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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]

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]

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]

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]

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]

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]

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]

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]

Despite the fatal impact of heavy precipitation on people's lives and the social economy, its accurate estimating remains challenging. In this study, we show how to address this issue by kilometer-scale simulations and how to reduce computational costs in Northeast China with the complex terrain and distribution of land and sea. Three typical heavy precipitation events are simulated at 3 km horizontal resolution, and each event is simulated with 24 combinations of schemes (with or without a scale-aware cumulus scheme, three microphysics schemes, and four planetary boundary layer schemes), which are evaluated against gauge observations. Compared to gauge observations, the ensemble mean of simulations of hourly maximum precipitation and average accumulated precipitation outperforms three widely accepted satellite products in the cold vortex and the snowstorm case, and is of comparable accuracy in the typhoon case. Overall, the microphysics scheme significantly impacts the maximum hourly precipitation, whereas the planetary boundary layer scheme has a strong control over the accumulated precipitation. The similarity among different simulations is linked to the level of convective instability's impact on heavy precipitation in each case, which also indicates that conducting 24 simulations can be not necessary. This study uses an ensemble performance estimation technique assuming the impact of different schemes is additive and finds that performing 13 rather than 24 simulations allows finding the best-performing combination of parameterization schemes, which allows for saving almost 50% of computational costs. [ABSTRACT FROM AUTHOR]

The phenomenon of electric fields applied to droplets, inducing droplet coalescence, is called the electro-coalescence effect. An analytic expression for electro-coalescence with the accurate electrostatic force for a pair of droplets with opposite-sign charges is established by treating the droplets as conducting spheres (CSs). To investigate this effect, we applied a weak electric field to a cumulus cloud using a cloud model that employs the super-droplet method, a probabilistic particle-based microphysics method. This study employs a two-dimensional (2D) large-eddy simulation (LES) in a flow-coupled model to examine aerosol microphysics (such as collision–coalescence enhancement, velocity fluctuations, and supersaturation fluctuations) in warm cumulus clouds without relying on subgrid dynamics. In the simulation, we assume that droplets carry opposite-sign charges and are well mixed within the cloud. The charge is not treated as an individual particle attribute. To assess fluctuation effects, we conducted 50 simulations with varying pseudo-random number sequences for each electro-coalescence treatment. The results show that, with CS treatment, the electrostatic force contributes a larger effect on cloud evolution than in previous research. With a lower charge limit of the maximum charge amount on the droplet, the domain total precipitation with CS treatment for droplets with opposite signs is higher than that with the no-charge (NC) setting. Compared to previous work, the multi-image dipole treatment of CS results in higher precipitation. It is found that the electro-coalescence effect could affect rain formation even when the droplet charge is at the lower charge limit. High pollution levels result in greater sensitivity to electro-coalescence. The results show that, when the charge ratio between two droplets is over 100, the short-range attractive electric force due to the multi-image dipole would also significantly enhance precipitation for the cumulus. It is indicated that, although the accurate treatment of the electrostatic force with the CS method would require 30 % longer computation time than before, it is worthwhile to include it in cloud, weather, and climate models. [ABSTRACT FROM AUTHOR]

On the basis of a definition of ecology centred on Merleau-Ponty's thought, this essay examines the various phases of Foucauldian thought and their respective relationships to possible ecological outcomes: the Dionysian phase, which lasts until The Order of Things; the microphysics of power phase, in which a philosophy of the will that radically breaks with any idea of the original becomes central; and the late Foucault phase, characterised by the themes of the hermeneutics of the self, subjectivity and critique. In the latter period in particular, in which Foucault's rapprochement with Canguilhem and the idea of a living being immersed in a dialectical relationship with the environment and with others is very strong, a model is identified that is particularly amenable to interpretation in ecological terms. The essay concludes with some research hypotheses on the possible relationship between a philosophy of the will, such as that mediated by Foucault from Nietzsche and Schopenhauer, and ecology. [ABSTRACT FROM AUTHOR]

Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings. [ABSTRACT FROM AUTHOR]

Current general circulation models struggle to capture the phase-partitioning of clouds accurately, both overestimating and underestimating the supercooled liquid substantially. This impacts the radiative properties of clouds. Therefore, it is of interest to understand which processes determine the phase-partitioning. In this study, microphysical-process rates are analysed to study what role each phase-changing process plays in low-level Arctic clouds. Several months of cloud-resolving ICON simulations using a two-moment cloud microphysics scheme are evaluated. The microphysical-process rates are extracted using a diagnostic tool introduced here, which runs only the microphysical parameterization using previously simulated days. It was found that the processes impacting ice are more efficient during polar night than polar day. For the mixed-phase clouds (MPCs), it became clear that phase changes involving the vapour phase dominated in contrast to processes between liquid and ice. Computing the rate of the Wegener–Bergeron–Findeisen process further indicated that the MPCs frequently (42 % of the time) seemed to be glaciating. Additionally, the dependence of each process on the temperature, vertical wind, and saturation was evaluated. This showed that, in particular, the temperature influences the occurrence and interactions of different processes. This study helps to better understand how microphysical processes act in different regimes. It additionally shows which processes play an important role in contributing to the phase-partitioning in Arctic low-level mixed-phase clouds. Therefore, these processes could potentially be better targeted for improvements in the ICON model that aim to more accurately represent the phase-partitioning of Arctic low-level mixed-phase clouds. [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]

Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement: Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months. [ABSTRACT FROM AUTHOR]

The shortwave cloud radiative effect (SWCRE) is important for the Arctic surface radiation budget and is a major source of inter‐model spread in simulating Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, we extend the existing Approximate Partial Radiative Perturbation (APRP) method by adding the absorptivity for the upward beam, considering differences in reflectivity between upward and downward beams, and analyzing the cloud masking effect resulting from changes in surface albedo. Using data from CMIP model experiments, the study decomposes the SWCRE over the Arctic surface and analyzes inter‐model differences in quadrupled CO2 simulations. The study accounts for the influence of surface albedo, cloud amount, and cloud microphysics in the response of SWCRE to Arctic warming. In the sunlit season, CMIP models exhibit a strong, negative SWCRE with a large inter‐model spread. Arctic clouds dampen the surface albedo feedback by reflecting incoming solar radiation and further decrease the shortwave radiation reflected by surface, a fraction of which is scattered back to the surface by clouds. Specifically, this accounts for the majority of the inter‐model spread in SWCRE. In addition, increased (decreased) cloud amount and cloud liquid water reduce (increase) incoming shortwave fluxes at the surface, but they are found to be not critical to the Arctic surface radiation budget and its inter‐model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative processes, accurately decomposes the individual SWCRE responses at the Arctic surface. Plain Language Summary: Clouds have a significant impact on how much sunlight reaches the Arctic surface. Here, we refine the method for decomposing sunlight changes into contributions from the variations in surface albedo, cloud amount, and other properties of clouds. The refined method is applied to various climate models in which carbon dioxide is abruptly quadrupled, trying to understand the inter‐model differences in the cloud‐sunlight interaction over the Arctic surface. In the sunlit season, the decrease in surface albedo due to melting sea ice is directly linked to the sunlight reduction by cloud, explaining notable inter‐model differences in total sunlight mediated by the cloud. Cloud reflection reduces incoming solar radiation, thereby mitigating the influence of surface albedo with less solar radiation reaching the surface. Also, clouds reflect less shortwave radiation from the surface due to decreased surface albedo. Changes in cloud amount and liquid water, while affecting incoming shortwave fluxes, are not found to be critical to the changes in the Arctic surface radiation budget produced by climate models. Overall, the refined method proves useful for breaking down the complex interactions between clouds and sunlight. It is a practical tool for understanding individual components of cloud‐sunlight interaction at the Arctic surface. Key Points: A SW radiative decomposition estimates how albedo, cloud fraction, and cloud reflectivity drive responses in SWCRE to increases in CO2Changes in surface albedo mostly determine the magnitude and the inter‐model spread of SWCRE changes observed in the CMIP modelsChanges in cloud fraction and reflectivity in CMIP models show minimal impact on the changes in shortwave cloud radiative effect [ABSTRACT FROM AUTHOR]

Understanding the macro and micro characteristics of precipitation in landfall typhoons is crucial to predicting the path and intensity of tropical cyclones by using numerical models. In this study, we use observations from the Global Precipitation Measurement Mission to analyze the microphysics of convection and stratiform precipitation during the landfall of Typhoon Lekima. The statistical results show that the correlation coefficient of the reflectivity factors of the spaceborne and ground-based radars is 0.77 and that the water content detected by the 18.7 GHz low-frequency vertical channel is positively correlated with the intense-precipitation rate. The storm top height is generally consistent with the location of heavy precipitation. The average near-surface precipitation rate and liquid water content of convective precipitation are higher than those of stratiform precipitation. The average mass-weighted raindrop diameter and particle number concentration of convective (stratiform) precipitation at a distance of 2 km above ground level are 1.52 mm (1.29 mm) and 39.52 (36.44, in decibel scale), respectively. Below the melting layer, there is a significant increase in average particle diameter, indicating that the collision aggregation growth process of raindrops is dominant. These results are potentially helpful in validating and improving microphysics parameterization in numerical models. [ABSTRACT FROM AUTHOR]

Short gamma-ray bursts (GRBs) are explosive transients caused by binary mergers of compact objects containing at least one neutron star. Multiwavelength afterglow observations provide constraints on the physical parameters of the jet, its surrounding medium, and the microphysics of the enhanced magnetic fields and accelerated electrons in the blast wave at the front of the jet. The synchrotron radio emission can be tracked for much longer than in other spectral regimes, and it can pin down the evolution of the spectral peak. We present the results of a systematic observing campaign of eight short GRBs with the MeerKAT radio telescope. Additionally, we present observations of four of these short GRBs using the ATCA radio telescope and two of these short GRBs with the e -MERLIN radio telescope. Using these results we report one possible detection of a short GRB afterglow from GRB 230217A and deep upper limits for the rest of our short GRB observations. We use these observations to place constraints on some of the physical parameters, in particular those related to electron acceleration, the circumburst density, and gamma-ray energy efficiency. We discuss how deeper observations with new and upgraded telescopes should be able to determine if the gamma-ray efficiency differs between long and short GRBs. We also report detections of the likely host galaxies for four of the eight GRBs and upper limits for another GRB, increasing the number of detected host galaxies in the radio with implications for the star formation rate in these galaxies. [ABSTRACT FROM AUTHOR]

Utilizing a decade-long observation from the spaceborne dual-frequency precipitation radar, this study investigates the seasonal variations and microphysical characteristics of precipitation in North China. The results elucidate that the mean storm top height (STH) attains its zenith during the summer, potentially linked to the pronounced strong evaporation and convection. An upsurge in STH is frequently correlated with the evolution of precipitation particles of greater dimensions for both convective and stratiform precipitation. This investigation further discerns that the predominant microphysical mechanisms underlying precipitation during the process from altitudes of 3 km to 1 km exhibit significant seasonal variations and are contingent upon the precipitation types. Collision and coalescence processes are identified as the predominant contributors to precipitation formation, whereas evaporative processes and particle size sorting are less significant. For the coalescence process, the lowest is from summer (29.24%) to autumn (38.01%), spring (50.84%) and winter (58.43%). Additionally, this study observes that the altitude of the melting layer in North China(3–4 km) is relatively lower than that in East China and Yangtze-Huai River Valley region(4.5 km), which may be ascribed to the higher latitude, resulting in comparatively lower temperatures aloft and thus a reduced height for the melting layer. Key Points: Elevating storm top height correlates positively with an upsurge in near-ground raindrop size, favoring the aggregation of larger raindrops. The melting layer of precipitation is lower than that in East China, which may be caused by higher latitude. Seasonal variations in the microphysical characteristics of convective and stratiform precipitation are systematically analyzed. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super‐particle model McSnow are used as training data. The ML performs a coarse‐graining of the particle‐resolved microphysics to multi‐category two‐moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML‐based bulk model. The ML‐based scheme is tested with simulations of increasing complexity. As a box model, the ML‐based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML‐based P3‐like scheme provides a more realistic extended stratiform region when compared to the standard two‐moment bulk scheme in ICON. In a realistic case study, the ML‐based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse‐grain super‐particle simulations to a bulk scheme of arbitrary complexity. Plain Language Summary: Numerical weather prediction and climate models need a description of unresolved cloud microphysical processes. Such microphysical parameterizations are usually formulated as systems of equations for bulk variables that describe the time evolution of clouds and precipitation. In this study, we use machine learning (ML) techniques to build such a parameterization. As input or training data simulations of a very detailed cloud model are used. This detailed model provides information not only on the mass and number of cloud particles but also other properties like the degree of melting or the mass of liquid drops frozen on the ice particles called rime mass. The ML approach can successfully construct the necessary statistical relations that are needed for microphysical parameterization. This parameterization is then tested in simulations of increasing complexity. The new ML‐based scheme provides physically reasonable solutions and improves the simulation of a line of thunderstorms. Key Points: Machine learning (ML) is successfully applied to build a complex bulk ice microphysics scheme by coarse‐graining output of a Lagrangian particle microphysics modelThe ML‐based P3‐like microphysics scheme improves the representation of the stratiform region of an idealized squall line compared to a classic two‐moment schemeThe ML‐based P3‐like microphysics scheme runs stable and provides meaningful results in three‐dimensional real‐case simulations [ABSTRACT FROM AUTHOR]

Convection-permitting modeling allows us to understand mechanisms that influence rainfall in specific regions. However, microphysics parameterization (MP) and planetary boundary layer (PBL) schemes remain an important source of uncertainty, affecting rainfall intensity, occurrence, duration, and propagation. Here, we study the sensitivity of rainfall to three MP [Weather Research and Forecasting (WRF) Single-Moment 6-class (WSM6), Thompson, and Morrison] and two PBL [the Yonsei University (YSU) and Mellor–Yamada Nakanishi Niino (MYNN)] schemes with a convection-permitting resolution (4 km) over northwestern South America (NWSA). Simulations were performed by using the WRF model and the results were evaluated against soundings, rain gauges, and satellite data, considering the spatio-temporal variability of rainfall over diverse regions prone to deep convection in NWSA. MP and PBL schemes largely influenced simulated rainfall, with better results for the less computationally expensive WSM6 MP and YSU PBL schemes. Regarding rain gauges and satellite estimates, simulations with Morrison MP overestimated rainfall, especially westward of the Andes, whereas the MYNN PBL underestimated precipitation in the Amazon–Savannas flatlands. We found that the uncertainty in the rainfall representation is highly dependent on the region, with a higher influence of MP in the Colombian Pacific and PBL in the Amazon–Savannas flatlands. When analyzing rainfall-related processes, the selection of both MP and PBL parameterizations exerted a large influence on the simulated lower tropospheric moisture flux and moisture convergence. PBL schemes significantly influenced the downward shortwave radiation, with MYNN simulating a greater amount of low clouds, which decreased the radiation income. Furthermore, latent heat fluxes were greater for YSU, favoring moist convection and rainfall. MP schemes had a marked impact on vertical velocity. Specifically, Morrison MP showed stronger convection and higher precipitation rates, which is associated with a greater latent heat release due to solid-phase hydrometeor formation. This study provides insights into assessing physical parameterizations in numerical models and suggests key processes for rainfall representation in NWSA. [ABSTRACT FROM AUTHOR]

A historic rainstorm occurred over Henan, China, in July 2021 ("7.20" extreme precipitation event), resulting in significant human casualties and socioeconomic losses. A global variable-resolution model (MPAS-Atmosphere v7.3) was employed to simulate this extreme precipitation event. A series of simulations have been done at both quasi-uniform (60 and 15 km) and variable-resolution (60–15 and 60–3 km) meshes from hydrostatic to nonhydrostatic scale with two parameterization scheme suites. For the 48 h peak precipitation duration (20–22 July), the 60–3 km variable-resolution simulation coupled with the scale-aware convection-permitting parameterization scheme suite stands out predominantly among other simulation experiments as it reproduces this extreme precipitation event most accurately. At 15 km resolution, the 60–15 km variable-resolution simulation achieves comparable forecasting skills to the 15 km quasi-uniform simulation but at a much reduced computing cost. In addition, we found that the default mesoscale suite generally outperforms the convection-permitting suite at 15 km resolution as simulations coupled with the convection-permitting suite missed the third peak of this extreme precipitation event, while the mesoscale suite did not. Furthermore, it is found that the large-scale circulation plays a critical role in the peak precipitation simulations at 15 km resolution, via influencing the simulated low-level wind. During the second peak precipitation period, simulations with the convection-permitting parameterization scheme suite at 15 km resolution generate a prominent low-level easterly wind component bias, which is largely attributed to the excessively evaporative cooling in the lower troposphere. This study further reveals that at 15 km resolution the diabatic heating from the grid-scale precipitation accounts more for the low-level wind bias than the convective-scale precipitation. Given that two different cloud microphysics schemes, namely Thompson and WSM6 schemes, are used in the convection-permitting and default mesoscale parameterization scheme suites, respectively, these microphysics schemes are found to be the primary contributor to the low-level wind simulation bias. [ABSTRACT FROM AUTHOR]

This article gives the overview of the Japan Aerospace Exploration Agency (JAXA) level 2 (L2) Standard and Research algorithms and products by Japanese science teams for EarthCARE Clouds, Aerosols and Radiation Explorer (EarthCARE), which is a JAXA and the European Space Agency (ESA) joint satellite mission. First three single sensor algorithms for 94GHz cloud profiling radar (CPR)-only, 355nm-atmospheric lidar with high spectral resolution function (ATLID)-only, and multi-spectral imager (MSI)-only retrievals, and their products were briefly reviewed. CPR-echo algorithms provide radar reflectivity factor, Doppler velocity, normalized radar cross section and path integral attenuation. CPR-only, CPR-ATLID synergy and CPR-ALTID-MSI synergy algorithms for standard cloud products provide cloud detection, cloud particle type and cloud microphysics, and the research products further provide Doppler velocity, terminal velocity and vertical air motion inside clouds. ATLID standalone algorithms produce aerosol, cloud and clear sky classification products as well as total aerosol extinction and extinction and number concentration of each aerosol types. ATLID-MSI synergy algorithms are developed to retrieve effective radius for each aerosol species in addition to the ATLID-only products. MSI algorithms retrieve cloud effective radius, ice and water content and cloud top pressure. Four sensor algorithms are prepared to produce shortwave and longwave radiative fluxes at the top of atmosphere, those at the surface and also heating rate profiles by using the outputs from CPR, ATLID and their synergy algorithms. The shortwave and longwave fluxes from the four sensor algorithms will then be compared with broad band radiation (BBR) to examine the consistency of the JAXA L2 retrievals. The algorithms are developed and evaluated by using observational data from satellites and ground-based instruments, and simulation data from the Japanese global cloud-resolving model, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with Joint simulator. As for space-borne data, existing space-borne satellites data such as CloudSat, CALIPSO, MODIS and CERES datasets are intensively used. For ground-based observations, High-sensitivity Ground-based Super Polarimetric Ice-crystal Detection and Explication Radar (HG-SPIDER) with a minimum sensitivity of -40 dBZ at 15 km and over -60 dBZ at 1 km, Electronic Scanning SPIDER (ES-SPIDER), 355 nm high spectral resolution lidar, multiple-field-of-view multiple scattering lidar and Doppler lidars are installed at EarthCARE super-site in Koganei, Tokyo and offers unique opportunities to evaluate and analyse EarthCARE data. [ABSTRACT FROM AUTHOR]

With the changing climate, the study of fog formation is essential due to the impact of the complexity of natural and anthropogenic aerosols. The evolution of the droplet size distribution in the presence of different aerosol species remains poorly understood. To make progress towards reducing the uncertainty of fog forecasts, the Eulerian–Lagrangian particle‐based small‐scale model for the diffusional growth of droplets is used to better understand the droplet activation and growth. The small‐scale model simulations are performed using observed data from the Winter Fog Experiment study over Indira Gandhi International Airport, New Delhi. The microphysical properties, such as droplet number concentrations (Nd) and liquid water content (LWC), important for fog simulation, are evaluated to gain more insights. The small‐scale simulations have shown the droplet microphysical properties at different evolutionary stages. The Nd and effective radius reff$$ {r}_{\mathrm{eff}} $$ change with variations in LWC for different aerosol chemistries (i.e., organics, mix, and inorganic). The calculated visibility at small scale is also shown with the variation of Nd and LWC. This study compared visibility from an existing parametrization with parcel–direct numerical simulation calculation. The hygroscopicity κ$$ \kappa $$, which is highly related to the activation of aerosols to condensation nuclei, is taken into account to demonstrate the contribution of aerosol chemistry to fog droplet formation. The results highlight that hygroscopicity is essential in the numerical model for fog and visibility prediction as the microphysical properties of fog are regulated by aerosol species. [ABSTRACT FROM AUTHOR]

QCD with the isospin chemical potential μ I is a useful laboratory to delineate the microphysics in dense QCD. To study the quark–hadron continuity, we use a quark–meson model that interpolates hadronic and quark matter physics at microscopic level. The equation of state is dominated by mesons at low density but taken over by quarks at high density. We extend our previous studies with two flavors to the three-flavor case to study the impact of the strangeness, which may be brought by kaons (K + , K 0) = (u s ¯ , s d ¯) and the UA(1) anomaly. In the normal phase, the excitation energies of kaons are reduced by μ I in the same way as hyperons in nuclear matter at the finite baryon chemical potential. Once pions condense, kaon excitation energies increase as μ I does. Moreover, strange quarks become more massive through the UA(1) coupling to the condensed pions. Hence, at zero and low temperature, the strange hadrons and quarks are highly suppressed. The previous findings in two-flavor models, sound speed peak, negative trace anomaly, gaps insensitive to μ I , persist in our three-flavor model and remain consistent with the lattice results to μ I ∼ 1 GeV. We discuss the non-perturbative power corrections and quark saturation effects as important ingredients to understand the crossover equations of state measured on the lattice. [ABSTRACT FROM AUTHOR]

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement: Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment. [ABSTRACT FROM AUTHOR]

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

Bulk microphysics schemes continue to face challenges due in part to the necessary simplification of hydrometeor properties and processes that is inherent to any parameterization. In all operational bulk schemes, one such simplification is the division of liquid water into two subcategories (cloud and rain) when predicting the evolution of warm clouds. It was previously found that biases in collisional growth in a bulk scheme with these separate liquid water categories can be mitigated with a unified liquid water category in which cloud and rain are contained within the same category. In this study, we examine the effect of artificially separating the liquid water category on other microphysical processes and in more realistic settings. Both our idealized 1D and 3D results show that a unified category bulk scheme is fundamentally better at predicting the timing and intensity of rain from warm‐phase cumulus clouds compared to a traditional (separate) category bulk scheme. This is because a unified category bulk scheme allows a bimodal distribution to exist within one traditional "rain" category, whereas separate category bulk scheme only have one mode per category. This advantage allows the unified bulk scheme to retain the information of the largest droplets even as they fall through a layer of small raindrops. A separate category bulk scheme fails to represent this bimodal feature in comparison. Plain Language Summary: Bulk microphysics schemes are fast enough for simulating the evolution of clouds in weather and climate prediction, but they also face problems when calculating the collision and falling of water droplets. Some of the problems might not come from our lack of cloud physics knowledge but how the schemes are designed. For example, bulk schemes artificially divide the liquid water category into smaller droplets ("cloud") and larger droplets ("rain"). It was found that the biases typically produced by a separate category bulk scheme can be fixed with a unified category bulk scheme. In this study, we look into how a unified category bulk scheme compares to a separate category one under more physical processes and model settings. We find that a unified category bulk scheme consistently outperforms a separate category one, especially when predicting the timing and heaviness of rainfall. This is because collision and falling of water droplets can create droplet size distributions with two peaks in the traditional cloud or rain category, which cannot be modeled with a separate category bulk scheme. Key Points: A unified category bulk scheme accurately predicts rainfall timing and intensity far better than a traditional separate category schemeSedimentation of bimodal rain distributions is poorly captured by a separate category scheme but well captured by a unified category oneThe improvement arises from more flexibility in size distributions despite the same number of total predicted moments in both schemes [ABSTRACT FROM AUTHOR]

This paper describes the addition of a stratospheric prognostic aerosol (SPA) capability – developed with the goal of accurately simulating sulfate aerosol formation and evolution in the stratosphere – in the Department of Energy (DOE) Energy Exascale Earth System Model, version 2 (E3SMv2). The implementation includes changes to the four-mode Modal Aerosol Module microphysics in the stratosphere to allow for larger particle growth and more accurate stratospheric aerosol lifetime following the Pinatubo eruption. E3SMv2-SPA reasonably reproduces stratospheric aerosol lifetime, burden, aerosol optical depth, and top-of-atmosphere flux when compared to remote sensing observations. E3SMv2-SPA also has close agreement with the interactive chemistry–climate model CESM2-WACCM (Community Earth System Model version 2–Whole Atmosphere Community Climate Model) – which has a more complete chemical treatment – and the observationally constrained, prescribed volcanic aerosol treatment in E3SMv2. Global stratospheric aerosol size distributions identify the nucleation and growth of sulfate aerosol from volcanically injected SO 2 from both major and minor volcanic eruptions from 1991 to 1993. The modeled aerosol effective radius is consistently lower than satellite and in situ measurements (max differences of ∼ 30 %). Comparisons with in situ size distribution samples indicate that this simulated underestimation in both E3SMv2-SPA and CESM2-WACCM is due to overly small accumulation and coarse-mode aerosols 6–18 months post-eruption, with E3SMv2-SPA simulating ∼ 50 % of the coarse-mode geometric mean diameters of observations 11 months post-eruption. Effective radii from the models and observations are used to calculate offline scattering and absorption efficiencies to explore the implications of smaller simulated aerosol size for the Pinatubo climate impacts. Scattering efficiencies at wavelengths of peak solar irradiance (∼ 0.5 µ m) are 10 %–80 % higher for daily samples in models relative to observations through 1993, suggesting higher diffuse radiation at the surface and a larger cooling effect in the models due to the smaller simulated aerosol; absorption efficiencies at the peak wavelengths of outgoing terrestrial radiation (∼ 10 µ m) are 15 %–40 % lower for daily samples in models relative to observations, suggesting an underestimation in stratospheric heating in the models due to the smaller simulated aerosol. These potential biases are based on aerosol size alone and do not take into account differences in the aerosol number. The overall agreement of E3SMv2-SPA with observations and its similar performance to the well-validated CESM2-WACCM makes E3SMv2-SPA a viable alternative to simulating climate impacts from stratospheric sulfate aerosols. [ABSTRACT FROM AUTHOR]

The super-droplet method (SDM) is a Lagrangian particle-based numerical scheme for cloud microphysics. In this work, a series of simulations based on the DYCOMS-II (RF02) setup with different horizontal and vertical resolutions are conducted to explore the grid convergence of the SDM simulations of marine stratocumulus. The results are compared with the double-moment bulk scheme (SN14) and model intercomparison project (MIP) results. In general, all SDM and SN14 variables show a good agreement with the MIP results and have similar grid size dependencies. The stratocumulus simulation is more sensitive to the vertical resolution than to the horizontal resolution. The vertical grid length DZ ≪ 2.5 m is necessary for both SDM and SN14. The horizontal grid length DX < 12.5 m is necessary for the SDM simulations. DX ≤ 25 m is sufficient for SN14. We also assess the numerical convergence with respect to the super-droplet numbers. The simulations are well converged when the super-droplet number concentration (SDNC) is larger than 16 super-droplets per cell. Our results indicate that the super-droplet number per grid cell is more critical than that per unit volume at least for the stratocumulus case investigated here. Our comprehensive analysis not only offers guidance on numerical settings essential for accurate stratocumulus cloud simulation but also underscores significant differences in liquid water content and cloud macrostructure between SDM and SN14. These differences are attributed to the inherent modeling strategies of the two schemes. SDM's dynamic representation of aerosol size distribution through wet deposition markedly contrasts with SN14's static approach, influencing cloud structure and behavior over a 6 h simulation. Findings reveal sedimentation's crucial role in altering aerosol distributions near cloud tops, affecting the vertical profile of cloud fraction (CF). Additionally, the study briefly addresses numerical diffusion's potential effects, suggesting further investigation is needed. The results underscore the importance of accurate aerosol modeling and its interactions with cloud processes in marine stratocumulus simulations, pointing to future research directions for enhancing stratocumulus modeling accuracy and predictive capabilities. [ABSTRACT FROM AUTHOR]

Extremely severe cyclonic storm (ESCS) 'Fani' formed in the North Indian Ocean and crossed at Puri in Orissa State on the east coast of India on 03 May 2019. In this study, we examine the sensitivity of convection permitting WRF simulations (3 km) of 'Fani' to cloud microphysics (CMP) schemes using radar and multi-satellite data products. Five CMP schemes, namely Thompson, Goddard, WSM6, Morrison and Lin are tested in WRF. Results show that the changes in the CMP schemes primarily affect the simulated intensity and have lesser impact on the track predictions. Simulations with Thompson followed by Goddard produced the best predictions for both track and intensity estimates. Our analysis reveals significant variations in vertical motions associated with Fani across different CMP schemes; the WSM6, Goddard and Lin schemes produced relatively stronger vertical motions. The explicit WRF simulations could reproduce the wind profiler radar observed intense convective motions during the transit of Fani between 1 and 2 May 2019 at Gadanki station. Experiments with Thompson and Goddard schemes simulated the mean vertical velocities in lower, middle and upper layers in better agreement with radar data. The Lin, WSM6 and Goddard CMP predicted stronger updraft velocities (~ 0.35 m/s); Thompson produced moderate updraft velocities (~ 0.25 m/s) in the upper troposphere over a relatively wider area of high theta-e (385–390 K) indicating the simulation of a convectively stronger and warmer core compared to Morrison. Our analysis suggests that the differences in vertical motions in various CMP simulations are mainly due to the variations in the warming in simulations. It has been found that WSM6, Lin and Goddard produced a deeper core (up to 200 hPa) with a stronger diabatic heating of ~ 6° C followed by Thompson, which simulated a moderately deep core extending to ~ 250 hPa with moderate heating of ~ 5 °C whereas Morrison produced a relatively weak core with a heating of ~ 4 °C limited to 300 hPa. The stronger simulated diabatic heating in Lin, WSM6 and Goddard produces stronger inflow, moisture convergence in the lower levels and stronger outflow and divergence in the upper levels leading to stronger convection in the core region in these cases. The Lin, WSM6 and Goddard mixed phase schemes with more solid hydrometeors simulated stronger radar reflectivities, and stronger eyewalls, due to more latent heat release leading to the development of a strong warm core in the upper troposphere and thus a stronger TC. [ABSTRACT FROM AUTHOR]

Martian CO2 ice clouds are intriguing features, representing a rare occurrence of atmospheric condensation of a major component. These clouds play a crucial role due to their radiative properties, interactions with surface, and coupling with microphysical cycles of aerosols. Observations have been limited, prompting modeling studies to understand their formation and dynamics. Here, we present the first high‐resolution 3D simulations of CO2 ice clouds using a Large‐Eddy Simulation (LES) model incorporating CO2 microphysics. We investigate cloud formation in idealized temperature perturbations in the polar night. A reference simulation with a −2K perturbation demonstrates that the formed CO2 ice cloud possesses a convective potential, leading to its ascent in the troposphere. We determine the timescales and orders of magnitude of various phenomena involved in the lifecycle of a CO2 ice cloud. Sensitivity tests show that convection can be inhibited or intensified by the thermodynamic and microphysical conditions of the simulated environment. Plain Language Summary: CO2 ice clouds have been observed in the Martian polar nights in the lower atmosphere. These clouds are extremely challenging to observe due to the lack of sunlight. However, they play a significant role in Mars' climate, especially through interactions with the surface and other atmospheric species. To study the formation processes of these clouds, we use a high‐resolution model. Even a relatively weak cooling can lead to the formation of a CO2 ice cloud with convective potential, enabling it to move vertically upward. We determine the characteristic times and altitudes of this phenomenon and investigate how they respond when atmospheric parameters, such as cooling temperature, the presence of extra dust, or horizontal winds are varied. Our findings show that some of these parameters can either inhibit or enhance convection development. Key Points: Coupling a convection model with a CO2 microphysics scheme can simulate the formation of convective CO2 ice clouds triggered by realistic perturbationsThese is a strong coupling between CO2 ice cloud convection and dust cycle in the troposphereThe convective process intensity depends strongly on the number of available condensation nuclei and the temperature of the perturbation [ABSTRACT FROM AUTHOR]

Approximately 150 Tg of water vapor and 0.42 Tg of sulfur dioxide were injected directly into the stratosphere by the January 2022 Hunga volcanic eruption, which represents the largest water vapor injection in the satellite era. A comparison of numerical simulations to balloon‐borne and satellite observations of the water‐rich plume suggests that particle coagulation contributed to the Hunga aerosol's effective dry radius increase from 0.2 μm in February to around 0.4 μm in March. Our model suggests that the stratospheric aerosol effective radius is persistently perturbed for years by moderate and large‐magnitude volcanic events, whereas extreme wildfire events show limited impact on the stratospheric background particle size. Our analysis further suggests that both the particle optical efficiency and the aerosols' stratospheric lifetime explain Hunga's unusually large aerosol optical depth per unit of the SO2 injection, as compared with the Pinatubo eruption. Plain Language Summary: The Hunga Tonga‐Hunga Ha'apai (HTHH) submarine volcano erupted in January 2022, injecting a modest amount of SO2 but a record amount of water vapor relative to other eruptions into the mid‐stratosphere observed in the satellite era. Our climate model simulations of the stratospheric aerosol suggest that the large water vapor injection caused the new particle formation of many new sulfuric acid solution particles with small radius, enhancing the collision efficiency. Rapid collision led to a peak radius of 0.2 μm by February and 0.4 μm by March of 2022. Our analysis finds that both particle optical properties and aerosol persistence collectively exert an influence on normalized anomaly aerosol optical depth, explaining the high aerosol optical depth per unit emission of SO2 by HTHH, relative to the 1991 Pinatubo eruption. Key Points: Co‐injected water vapor triggered a sudden and large production of aerosols, followed by a fast particle growth via coagulationSimulations and observations from La Réunion and Lauder suggest that the stratospheric aerosol size was elevated for 2 yearsBoth the high optical efficiency and long stratospheric lifetime of HTHH aerosol contribute to a higher AOD per unit emission than Pinatubo [ABSTRACT FROM AUTHOR]

Mixed‐phase stratiform clouds contain numerous liquid, mixed‐phase, and ice clusters, quantifying the cluster scales is potentially helpful to improve the parameterizations of microphysics and radiation models. However, the scales of hydrometeor clusters at different levels of stratiform clouds are not well understood. In this study, using airborne measurements and a large eddy simulation, we show that turbulence plays an important role in controlling the clusters with length of a few hundred meters, while the scales of larger clusters have stronger vertical variations from cloud base to top. The liquid clusters are the largest near the cloud top, while the lengths of ice clusters decrease from cloud base to top. The lengths of mixed‐phase clusters depend on the glaciation process, a faster glaciation results in smaller mixed‐phase clusters. The results improve our understanding on how the liquid and ice are mixed at different levels in stratiform clouds. Plain Language Summary: Mixed‐phase clouds are vital to the global radiative balance and water cycle. The coexistence of liquid and ice, and the way they mix have significant impacts on the cloud microphysical properties. Observations suggest hydrometeors organize in clusters in mixed‐phase stratiform clouds, while such inhomogeneity is still poorly represented in models. In this study, using airborne measurements and model simulation, we analyzed the scales of liquid, mixed‐phase, and ice clusters at different levels in a stratiform cloud observed in Northeast China. The results suggest turbulence controls the scale of clusters with length of a few hundred meters, while larger clusters have stronger vertical variations. The results improve our understanding on the cluster scales of hydrometeors at different levels in stratiform clouds, and are potentially helpful to improve the parameterizations of microphysics and radiation. Key Points: Turbulence plays an important role in controlling the scale of hydrometeor clustersThe scales of liquid, mixed‐phase, and ice clusters have different height dependencies from cloud top to baseA faster glaciation process results in smaller mixed‐phase clusters [ABSTRACT FROM AUTHOR]

Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility. Plain Language Summary: Uncertainty in predicting future global temperature inferred from the historical record of warming is dominated by how much the warming due to greenhouse gases has been offset by the cooling due to aerosols. Aerosols are small liquid and solid particles that play an important role in cloud formation. The majority of cooling from aerosols is through reflecting incoming solar radiation back to space by cloud. In this study, we constrain an ensemble of possible global model configurations with observations of cloud properties and radiation to reduce uncertainty in the response of clouds and ultimately radiation to anthropogenic aerosol. While observations substantially reduce the uncertainty in both changes in the number of droplets and amount of liquid cloud, the constraint on aerosol cooling is minimal. We argue that the relatively weak constraint is because large changes in cloudiness are accompanied by small change in reflected sunlight due to increased cloudiness. Key Points: Models with lower precipitation efficiency result in larger mean‐state liquid water path (LWP) and larger adjustments in LWP to aerosolLarger mean‐state LWP coincides with lower albedo susceptibility to LWPThe combination of these processes results in buffering of the radiative effect of LWP adjustments [ABSTRACT FROM AUTHOR]

Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation nuclei (CCN) on electrical activities in thunderstorm clouds. The bulk thunderstorm models use mean sizes of particles and terminal-fall velocities. This causes calculation deviation in the electrification simulation, which in turn leads to deviations in the simulation of lightning processes. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities. For evaluating the impacts of aerosol particles, specifically CCN, on the properties of continental thunderclouds, aerosols from both clean and polluted continental environments were selected. Cloud simulations indicate that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases the number of small ice crystals compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. Consequently, a significant decrease in large ice particles leads to a weakened process of charge separation under conditions of pollution. As a direct result, there is about a 43% reduction in lightning frequency and a delay of approximately 5 min in the lightning process under polluted conditions. [ABSTRACT FROM AUTHOR]

Small-scale dark matter structures lighter than a billion solar masses are an important probe of primordial density fluctuations and dark matter microphysics. Due to their lack of starlight emission, their only guaranteed signatures are gravitational in nature. We report on results of a search for astrometric weak lensing by compact dark matter subhaloes in the Milky Way with Gaia DR3 data. Using a matched-filter analysis to look for correlated imprints of time-domain lensing on the proper motions of background stars in the Magellanic Clouds, we exclude order-unity substructure fractions in haloes with masses Ml between 107 and |$10^9 \, {\rm M}_\odot$| and sizes of one parsec or smaller. We forecast that a similar approach based on proper accelerations across the entire sky with data from Gaia DR4 may be sensitive to substructure fractions of fl ≳ 10−3 in the much lower mass range of |$10 \, {\rm M}_\odot \lesssim M_l \lesssim 3 \times 10^3 \, {\rm M}_\odot$|⁠. We further propose an analogous technique for stacked star–star lensing events in the regime of large impact parameters. Our first implementation is not yet sufficiently sensitive but serves as a useful diagnostic and calibration tool; future data releases should enable average stellar mass measurements using this stacking method. 1 [ABSTRACT FROM AUTHOR]

We explore the domain-specific Python library GT4Py (GridTools for Python) for implementing a representative physical parametrization scheme and the related tangent-linear and adjoint algorithms from the Integrated Forecasting System (IFS) of ECMWF. GT4Py encodes stencil operators in an abstract and hardware-agnostic fashion, thus enabling more concise, readable and maintainable scientific applications. The library achieves high performance by translating the application into targeted low-level coding implementations. Here, the main goal is to study the correctness and performance-portability of the Python rewrites with GT4Py against the reference Fortran code and a number of automatically and manually ported variants created by ECMWF. The present work is part of a larger cross-institutional effort to port weather and climate models to Python with GT4Py. The focus of the current work is the IFS prognostic cloud microphysics scheme, a core physical parametrization represented by a comprehensive code that takes a significant share of the total forecast model execution time. In order to verify GT4Py for Numerical Weather Prediction (NWP) systems, we put additional emphasis on the implementation and validation of the tangent-linear and adjoint model versions which are employed in data assimilation. We benchmark all prototype codes on three European supercomputers characterized by diverse GPU and CPU hardware, node designs, software stacks and compiler suites. Once the application is ported to Python with GT4Py, we find excellent portability, competitive performance, and robust execution in all tested scenarios including with reduced precision. [ABSTRACT FROM AUTHOR]

Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs. [ABSTRACT FROM AUTHOR]

The Cloud Profiling Radar (CPR) of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) has a new capability to observe the Doppler velocity related to the vertical air motion of the terminal velocity of hydrometeors. The new observation from space will be used to evaluate and improve the model. Before the launch of EarthCARE, we need to develop a methodology for using the CPR data for model evaluations. In this study, we evaluated simulated data by a stretched version of the global non-hydrostatic model over Japan with a ground-based CPR using an instrument design similar to the EarthCARE CPR. We chose two cases with different precipitation events in September 2019 using two cloud microphysics schemes. We introduced the categorization method for evaluating microphysics using Doppler velocity. The results show that the liquid and solid phases of hydrometeors are divided in Doppler velocity, and the model's terminal velocities of rain, snow, and graupel categories can be evaluated with the observation. The results also show that the choice of microphysics scheme has a more significant impact than the dependence on precipitation cases. We discussed the application of the EarthCARE-like simulation results using a satellite simulator. [ABSTRACT FROM AUTHOR]

In cloudy situations, infrared (IR) and microwave (MW) observations are complementary, with infrared observations being sensitive to small cloud droplets and ice particles and with microwave observations being sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g.,). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer (RT) for TIROS Operational Vertical Sounder (RTTOV v13) are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated, (ii) with only one source of errors considered at a time and (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of 15 d in summer and 15 d in winter over the Atlantic Ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of the strengths of both spectral ranges, leading to more accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that, in all cases explored, even though the radiative transfer and numerical modeling errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, the compromise remains positive. [ABSTRACT FROM AUTHOR]