐scale modelsA 4 km grid spacing model captures PE within 15% accuracy of a 250 m model, partly due to error cancellation effectsKm‐scale and large‐eddy simulations agree on a decrease in PE with climate warming due to a larger increase in condensation compared to precipitation [ABSTRACT FROM AUTHOR]

This study proposes a methodology for analyzing the precipitation process in mixed‐phase clouds using multisensor satellite data, including radar, lidar, and imager. By leveraging a specific multivariate statistic, we elucidate the vertical microphysical structures of mixed‐phase clouds and their transitions associated with cloud particle growth and phase change. Expanding upon previous warm rain process diagnostics, we integrate cloud thermodynamic phase information from lidar and imager, representing the phase near the cloud top and column, respectively, to classify the vertical microphysical structures obtained from radar. Our global composite analysis reveals a systematic transition from non‐precipitating to precipitating characteristics with increasing ice phase fraction of the cloud column, rather than near the cloud top, and increasing cloud‐top particle size. These findings offer valuable observational references for evaluating numerical models in precipitation physics. Plain Language Summary: To ensure a robust assessment of precipitation physics within global climate and numerical weather prediction models, it is imperative to diagnose the precipitation process using satellite observations on a global scale. Here, we propose a new methodology to address this requirement using multisensor satellite measurements to extend our previously developed method for warm liquid‐phase rain into more general ice‐containing mixed‐phase precipitation. For this purpose, satellite‐based information on the cloud thermodynamic phase obtained from the lidar and imager was exploited to statistically classify the vertical profile characteristics of precipitation observed by radar. The results showed that precipitation tended to occur more efficiently with an increasing ice‐phase fraction of the cloud‐column and the cloud‐top particle size. The statistics derived from observations provide a benchmark for evaluating model precipitation physics, facilitating process‐oriented assessments of numerical models. Key Points: The mixed‐phase precipitation was diagnosed using a combination of multisensor satellite measurements by radar, lidar, and imagerMultivariate statistics were constructed to display the radar reflectivity classified by cloud thermodynamic phase and cloud particle sizeThe statistics show more precipitating character with higher ice‐phase fraction of the cloud optical thickness and cloud‐top particle size [ABSTRACT FROM AUTHOR]

This study quantified the relative impact of microphysics parameterization (MP) and cumulus parameterization (CP) schemes on storm prediction using a non‐hydrostatic weather research and forecasting model for an intense Medicane: IANOS. All the simulations agreed with the main observed characteristics of this storm, with some discrepancies in magnitude and location, which vary with CP and MP schemes. These discrepancies were larger during the mature phase of the storm. In the CP‐on simulations, the non‐scale aware Kain–Fritsch (KF) scheme typically resulted in higher precipitation, while the Betts‐Miller‐Janjic (BMJ) scheme led to lower precipitation compared to the CP‐off simulations (C0). The tendency of the KF scheme to consume available convective potential energy at a relatively high rate (i.e., short time scale) resulted in high mass fluxes and latent heat release, leading to strengthened convective activity and, hence, precipitation. The multi‐scale aware (MSKF) substantially reduces the precipitation compared to KF due to the reduced contribution of convective scale precipitation. It also modulates the spatial structure precipitation compared to KF, especially light precipitation over the outer bands, and the contribution of grid‐scale precipitation to total precipitation. KF shows the lowest contribution, around 50%, whereas BMJ exhibits a slightly higher contribution, and MSKF versions nearly reach 100%, making it closer to CP‐off. The improved performance in MSKF compared to KF highlights the importance of MSKF convection parameterization for gray zone (~1–4 km) simulation. The persistent discrepancy in landfall location in CP‐on and CP‐off simulation underscored the need of further investigating other physics parameterizations and dynamical mechanisms. Plain Language Summary: This study made an effort to unravel model sensitivity to microphysics parameterization (MP) and cumulus parameterization in convection‐permitting simulations, considering as a case study the intense cyclone "IANOS" that passed over the Mediterranean Sea. All simulations agreed with the main observed storm characteristics, though high‐resolution observations are still lacking to increase the results' confidence. The multi‐scale aware substantially reduces the precipitation compared to non‐scale aware Kain–Fritsch due to the reduced contribution of convective scale precipitation; however, the magnitude of the precipitation reduction varies its pairing with MP, indicating how the need for careful selection of microphysics and cumulus schemes is crucial. None of the sensitivity experiments successfully reproduced the accurate storm landfall location, prompting the need for additional research about the influence of alternative physics parameterizations such as planetary boundary layer and radiation as well as exploring other dynamical processes such as air‐sea coupling. Key Points: Simulations of Medicane IANOS at 3 km horizontal resolution are highly sensitive to microphysics and cumulus parameterization schemesMulti‐scale aware Kain–Fritsch (MSKF) improves the performance compared to Kain–Fritsch, which varies with microphysics parameterizationsMSKF's ability to generate optimal contributions of convective and grid‐scale precipitation makes it effective for gray zone (~1–4 km) simulation [ABSTRACT FROM AUTHOR]

The urbanization and frequency of extreme rainfall events (EREs) have considerably increased over the recent decade in several cities in India. Forecasting of these EREs remains a significant challenge not only in urban environments but also across diverse geographical regions. However, there is a particularly pressing need for improved rainfall forecasts in urban areas where the impacts of cities and human activities are profound. Using the Weather Research and Forecasting (WRF) model, a series of sensitivity experiments have been carried out by changing the various parameterization schemes to establish an improved model configuration for predicting EREs across the city of Bhubaneswar, Odisha- one of the most vulnerable cities to heavy rainfall in the recent decades. The study examines the influence of the urban heat island (UHI) effect on EREs, focusing on two specific EREs that occurred over Bhubaneswar and neighboring regions during the summer and post-monsoon season on July 19–21, 2018, and October 19–21, 2017. A total of thirty-two combinations of various cumulus, microphysics, and land surface sensitivity experiments are carried out for each ERE (a total of 64 for the two events). The results show that the combinations of Noah + + Nocumulus and Noah-MP + Thompson + Kain-Fritsch are the most effective in capturing the spatial and temporal patterns of EREs with a root mean square error of 33.9 and 36.2 mm, respectively. In addition, the study successfully reproduced vertical integrated specific humidity. Moreover, it has been observed that the UHI effect reduces rainfall intensity by 4% within urban areas during extreme rainfall events. [ABSTRACT FROM AUTHOR]

A full two-moment microphysics parameterization of the LIMA scheme (with LIMA standing for Liquid Ice Multiple Aerosols and hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow/aggregates, graupel, and hail), in addition to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number-concentration–mixing-ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow/aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio; to decrease the other ice hydrometeors by slowing down the growing processes of snow/aggregates, graupel, and hail; and to enhance the feedbacks between raindrops and the ice-phase hydrometeors. This comparison also emphasizes the unreasonable diagnostic approach used to estimate the number concentration of precipitating ice particles in the previous version of the scheme. The new scheme is more efficient at producing earlier raindrops at ground level and reducing hail precipitation. [ABSTRACT FROM AUTHOR]

Typhoons, characterized by their high destructive potential, significantly impact coastal residents' lives and property safety. To optimize numerical models' typhoon simulation, carefully selecting appropriate physical parameterization schemes is crucial, offering robust support for disaster prevention and reduction efforts. This study focuses on Typhoon Mujigae, conducting a comparative analysis of different physical parameterization schemes (microphysics, cumulus parameterization, shortwave radiation, and longwave radiation) in WRF simulations. The key findings are as follows: cumulus and microphysics parameterization schemes notably influence the simulation of typhoon tracks and intensity, while the impact of longwave and shortwave radiation schemes is relatively minor. Typhoon intensity is more sensitive to the choice of parameterization schemes than track. Together, the Kain-Fritsch cumulus convection scheme, WRF Single Moment 5-class scheme, and Dudhia/RRTM radiation scheme yield the best intensity simulation results. Compared with the Betts-Miller-Janjić and Grell 3D scheme, the use of the Kain-Fritsch scheme results in a clearer, taller eyewall and more symmetric deep convection, enhancing precipitation and latent heat release, and consequently improving the simulated typhoon intensity. More complex microphysics schemes like Purdue Lin, WRF Single Moment 5-class, and WRF Double Moment 6-class perform better in simulations, while simpler schemes like Kessler and WSM3 exhibit significant deviations in typhoon simulations. Particularly, the large amount of supercooled water clouds simulated by the Kessler scheme is a major source of bias. Furthermore, a coupling effect exists between cumulus convection and microphysics parameterization schemes, and only a reasonable combination of both can achieve optimal simulation results. [ABSTRACT FROM AUTHOR]

The cold (⁠|$\sim 10^{4}\, {\rm K}$|⁠) component of the circumgalactic medium (CGM) accounts for a significant fraction of all galactic baryons. However, using current galaxy-scale simulations to determine the origin and evolution of cold CGM gas poses a significant challenge, since it is computationally infeasible to directly simulate a galactic halo alongside the sub-pc scales that are crucial for understanding the interactions between cold CGM gas and the surrounding 'hot' medium. In this work, we introduce a new approach: the Cold Gas Subgrid Model (CGSM), which models unresolved cold gas as a second fluid in addition to the standard 'normal' gas fluid. The CGSM tracks the total mass density and bulk momentum of unresolved cold gas, deriving the properties of its unresolved cloudlets from the resolved gas phase. The interactions between the subgrid cold fluid and the resolved fluid are modelled by prescriptions from high-resolution simulations of 'cloud crushing' and thermal instability. Through a series of idealized tests, we demonstrate the CGSM's ability to overcome the resolution limitations of traditional hydrodynamics simulations, successfully capturing the correct cold gas mass, its spatial distribution, and the time-scales for cloud destruction and growth. We discuss the implications of using this model in cosmological simulations to more accurately represent the microphysics that govern the galactic baryon cycle. [ABSTRACT FROM AUTHOR]

Entrainment of subsaturated air into a cloud can influence its optical and microphysical properties in various ways, depending on the droplet evaporation and turbulent mixing time scales. Previous experiments in the Pi convection-cloud chamber have revealed that, given a fixed entrained air property, the mixing of entrained subsaturated air results in complete evaporation of some cloud droplets, with the rest remaining unchanged. This is a signature of inhomogeneous mixing. While comparing the results of entrainment with varying air properties, the mixing signature appears as if the subsaturated air is well mixed with the cloud to evenly reduce the droplets' size. In other words, taken together, the experiments appear to have the signature of homogeneous mixing. To explore these results in a greater depth, we conduct large-eddy simulations combined with a bin microphysics scheme. Our results reproduce the similar signatures of inhomogeneous and homogeneous mixing, implying that LES can resolve the inhomogeneous mixing when the grid spacing is smaller than the entrained air parcel. Additionally, we observe that increasing the aerosol injection rate enhances the signature of inhomogeneous mixing, while coarser grid spacing diminishes it. Finally, the change in wall fluxes in response to various entrained air properties confirms that the homogeneous signature seen in the analysis of an ensemble of simulations is the result of various equilibrium states. This further strengthens the suggestion that the homogeneous mixing signature found in aircraft observations near the cloud top may result from combining entrainment events of different intensities, possibly caused by various-sized eddies. Significance Statement: Large-eddy simulation and size-resolved microphysics can resolve time scales for turbulent mixing and evaporation and, therefore, are well suited for reproducing, extending, and interpreting the entrainment experiment in the Pi convection-cloud chamber. Our simulation results confirm (i) the inhomogeneous mixing signature for an individual entrainment event and (ii) the appearance of homogeneous mixing in an ensemble of entrainment episodes. Furthermore, we demonstrate that the inhomogeneous mixing signature is more pronounced in a polluted cloud, but coarser grid spacing in simulations may compromise the accuracy of this signature. Last, the homogeneous mixing signature results from various equilibrium states established for different entrainment intensities and adjusted wall fluxes, which are challenging to measure experimentally but can be easily analyzed in the simulations. [ABSTRACT FROM AUTHOR]

Cloudburst events are characterized by very high rainfall rates (100 mm h−1) over a small area (~10×10 km). It catastrophically damages the properties and causes loss of lives. Therefore, it is essential to understand the nature of the microphysical processes that contributed to producing such a high rain rate. Collision processes inside the cloud are vital in determining the rainfall rate. Hence, in the present study, we have evaluated a comparative performance of disparate collision efficiencies based on different formulations during the cloud burst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 AM IST (06:15 UTC 10 June 2021). The lifting condensation level (LCL), lifting deposition level (LDL), and lifting freezing levels (LFL) have been calculated using the ERA5 dataset. The collision efficiencies were determined on 100 levels between the lifting condensation and freezing levels. The appraisal of eight formulations shows that Onishi, Beard and Grover, Bohm, and Jin formulations yielded higher collision efficiencies during the cloudburst event, which is expected. Meanwhile, the estimated collision efficiency based on Ahmad's formulation is high for specific bins only. The formulations by Barnet, Long, and Lee and Baik, however, yielded very low values of collision efficiencies during the cloud burst event. Research highlights: Collision processes inside the cloud are vital in determining the rainfall rates especially during cloud burst. Eight collision efficiency formulations have been apprised during the cloud burst event of Sauni Binsar, Uttarakhand, India. The complex interaction between synoptic systems and orography caused atmosphere over Sauni Binsar to be convective and moist few hours before the cloud burst event. Collision efficiency formulation by Onishi, Beard and Grover, Bohm, and Jin estimated higher collision efficiencies, which is appropriate, while Ahmad estimated high values for specific bins only. Collision efficiency formulation by Barnet, Long, and Lee and Baik estimated very low values. [ABSTRACT FROM AUTHOR]

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included. [ABSTRACT FROM AUTHOR]

Various small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. The movies were used to capture hydrometeors with approximately 110,000 frames per sounding. When the particles in such movies were manually classified and measured sizes of particles for every several frames, it took an unrealistically long time to perform statistical analysis because of the large number of observed frames. Particle classification is subjective for the observers. This study developed a technique to classify cloud particles objectively using deep learning to overcome these problems and investigated the statistical microphysical characteristics of clouds. This study used the deep learning method You Only Looking Once for detection and classification. The training data were obtained using HYVIS in the Republic of Palau in 2013. The results trained using only HYVIS images showed low validity because some types of particles in the training data were insufficient. The data for typical particle shapes were augmented to improve the classification. Thus, the validity of classification using augmented data was improved. We used the Yonaguni Island HYVIS observation results from manual and artificial intelligence (AI) classification. The AI tended to classify the less irregular type than the manual, but no other significant differences were found. We believe this AI classification system will be for cloud microphysical studies on solid-phase particles. [ABSTRACT FROM AUTHOR]

This study employed version 4.2.2 of the Weather Research and Forecasting (WRF) model for this simulation and applied two microphysics schemes, the Thompson scheme (THOM) and Milbrandt–Yau scheme (MY)—which are widely used in convective simulations—to simulate a mesoscale severe convective precipitation event that occurred in southeastern China on 8 May 2017. The simulations were then compared with dual-polarization radar observations using a radar simulator. It was found that THOM produced vertical structures of radar reflectivity (ZH) closer to radar observations and accumulated precipitation more consistent with ground-based observations. However, both schemes overestimated specific differential phase (KDP) and differential reflectivity (ZDR) below the 0 °C level. Further analysis indicated that THOM produced more rain with larger raindrop sizes below the 0 °C level. Due to the close connection between raindrop breakup, evaporation rate, and raindrop size, sensitivity experiments on the breakup threshold (Db) and the evaporation efficiency (EE) of the THOM scheme were carried out. It was found that adjusting Db significantly changed the simulated raindrop size distribution and had a certain impact on the strength of cold pool; whereas modifying EE not only significantly changed the intensity and scope of the cold pool, but also had great effect on the raindrop size distribution. At the same time, comparison with dual-polarization radar observations indicated that reducing the Db can improve the model's simulation of polarimetric radar variables such as ZDR. This paper specifically analyzes a severe convective precipitation event in the Guangdong region under weak synoptic conditions and a humid climate. It demonstrates the feasibility of a method based on polarimetric radar data that modifies Db of THOM to achieve better consistency between simulations and observations in southeast China. Since the microphysical processes of different Mesoscale Convective Systems (MCSs) vary, the generalizability of this study needs to be validated through more cases and regions in the future. [ABSTRACT FROM AUTHOR]

Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15–16 h. [ABSTRACT FROM AUTHOR]

During multiple field campaigns, small quasi-spherical ice crystals, commonly referred to as frozen droplets (FDs), and their aggregates (frozen-droplet aggregates, FDAs) have been identified as the predominant habits in the upper regions of deep convective clouds (DCCs) and their associated anvils. These findings highlight the significance of FDs and FDAs for understanding the microphysics and radiative properties of DCCs. Despite the prevalence of FDs and FDAs at the tops of DCCs where they directly contribute to cloud radiative effect, the detailed single-scattering properties (e.g., scattering-phase function P11 and asymmetry parameter g) of FDs and FDAs remain highly uncertain. This uncertainty is mainly due to insufficient in situ measurements and the resolution of cloud probes, which hinder the development of idealized shape models for FDs and FDAs. In this study, two shape models, the Gaussian random sphere (GS) and droxtal (DX), are proposed as possible representations for the shapes of FDs and FDAs measured in situ. A total of 120 individual models of GSs and 129 models of DXs were generated by varying their shapes. Furthermore, by attaching these individual models in both a homogeneous and heterogeneous manner, three different types and a total of 404 models of FDAs were created: (1) aggregates of GSs; (2) aggregates of DXs; and (3) combinations of GSs and DXs, which are called habit mixtures (HMs). The P11 and g values of the developed models were calculated using a geometric optics method at a wavelength of 0.80 µm and then compared with those obtained using a polar nephelometer (PN) during the CIRCLE-2 field campaign to assess the models. Both individual-component ice crystals (i.e., either GS or DX) and homogeneous-component aggregates (i.e., either aggregates of GSs or aggregates of DXs) showed substantial differences compared with the PN measurements, whereas the P11 of the HMs was found to most accurately match the P11 measured in situ, reducing the differences to + 0.87 %, + 0.88 %, and - 5.37 % in the forward-, lateral-, and backward-scattering regions, respectively. The g value of the HMs was found to be 0.80, which falls within the range of the PN measurement (0.78 ± 0.04). The root-mean-square error for the HM was minimized to a value of 0.0427. It was shown that the novel HMs developed in this study demonstrated better performance than in previous research where HMs were developed indirectly by weighting the calculated P11 of shape models to interpret in situ measurement. The results of this study suggest potential implications for enhancing the calculation of single-scattering properties of ice crystals in DCCs. [ABSTRACT FROM AUTHOR]

Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the WPT. In the present study, we analyze the performance of the one-dimensional spectral bin model (SBM) developed by Carlin and Ryzhkov (2019), which simulates the change in the physical characteristics of precipitation particles of various sizes as they fall from the cloud top to the ground and diagnoses surface WPT. We compare the performance of the SBM and four other diagnostic methods that use the following variables: 1) atmospheric thickness, 2) wet-bulb temperature, 3) temperature and relative humidity, and 4) wet-bulb temperature and low-level lapse rate. Three reference WPTs (snow [SN], rain [RA], and RASN) are obtained from particle size velocity (PARSIVEL) disdrometer data using a newly proposed decision algorithm. The results show that the SBM has the highest overall skill score for winter precipitation, especially at the mountain sites. In contrast, the skill score of the SBM is lower than the other methods for RA. These results indicate that the SBM simulations tend to underestimate melting compared to observations. We thus explore the effects of the SBM's microphysics scheme on the extent of melting in cases of misdiagnosed RA. An optimized SBM that uses the climatological snow density‑diameter relationship for the Pyeongchang region produces an increased amount of melting and achieves an improved skill score compared to the original SBM, which uses climatological relationships for Colorado region. [ABSTRACT FROM AUTHOR]

The Four-Dimensional Variational (4DVar) data assimilation system of the Advanced Research Weather Research and Forecasting (WRF) model, developed by the international community dedicated to data assimilation research and operations, is customised to simulate the super cyclonic storm "Amphan" formed over the Bay of Bengal during May 16, 2020, to May 21, 2020. Five simulations are conducted using five different microphysics schemes namely, Kessler, Lin et al., WRF Single Moment 3-class (WSM3), WSM5, and WSM6 at a horizontal resolution of 18 km, keeping the Kain–Fritsch cumulus and the Yonsei University planetary boundary-layer scheme fixed. The model simulated features of "Amphan" are compared with observational data from the India Meteorological Department (IMD), the Global Precipitation Measurement mission (GPM), and the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA-5) over the specified region. Among all the schemes, Lin et al. scheme shows track remarkably close to the observed track. Lin et al. (WSM5) scheme shows least along track (AT) error of 7.47 km at 24-h forecast length. Lin et al. shows least AT error of 5.8 km (28.12 km) for 48-h (72-h) forecast length. All schemes except Kessler and WSM3 show the spatial distribution of maximum sustained wind (MSW) surrounding the eye of the cyclone which is similar with ERA5 data. All the schemes underestimate the 10m-MSW during the entire life of the storm. However, the Kessler scheme simulates higher 10m-MSW during 00 UTC 18 May to 12 UTC 19 May in comparison to other schemes and further the simulated MSW matches with IMD observation up to 06 UTC 20 May. The Kessler scheme overestimates the MSLP for the intensity level ESCS-VSCS-SCS-CS valid 09 UTC on 19 May to 00 UTC on 21 May and other schemes underestimate during this period. The analysis carried out with the Method for Object-Based Diagnostic Evaluation tool reveals that the Lin et al. (WSM6) scheme indicates enhanced forecast proficiency for accumulation valid 00 UTC 20 May (21 May) 2020. The analysis of vertically integrated moisture transport (VIMT) and vertically integrated moisture divergence (VIMD) suggests that the greater moisture transport is quite evident in Lin et al. scheme during the SuCS intensity level. Kessler scheme is efficient in simulating warm-rain process and high intensity storm. [ABSTRACT FROM AUTHOR]

One ought to be logically consistent. This is sometimes referred to as the Normativity Thesis about logic. But why do we acknowledge it? This is the "source question". This paper shows that Frege provided a promising, yet often ignored, answer to that question. Frege held that the logical consequence relation |= is not intrinsically normative. Rather, the normativity of logic is derived from something other than logic itself, namely the norm that one ought to judge truly and not falsely. I demonstrate that this carves out a spectrum of two opposed views on why logic is normative. I call Frege's view extrinsicism, which contrasts with intrinsicism. The extrinsicist view, from an exegetical point of view, is basically absent from all interpretations of Frege's view on the normativity of logic. From a systematic point of view, it offers a sound alternative to the widely accepted assumption that logic is demarcated from other disciplines such as microphysics, biology or arithmetic through a distinct and exceptional kind of normative force over how to judge and reason about the world, because it is taken to have a direct relation to truth and truth preservation. [ABSTRACT FROM AUTHOR]

Moving mirrors as analogue sources of Hawking radiation from black holes have been explored extensively but less so with cosmological particle creation (CPC), even though the analogy between the dynamical Casimir effect (DCE) and CPC based on the mechanism of the parametric amplification of quantum field fluctuations has also been known for a long time. This 'perspective' essay intends to convey some of the rigor and thoroughness of quantum field theory in curved spacetime, which serves as the theoretical foundation of CPC, to DCE, which enjoys a variety of active experimental explorations. We have selected seven issues of relevance to address, starting from the naively simple ones, e.g., why one should be bothered with 'curved' spacetime when performing a laboratory experiment in ostensibly flat space, to foundational theoretical ones, such as the frequent appearance of nonlocal dissipation in the system dynamics induced by colored noises in its field environment, the existence of quantum Lenz law and fluctuation–dissipation relations in the backreaction effects of DCE emission on the moving atom/mirror or the source, and the construction of a microphysics model to account for the dynamical responses of a mirror or medium. The strengthening of the theoretical ground for DCE is not only useful for improving conceptual clarity but needed for the development of the proof-of-concept type of future experimental designs for DCE. The results from the DCE experiments in turn will enrich our understanding of quantum field effects in the early universe because they are, in the spirit of analogue gravity, our best hopes for the verification of these fundamental processes. [ABSTRACT FROM AUTHOR]

A simple analytical model, the zero‐buoyancy plume (ZBP) model, has been proposed to understand how small‐scale processes such as plume‐environment mixing and evaporation affect the steady‐state structure of the atmosphere. In this study, we refine the ZBP model to achieve self‐consistent analytical solutions for convective mass flux, addressing the inconsistencies in previous solutions. Our refined ZBP model reveals that increasing plume‐environment mixing can increase upper‐troposphere mass flux through two pathways: increased cloud evaporation or reduced atmospheric stability. To validate these findings, we conducted small‐domain convection‐permitting Radiative‐Convective Equilibrium simulations with horizontal resolutions ranging from 4 km to 125 m. As a proxy for plume‐environment mixing strength, the diagnosed entrainment rate increases with finer resolution. Consistent with a previous study, we observed that both anvil cloud fraction and upper‐troposphere mass flux increase with higher resolution. Analysis of the clear‐sky energy balance in the simulations with two different microphysics schemes identified both pathways proposed by the ZBP model. The dominant pathway depends on the relative strengths of evaporation cooling and radiative cooling in the environment. Our work provides a refined simple framework for understanding the interaction between small‐scale convective processes and large‐scale atmospheric structure. Plain Language Summary: High, anvil‐shaped clouds in the tropics significantly impact our climate, but simulating them accurately is challenging. Our study investigates how small‐scale mixing affects anvil clouds and vertical air movement in the upper atmosphere. We refined a simple analytical model, which treats convection as a single plume, to better understand these processes. Our results show that stronger mixing increases the amount of air moving upward, either through increased cloud evaporation or reduced atmospheric stability. We confirmed these findings with km‐scale numerical simulations at various grid sizes. Finer resolutions, indicating stronger mixing, led to more anvil clouds and greater upward air movement, supporting our simple plume model's predictions. These insights improve our understanding of how small‐scale processes influence the large‐scale atmosphere. Key Points: We derived a self‐consistent analytical solution for a zero‐buoyancy plume model to understand the steady‐state tropical atmosphereThe plume model suggests that increased mixing enhances convective mass flux via greater cloud evaporation or reduced atmospheric stabilitySteady‐state simulations with finer grids, indicating stronger mixing, show increased anvil cloud amount and upper‐troposphere mass flux [ABSTRACT FROM AUTHOR]

Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects. [ABSTRACT FROM AUTHOR]

The rate at which aerosols spread from a point source injection, such as from a ship or other stationary pollution source, is critical for accurately representing subgrid plume spreading in a climate model. Such climate model results will guide future decisions regarding the feasibility and application of large-scale intentional marine cloud brightening (MCB). Prior modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers and shear. In this study, we apply a Lagrangian particle model (PM-ABL v1.0), governed by a Langevin stochastic differential equation, to create a simplified framework for predicting the rate of spreading from a ship-injected aerosol plume in sheared, precipitating, and non-precipitating boundary layers. The velocity and position of each stochastic particle is predicted with the acceleration of each particle being driven by the turbulent kinetic energy, dissipation rate, momentum variance, and mean wind. These inputs to the stochastic particle velocity equation are derived from high-fidelity large-eddy simulations (LES) equipped with a prognostic aerosol–cloud microphysics scheme (UW-SAM) to simulate an aerosol injection from a ship into a cloud-topped marine boundary layer. The resulting spreading rate from the reduced-order stochastic model is then compared to the spreading rate in the LES. The stochastic particle velocity representation is shown to reasonably reproduce spreading rates in sheared, precipitating, and non-precipitating cases using domain-averaged turbulent statistics from the LES. [ABSTRACT FROM AUTHOR]

Recent studies have suggested that injection of solid particles such as alumina and calcite particles for stratospheric aerosol injection (SAI) instead of sulfur-based injections could reduce some of the adverse side effects of SAI such as ozone depletion and stratospheric heating. Here, we present a version of the global aerosol–chemistry–climate model SOCOL-AERv2 and the Earth system model (ESM) SOCOLv4 which incorporate a solid-particle microphysics scheme for assessment of SAI of solid particles. Microphysical interactions of the solid particle with the stratospheric sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code (RTC) for the first time within an ESM. Therefore, the model allows simulation of heterogeneous chemistry at the particle surface as well as feedbacks between microphysics, chemistry, radiation and climate. We show that sulfur-based SAI results in a doubling of the stratospheric aerosol burden compared to the same mass injection rate of calcite and alumina particles with a radius of 240 nm. Most of the sulfuric acid aerosol mass resulting from SO2 injection does not need to be lifted to the stratosphere but is formed after in situ oxidation and subsequent water uptake in the stratosphere. Therefore, to achieve the same radiative forcing, larger injection rates are needed for calcite and alumina particle injection than for sulfur-based SAI. The stratospheric sulfur cycle would be significantly perturbed, with a reduction in stratospheric sulfuric acid burden by 53 %, when injecting 5 Mtyr-1 (megatons per year) of alumina or calcite particles of 240 nm radius. We show that alumina particles will acquire a sulfuric acid coating equivalent to about 10 nm thickness if the sulfuric acid is equally distributed over the whole available particle surface area in the lower stratosphere. However, due to the steep contact angle of sulfuric acid on alumina particles, the sulfuric acid coating would likely not cover the entire alumina surface, which would result in available surface for heterogeneous reactions other than the ones on sulfuric acid. When applying realistic uptake coefficients of 1.0, 10-5 and 10-4 for H2SO4 , HCl and HNO3 , respectively, the same scenario with injections of calcite particles results in 94 % of the particle mass remaining in the form of CaCO3. This likely keeps the optical properties of the calcite particles intact but could significantly alter the heterogeneous reactions occurring on the particle surfaces. The major process uncertainties of solid-particle SAI are (1) the solid-particle microphysics in the injection plume and degree of agglomeration of solid particles on the sub-ESM grid scale, (2) the scattering properties of the resulting agglomerates, (3) heterogeneous chemistry on the particle surface, and (4) aerosol–cloud interactions. These uncertainties can only be addressed with extensive, coordinated experimental and modelling research efforts. The model presented in this work offers a useful tool for sensitivity studies and incorporating new experimental results on SAI of solid particles. [ABSTRACT FROM AUTHOR]

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent. [ABSTRACT FROM AUTHOR]

Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process level arising from limited systematic observations. Most surface-based active remote sensors, including today's operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and submeter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single-photon-counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate. SIGNIFICANCE STATEMENT: In this study, we introduced two novel remote sensing instruments capable of high-resolution cloud observations: a cloud radar system with a range resolution down to 2.8 m and a lidar system with a range resolution down to 10 cm. Observations show that the radar and lidar systems resolve detailed cloud structures and microphysical processes which would not be obtained from traditional remote sensing systems. The high-resolution measurements provide insights toward the understanding of cloud microphysics at a fundamental level. [ABSTRACT FROM AUTHOR]

This study addresses the critical need for accurate lightning prediction in northeastern India, a region frequently impacted by severe convective storms. Despite advancements in forecasting, there remains a gap in predicting lightning activity, especially during extreme weather. This research evaluates the Weather Research and Forecasting (WRF) model's performance in simulating lightning events on June 16 and 17, 2022. The study employs the Morrison double-moment 6-class microphysics scheme and the Price and Rind A simple lightning parameterization for calculating global lightning distributions lightning parameterization method to model cloud-to-ground (CG) and intra-cloud (IC) lightning. Model predictions are validated against data from the Indian Institute of Tropical Meteorology Lightning Location Network (IITM-LLN) and the World-Wide Lightning Location Network (WWLLN), alongside INSAT-3D cloud brightness temperature and NASA-IMERG rainfall data. The research findings show that the lightning parameterization scheme with a 20 dBZ top threshold provides accurate predictions, with spatial autocorrelation metrics of 67.41% and 85.75% for June 16 and 17, respectively. Further analysis through skill scores, such as the Equitable Threat Score and Probability of Detection, supports the model's reliability. Hotspot analysis identifies regions of heightened lightning activity, offering valuable insights into areas prone to frequent strikes. This research emphasizes the importance of advanced modeling techniques and comprehensive data integration in enhancing weather forecasts, contributing to improved safety and mitigation strategies for extreme weather events in northeastern India. [ABSTRACT FROM AUTHOR]

Although several studies have shown that sea spray aerosol (SSA) has the potential to act as ice nucleating particles (INP) impacting cloud formation, there is a lack of marine INP studies in tropical latitudes. This is partly due to the unavailability of local oceanographic cruises that perform aerosol-cloud interaction studies in the tropics, as well as the scarcity of appropriate aerosol and cloud microphysics instrumentation. The present study shows the development of the UNAM-MARine Aerosol Tank (UNAM-MARAT), a device that simulates wave breaking to generate SSA particles with the main purpose to characterize their physicochemical properties including their ice nucleating abilities. The UNAM-MARAT was characterized using Instant Ocean Sea Salt and its potential to study ambient sea waters was evaluated with sea seawater samples collected from the Port of Veracruz (PoV) in the Gulf of Mexico, and from the Bay of Acapulco (BoA) and the Bay of Santiago- Manzanillo (BoSM) in the Mexican Pacific Ocean. The portable and automatic UNAM-MARAT is able to generate aerosol particle concentrations as high as 2000 cm-3 covering a wide range of sizes, from 30 nm to 10 μm, similar to those found in the ambient marine boundary layer. The SSA generated from the three natural seawater samples was found to act as INP via immersion freezing, with INP concentrations as high as 130.7 L-1. The particles generated from the BoA seawater samples were the most efficient INPs, reporting the highest ice active site density (ns) values between -20 and -30 °C. Our results also show the direct relationship between particle size and its composition. Larger particles (> 1 μm) were found to be enriched in sodium chloride. In contrast, the fraction of Ca2+, Mg2+, and NO3- was found to increase with decreasing the particle size from 10 μm to 320 nm. This suggests the presence of dissolved organic material in the submicron particles. [ABSTRACT FROM AUTHOR]

Using multifrequency radar observations providing raindrop size distribution evolution with high spatial and temporal resolution, this study aims to assess the ability of different parameterizations of raindrop self‐collection and breakup processes applied in mesoscale models, to reproduce the statistics derived from observations. The stratiform zones of two types of precipitating systems are studied, a frontal situation that occurred over Finland in June 2014 and a squall line system observed over Oklahoma in June 2011. An analysis method for determining raindrop trajectories was used to obtain the temporal variation of the total raindrop concentration from the observations. The resulting raindrop concentration rate as a function of the mean volume diameter reveals significant differences with the parameterizations currently used in two‐moment bulk microphysics schemes. These results show that even if they produce variations in raindrop concentration of the same order of magnitude as the observations, the current parameterizations diverge from the median of the observations, resulting in an overestimation of either the self‐collection or the breakup process. From the median of radar observations, new parameterizations of the self‐collection and breakup processes and of rain self‐collection efficiency are developed and can be implemented in two‐moment bulk microphysics schemes. Plain Language Summary: A better knowledge of atmospheric processes leading to precipitation is mandatory to better predict severe floods and mitigate their impact on human societies, in particular in the context of climate change. Some of the most uncertain rain microphysics processes are the raindrop self‐collection and breakup. These processes are represented in numerical weather prediction (NWP) models via significantly different parameterizations, based on few laboratory experiments or from purely empirical approaches. In this work, two types of precipitating systems are studied, a frontal situation that occurred over Finland in June 2014 and a squall line system observed over Oklahoma in June 2011. In both cases, multifrequency radar observations provide raindrop size distribution with high spatial and temporal resolution. Comparisons between observations and current parameterizations show significant divergences. Hence, a new parameterization of raindrop self‐collection and breakup processes is developed based on multifrequency radar observations. Key Points: The temporal variation of raindrop concentrations as they fall is derived from zenith pointing Ka‐ and W‐band radar measurementsRaindrop concentration rate obtained from the parameterizations of microphysical processes used in mesoscale models differ from observationsA new parameterization of self‐collection and breakup processes based on the multifrequency radar observations is proposed [ABSTRACT FROM AUTHOR]

This study examines the vertical variations of cloud microphysics and their correlation with the thickness of the entrainment interfacial layer (EIL) in stratocumulus clouds, observed in the Physics of Stratocumulus Top (POST) aircraft measurement campaign. From the mixing fraction analysis, we identified EIL between the free atmosphere and cloud top for all 15 POST flights, and found that EIL thickness significantly influenced the vertical variation of cloud microphysics and thermodynamics. In several flights, a trend toward stronger homogeneous mixing traits with increasing depth from the cloud top was found, indicative of the vertical movement of mixed (i.e., entrainment‐affected and diluted) parcels. However, in one flight, this trend was limited to the middle part of the cloud only, with the correlation between virtual potential temperature and liquid water content being strongly negative near the cloud top, suggesting limited downward movement of mixed parcels. Another important finding is that there was a robust negative correlation between long‐wave cooling rate near the cloud top and EIL thickness, highlighting differences in radiative cooling rates between mixed and unmixed parcels due to differences in liquid water content between them. These insightful findings will be crucial for enhancing our understanding of the role of EIL in modulating entrainment and the vertical movement of mixed parcels in stratocumulus clouds. [ABSTRACT FROM AUTHOR]

Recent studies have suggested that stratospheric aerosol injection (SAI) of solid particles for climate intervention could reduce stratospheric warming compared to injection of SO2 ${\text{SO}}_{2}$. However, interactions of microphysical processes, such as settling and coagulation of solid particles, with stratospheric dynamics have not been considered. Using a global chemistry‐climate model with interactive solid particle microphysics, we show that agglomeration significantly reduces the backscatter efficiency per unit of injected material compared to mono‐disperse particles, partly due to faster settling of the agglomerates, but mainly due to increased forward‐ over backscattering with increasing agglomerate size. Despite these effects, some materials substantially reduce required injection rates as well as perturbation of stratospheric winds, age of air and stratospheric warming compared to injection of SO2 ${\text{SO}}_{2}$, with the most promising results being shown by 150 nm diamond particles. Uncertainties remain as to whether stratospheric dispersion of solid particles is feasible without formation of agglomerates. Plain Language Summary: [Stratospheric warming is an undesired side effect of climate intervention via SAI. Recent studies have shown that stratospheric warming could be reduced when injecting solid particles instead of gaseous SO2 ${\text{SO}}_{2}$. However, most of these studies looked at the stratospheric particle mass required for a given radiative forcing (RF), without accounting for gravitational settling of particles or the effect of particles sticking together after mutual collision. We show that accounting for these effects significantly reduces the amount of backward reflected radiation per unit of stratospheric particle mass decreasing the radiative efficiency of the injected material. This is due to the combined effect of faster gravitational settling and the larger fraction of forward reflected radiation over backward reflected radiation with increasing agglomerate size. However, we show that injection of diamond particles at a radius of 150 nm instead of SO2 ${\text{SO}}_{2}$ significantly reduces required stratospheric injection rates as well as perturbation of stratospheric winds, age of stratospheric air and stratospheric water vapor concentrations due to small stratospheric warming per unit of RF. However, large uncertainties remain as to whether it will be feasible to inject solid particles into the stratosphere at concentrations low enough to prohibit that the particles stick together.] Key Points: We explore stratospheric injections of six solid particles and gaseous SO2 within a climate model with comprehensive aerosol microphysicsAccounting for settling and agglomeration of solid particles can substantially reduce the radiative forcing (RF) per unit of injected materialInjection of diamond particles (r = 150 nm) instead of SO2 largely reduces stratospheric temperature, circulation and water vapor anomalies [ABSTRACT FROM AUTHOR]

Determining whether a cloud will evolve into a thunderstorm is beneficial for understanding thunderstorm formation and also important for ensuring the safety of society. However, a clear understanding of the microphysics of clouds in terms of the occurrence of lightning activity has not been attained. Vast field observations and laboratory experiments indicate that graupel, which is rimed ice, is a vital hydrometeor for lightning generation and is the foundation of riming electrification. In this study, polarimetric radar and lightning observations are used to compare the ice microphysics associated with graupel between 57 isolated thunderstorms and 39 isolated non-thunderstorms, and the differences in radar parameters are quantified. Our results for the occurrence of lightning activity in clouds revealed the following results: (1) the maximum difference in graupel volume at the - 10 °C isotherm height between thunderstorms and non-thunderstorms reached approximately 7.6 km3 ; (2) the graupel particles approached spherical shapes, with a mean differential reflectivity (ZDR) value of 0.3 dB , which likely indicated that heavily rimed graupel was present; (3) the median values of horizontal reflectivity (ZH) or ZDR at positions where the source initiation and channel of the first lightning flashes were nearly 31 dBZ or 0 dB ; and (4) 98.2 % of the thunderstorms were equipped with a ZDR column, and the mean depth was ∼ 2.5 km. Our study deepens our understanding of lighting physics and thunderstorm formation. [ABSTRACT FROM AUTHOR]

The Asian Summer Monsoon (ASM) convection transports aerosols and their precursors from the boundary layer to the upper troposphere and lower stratosphere (UTLS). This process forms an annually recurring aerosol layer near the tropopause. Recent observations have revealed a distinct property of the aerosol layer over the ASM region, it is nitrate‐rich. We present a newly implemented aerosol formation algorithm that enhances the representation of nitrate aerosol in the Community Aerosol and Radiation Model for Atmospheres (CARMA) coupled with the Community Earth System Model (CESM). The simulated aerosol chemical composition, as well as vertical distributions of aerosol size and mass, are evaluated using in situ and remote sensing observations. The simulated concentrations (ammonium, nitrate, and sulfate) and size distributions are generally within the error bars of data. We find nitrate, organics, and sulfate contribute significantly to the UTLS aerosol concentration between 15°–45°N and 0°–160°E. The two key formation mechanisms of nitrate‐containing aerosols in the ATAL are ammonium neutralization to form ammonium nitrate in regions where convection is active, and condensation of nitric acid in regions of cold temperature. Furthermore, including nitrate formation in the model doubles the surface area density in the tropical tropopause region between 15°–45°N and 0°–160°E, which alters the chlorine partitioning and subsequently impacts the rate of ozone depletion. Plain Language Summary: The Asian Summer Monsoon can efficiently transport pollutants into the upper troposphere and form a layer of aerosols called the Asian Tropopause Aerosol Layer (ATAL). This research investigates the two key formation mechanisms for nitrate aerosol in the ATAL: one is due to cold temperature, and one is due to ammonium neutralization. The simulations are validated against in situ measurements. It also found that O3 chemistry inside ATAL is influenced by nitrate aerosol. Key Points: A new nitrate aerosol formation algorithm is implemented for the community model CARMA to represent the Asian Tropopause Aerosol LayerThe simulated aerosol size distributions and compositions are within error bars of observationsIncluding the new nitrate formation scheme doubles the simulated surface area, changing the chlorine partitioning and ozone depletion [ABSTRACT FROM AUTHOR]

Bi‐spectral retrievals of droplet effective radius and cloud optical depth are widely utilized to estimate aerosol cloud interactions (ACI) in warm clouds in the marine boundary layer. Here, we assess the effect of retrieval errors due to the neglect of 3D radiative transfer during the retrieval process on this analysis of ACI. We use an ensemble of stochastically‐modeled cloud fields and 3D radiative transfer simulations to study the retrieval errors at a solar zenith angle of 30°. Simulated retrieval biases in droplet number concentration (Nd) for all three MODIS channels vary systematically from +35% to −80% as cloud heterogeneity increases. Pixel‐level errors can be much larger. Commonly utilized subsampling strategies do not reduce the systematic variation in retrieval error. Negative error correlations between optical depth and droplet effective radius produce spuriously negative slopes between the logarithm of liquid water path and Nd (−1.0 to −0.3). Pixels at the center of (8 km)2 patches that are not overcast have a relative bias in Nd of −50%. The relative frequency of these biased pixels varies linearly with the clear fraction in MODIS data and form the basis for a simple parameterization of Nd bias that varies with cloud fraction (CF). Using this parameterization, synthetic data experiments indicate that estimates of the first indirect effect in the tropical ocean can be overestimated by up to 30%, and the effect of aerosol on CF is overestimated by 50%, due to neglecting the correlation between retrieval errors and cloud microphysics. Plain Language Summary: Humans produce pollution which takes the form of small particles in the air called aerosols. The emission of these aerosol particles is predicted to increase the amount of the Sun's light that is reflected by clouds, and thereby offset some amount of global warming. We need measurements to understand whether this effect is occurring over the Earth to a significant degree. Satellites that observe the Earth are one of the main ways in which we can detect the changes of cloud reflectivity due to anthropogenic pollution. In this work, we showed that the most widely used algorithm for interpreting changes in cloud reflectivity misinterprets the changes in reflectivity due to variations in cloud spatial structure (e.g., cloud top bumpiness) as being caused only by variations in the sizes of droplets. This limitation lowers our confidence in our observations of the effects of human pollution on the Earth's climate and motivates the development of new instruments and techniques that do account for changes in cloud spatial structure, which will further our understanding of the role of human pollution on the Earth's climate. Key Points: Popular techniques for selecting bi‐spectral cloud retrievals with low error do not reduce systematic bias caused by cloud heterogeneityCorrelations between cloud heterogeneity and microphysics bias estimates of first indirect effect and cloud fraction adjustmentAnticorrelated errors in cloud optical depth and droplet effective radius bias estimates of liquid water path adjustments [ABSTRACT FROM AUTHOR]

To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machinelearning tool was used to evaluate the accuracy. Unintuitively, the machinelearning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested. [ABSTRACT FROM AUTHOR]

Cloud microphysics fulfills a fundamental role in the formation and evolution of marine fog, but it is not fully understood. Numerous studies have addressed this by means of direct observations and modeling efforts. However, collision–coalescence of aerosols and fog droplets is a process often neglected. In this study we perform an analysis of the role of particle collections on the formation, development, and microphysical structure of marine fog. It was found that collisions open a path for aerosol activation by means of collisional activation. In addition, collisions contribute to the diffusional activation of fog particles by adding water mass to the growing aerosols, making them reach the required critical radius faster. Furthermore, collisions have a homogenizing effect on hygroscopicity, facilitating the activation of accumulation‐mode aerosols by increasing their diffusional growth. [ABSTRACT FROM AUTHOR]

We examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation. [ABSTRACT FROM AUTHOR]

The work presented in this article studies the impact of cloud condensation nuclei (CCN) activation for fog forecasting and improves its parameterization in the LIMA (Liquid, Ice, Multiple Aerosols) two‐moment microphysical scheme, building upon the Local And Non‐local Fog EXperiment (LANFEX) field campaign observations, specifically the intensive observation period (IOP) 1 and the DEMISTIFY intercomparison. Large‐eddy simulations were performed with the Meso‐NH model, first using a prognostic supersaturation allowing us to compute the number of activated CCN at each time step, and then with the usual saturation adjustment hypothesis and a diagnostic maximum supersaturation. The prognostic supersaturation method provided very good results, similar to those from earlier simulations of this case using a bin scheme, and was thus used as a reference. In contrast, the diagnostic maximum supersaturation method strongly overestimated droplet numbers and produced a too‐thick fog. Thus, improvements to the maximum supersaturation diagnostic were proposed, by (1) revising the temperature tendency and (2) accounting for pre‐existing cloud droplets in the activation parameterization. These improvements resulted in a simulation in good agreement with observations and the reference simulation, and are promising for use in numerical weather prediction systems with a lower resolution and/or a longer time step. [ABSTRACT FROM AUTHOR]

We present MATRIX‐VBS, a new aerosol scheme that simulates organic partitioning in an aerosol microphysics model, as part of the NASA GISS ModelE Earth System Model. MATRIX‐VBS builds on its predecessor aerosol microphysics model MATRIX (Bauer et al., 2008, https://doi.org/10.5194/acp‐8‐6003‐2008) and was developed in the box model framework (Gao et al., 2017, https://doi.org/10.5194/gmd‐10‐751‐2017). The scheme features the inclusion of organic partitioning between the gas and particle phases and the photochemical aging process using the volatility‐basis set (Donahue et al., 2006, https://doi.org/10.1021/es052297c). To assess the performance of the new model, we compared its mass concentration, number concentration, and activated number concentration to the original scheme MATRIX, and evaluated its mass concentrations in four seasons against data from the NASA Atmospheric Tomography Mission (ATom) aircraft campaign. Results from MATRIX‐VBS show that organics are transported further away from their source, and their mass concentration increases aloft and decreases at the surface compared to those in MATRIX. The mass concentration of organics at the surface agrees well with measurements, and there are discrepancies for vertical profiles aloft. In the new scheme, the global mass load of organic aerosols increased by 50%, there is also an increased number of particles at the surface and fewer activated ones in most regions. The new scheme presents advanced and more comprehensive capability in simulating aerosol processes. Plain Language Summary: MATRIX‐VBS is a new aerosol microphysics component in NASA's GISS ModelE Earth System Model. It is an advancement over the previous MATRIX aerosol microphysics model, incorporating how organics in the atmosphere are divided between gas and particle phases. We tested how well MATRIX‐VBS performs by comparing how much mass and how many particles it simulates compared to those from the original MATRIX model, and compared particle amounts from both models against real‐world data collected by NASA's ATom aircraft campaign. We found that in MATRIX‐VBS, organic particles spread further from their source regions, increase higher up in the atmosphere, and decrease near the ground. The model's simulations matched well with ground measurements, but there were some differences in higher altitudes. Overall, the new model showed a 50% increase in the global amount of organic aerosols, more particles at ground level, but fewer that could lead to cloud formation. This indicates that MATRIX‐VBS offers a more detailed and accurate way of understanding how these tiny particles behave in our atmosphere. Key Points: We present an aerosol microphysical scheme MATRIX‐VBS that simulates organic partitioning in GISS ModelE using the volatility‐basis setOrganic aerosols are transported further away and aloft, and their mass load increased in MATRIX‐VBSNumber of aerosols increased in MATRIX‐VBS while activated number concentration decreased [ABSTRACT FROM AUTHOR]

Global modeling of aerosol‐particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine‐resolution global models are desirable for representing nonlinear aerosol‐microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol‐microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO‐Moment Aerosol Sectional (TOMAS) microphysics scheme with the High‐Performance configuration of the GEOS‐Chem model of atmospheric composition (GCHP), a coupling termed GCHP‐TOMAS. GCHP's architecture allows massively parallel GCHP‐TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 105 model columns) versus the previous single‐node capability of GEOS‐Chem‐TOMAS (tens of cores, 200 km × 250 km, 1.3 × 104 model columns). GCHP‐TOMAS runtimes have near‐ideal scalability with computing‐core number. Simulated global‐mean number concentrations increase (dominated by free‐tropospheric over‐ocean sub‐10‐nm‐diameter particles) toward finer GCHP‐TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free‐tropospheric total particle number by 18.5%, and over‐ocean sub‐10‐nm‐diameter particles by 39.8% at 4‐km altitude. With a cascade of contributing factors, free‐tropospheric particle‐precursor concentrations increase (32.6% at 4‐km altitude) with resolution, promoting new‐particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality. Plain Language Summary: Small particles in the air have important effects on Earth's climate and air quality. Representing the number and size of these particles in global models is challenging because their processes are complex. This factor has often limited global‐model horizontal resolution because fine global resolution models (e.g., 25 km × 25 km or smaller) generally ran too slowly but would be useful for representing details missed at traditional coarse resolution (e.g., 200 km × 250 km). We start with a detailed particle scheme that previously only ran at coarse global resolution because fine resolution would take too long. We present the initial use of this scheme in an updated model version, with a structure allowing a fast‐running, high‐memory model with fine resolution, by using hundreds to thousands of computer cores. In the updated structure, model speed increases with the number of cores used. We find that the total number of particles in the model is more with fine compared to coarse model resolution. These increases are most in Earth's remote regions and for particles which come from gas. Using fine model resolution globally when representing particles could change our understanding of how they impact Earth's climate and air quality. Key Points: We couple aerosol microphysics with GEOS‐Chem's High‐Performance configuration for fine (25 km × 25 km) global‐resolution capabilityGlobal‐mean aerosol number increases with model resolution, dominated by particles smaller than 10 nm in the over‐ocean free troposphereToward finer horizontal resolution, enhanced particle precursor loading in the free troposphere promotes particle formation and growth [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes. [ABSTRACT FROM AUTHOR]

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The study on the characteristic and the parameterization of cloud droplet effective radius (re) is important for the optimization of warm cloud microphysical schemes in numerical models. Based on numerical simulations combined with the four aircraft observations of stratiform warm clouds in Jiangxi, China, this work investigated their microphysical properties, focusing on the evolutionary pattern and the parameterization of re, as well as the correlation between re and the number concentration (Nc) of cloud droplet and liquid water path (LWP). It is found that there is a negative correlation between re and Nc, and the rate of the decrease could be increased by 2–4 times with a strengthening of collision‐coalescence process. What's more, the direct introduction of collision‐coalescence is conducted based on the existing re parameterization schemes, and the results show that it can perform well in most of collision‐coalescence intensities. It is also found that LWP is positively correlated with re when re is less than 12 μm, after which the relationship changes to a negative correlation due to the impact of rainfall intensity. Plain Language Summary: The bin microphysical scheme can reproduce the microphysical structures of warm cloud, combined with aircraft observations to investigate the microphysics within warm clouds. This work analyzes the evolution characteristics and the parameterization of effective radius in warm clouds based on bin microphysics simulations. Thereafter, the impact of these cloud microphysical quantities to liquid water path (LWP) are also researched. The results show that the rate of effective radius decrease with number concentration can be intensified by collision‐coalescence intensity, and an effective radius parameterization scheme related to collision‐coalescence is proposed based on the existing parameterization schemes. The variation pattern of LWP with effective radius is linked to the critical radius for rainfall intensity. Above all, the effective radius variation pattern depends largely on collision‐coalescence intensity and can affect LWP a lot. These findings are important for understanding warm cloud microphysical parameterization and improving climate assessment. Key Points: The relation between the effective radius and cloud droplet number concentration greatly influenced by collision‐coalescence intensityAn effective radius parameterization scheme which is directly related to collision‐coalescence is considered based on the existing schemesLiquid water path has different relationship with number and effective radius of cloud droplets, also affected by the rainfall intensity [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) scheme was modified by incorporating predicted graupel density. Explicitly predicted graupel density, in turn, modifies graupel characteristics such as the fall velocity–diameter and mass–diameter relationships of graupel. The modified WDM6 has been evaluated based on a two-dimensional (2D) idealized squall line simulation and winter snowfall events that occurred during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign over the Korean Peninsula. From the 2D simulation, we confirmed that the modified WDM6 can simulate varying graupel densities, ranging from low values in an anvil cloud region to high values in the convective region at the mature stage of a squall line. Simulations with the modified WDM6 increased graupel amounts at the surface and decreased graupel aloft because of the faster sedimentation of graupel for two winter snowfall cases during the ICE-POP 2018 campaign, as simulated in the 2D idealized model. The altered graupel sedimentation in the modified WDM6 influenced the magnitude of the major microphysical processes of graupel and snow, subsequently reducing the surface snow amount and precipitation over the mountainous region. The reduced surface precipitation over the mountainous region mitigates the surface precipitation bias observed in the original WDM6, resulting in better statistical skill scores for the root mean square errors. Notably, the modified WDM6 reasonably captures the relationship between graupel density and its fall velocity, as retrieved from 2D video disdrometer measurements, thus emphasizing the necessity of including predicted graupel density to realistically represent the microphysical properties of graupel in models. [ABSTRACT FROM AUTHOR]

It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation. [ABSTRACT FROM AUTHOR]

Large-eddy simulations (LESs) are conducted for each day of the intensive observation periods (IOPs) of the Green Ocean Amazon (GoAmazon) field campaign to characterize the updrafts and microphysics within deep convective cores while contrasting those properties between Amazon wet and dry seasons. Mean Doppler velocity (Vdop) values simulated using LESs are compared with 2-year measurements from a radar wind profiler (RWP) as viewed by statistical composites separated according to wet- and dry-season conditions. In the observed RWP and simulated LES Vdop composites, we find more intense low-level updraft velocity, vigorous graupel generation, and intense surface rain during the dry periods compared with the wet periods. To investigate coupled updraft–microphysical processes further, single-day golden cases are selected from the wet and dry periods to conduct detailed cumulus thermal tracking analysis. Tracking analysis reveals that simulated dry-season environments generate more droplet-loaded low-level thermals than wet-season environments. This tendency correlates with seasonal contrasts in buoyancy and vertical moisture advection profiles in large-scale forcing. Employing a normalized time series of mean thermal microphysics, the simulated cumulus thermals appear to be the primary generator of cloud droplets. When subsequent thermals penetrate the ice crystal layer, droplets within the thermals interact with entrained ice crystals, which enhances riming in the thermals. This appears to be a production pathway of graupel/hail particles within simulated deep convective cores. In addition, less-diluted dry-case thermals tend to be elevated higher, and graupel grows further during sedimentation after spilling out from thermals. Therefore, greater concentrations of low-level moist thermals likely result in more graupel/hail production and associated dry-season convective vigor. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds. [ABSTRACT FROM AUTHOR]

A mobile sampler for the collection of aerosol particles on an uncrewed aerial vehicle (UAV) was developed and deployed during three consecutive Pallas Cloud Experiment campaigns in the vicinity of the Sammaltunturi Global Atmosphere Watch site (67°58' N, 24°7' E, 565 m above sea level). The sampler is designed to collect aerosol particles onto Nuclepore filters, which are subsequently analysed for the temperature-dependent number concentration of ice-nucleating particles of the sampled aerosol with the Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology (INSEKT). This setup is an easy and flexible way to connect INP concentration measurements with cloud microphysics. The sampler was flown with a fixed-wing UAV in different altitudes up to 1000 m above ground level. The total flight time ranges from 1 hour to more than 1.5 hours, depending on environmental conditions. Pressure, temperature and relative humidity are also measured to provide information about the meteorological flight conditions. The flow over the filter was maintained by a micro-diaphragm pump, providing around 10 standard litres per minute over a small filter (diameter of 25 mm) and around 11 standard litres per minute over a larger filter (diameter of 47 mm) at a pressure corresponding to 500 m above sea level. For a typical flight time of 1.5 hours, this results in a sampled air volume of about 930 to 1000 standard litres per flight, giving an INP detection limit of approximately 1.1 × 10−3 and 1.0 × 10−3 INPs per standard litre, respectively. For comparison to the flight results, a similar setup was deployed at ground level. The comparison shows a clear distinction from the water and handling blank background for both setups, proving the technical feasibility of the setups. Furthermore, for some flights, a shift between the two INP populations can be seen, indicating that ground-based INP measurements deviate from the samples collected on-board the UAV. [ABSTRACT FROM AUTHOR]

This study utilized the WRF model to investigate the track evolution and rapid intensification (RI) of Typhoon Doksuri (2023) as it moved across the Luzon Strait and through the South China Sea (SCS). The simulation results indicate that Doksuri has a smaller track sensitivity to the use of different physics schemes, while having a greater intensity sensitivity. Sensitivity numerical experiments with different physics schemes can well capture its northwestward movement in the first two days, but they predict less westward track deflection as the typhoon moves across the Luzon Strait and through the SCS. Moreover, all the experiments successfully simulated Doksuri's RI, albeit with quite different rates and a time lag of 12 h. Among different combinations of physics schemes, there exists an optimal set of cumulus parameterization and cloud microphysics schemes for track and intensity predictions. Doksuri's track changes as the typhoon moved across the Luzon Strait and through the SCS were influenced by the topographic effects of the terrain of the Philippines and Taiwan, to different extents. The track changes of Doksuri are explained by the wavenumber-one potential vorticity (PV) tendency budget from different physical processes, highlighting that the horizontal PV advection dominates the PV tendency throughout most of the simulation time due to the offset of vertical PV advection and differential diabatic heating. In addition, this study applies the extended Sawyer–Eliassen (SE) equation to compare the transverse circulations of the typhoon induced by various forcing sources. The SE solution indicates that radial inflow was largely driven in the lower-tropospheric vortex by strong diabatic heating, while being significantly enhanced in the lower boundary layer due to turbulent friction. All other physical forcing terms were relatively insignificant for the induced transverse circulation. The coordinated radial inflow at low levels may have led to the eyewall development in unbalanced dynamics. Intense diabatic heating thus was vital to the severe RI of Doksuri under a weak vertical wind shear. [ABSTRACT FROM AUTHOR]