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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]

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]

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]

The clouds have a great impact on Venus's energy budget and climate evolution, but its three‐dimensional structure is still not well understood. Here we incorporate a simple Venus cloud physics scheme into a flexible GCM to investigate the three‐dimensional cloud spatial variability. Our simulations show good agreement with observations in terms of the vertical profiles of clouds and H2SO4 vapor. H2O vapor is overestimated above the clouds due to efficient transport in the cloud region. The cloud top decreases as latitude increases, qualitatively consistent with Venus Express observations. The underlying mechanism is the combination of H2SO4 chemical production and meridional circulation. The mixing ratios of H2SO4 at 50–60 km and H2O vapors in the main cloud deck basically exhibit maxima around the equator, due to the effect of temperature's control on the saturation vapor mixing ratios of the two species. The cloud mass distribution is subject to both H2SO4 chemical production and dynamical transport and shows a pattern that peaks around the equator in the upper cloud while peaks at mid‐high latitudes in the middle cloud. At low latitudes, H2SO4 and H2O vapors, cloud mass loading and acidity show semidiurnal variations at different altitude ranges, which can be validated against future missions. Our model emphasizes the complexity of the Venus climate system and the great need for more observations and simulations to unravel its spatial variability and underlying atmospheric and/or geological processes. Plain Language Summary: On Venus, highly reflective clouds cover the surface entirely. This means that the clouds greatly impact Venus's current and, very likely, past energy budget. Therefore, understanding the Venus clouds is essential to constructing a full paradigm of the Venus climate and evolution. However, due to the lack of both three‐dimensional, long‐term observations and comprehensive climate models, the cloud spatial structure and its impact on atmospheric processes remain elusive. Here, we construct a three‐dimensional climate model that includes cloud physics and simple chemistry as the first step toward fully understanding the Venus clouds. Our simulated vertical profiles of the Venus clouds agree well with observations. We find that the condensable gases, sulfuric acid and water vapors in the cloud region become more abundant in the lower latitudes due to the temperature difference over different latitudes. The cloud top becomes lower as it approaches the polar region, and the underpinning processes are related to sulfuric acid chemical production and meridional circulation. The equatorial cloud structure shows semidiurnal features, which are related to the excited thermal tides in the Venus atmosphere. Our study is preparing for future Venus missions like EnVision, to maximize their science returns. Key Points: We construct a Venus climate model with cloud physics, and the cloud vertical structure agrees with observationsH2SO4 and H2O vapors in the middle cloud basically follow their SVMRs and show higher concentrations at low latitudesThe semidiurnal thermal tide affects H2SO4 and H2O vapors, cloud mass loading and acidity at different altitudes [ABSTRACT FROM AUTHOR]

The total aviation effective radiative forcing is dominated by non-CO2 effects. The largest contributors to the non-CO2 effects are contrails and contrail cirrus. There is the possibility of reducing the climate effect of aviation by avoiding flying through ice-supersaturated regions (ISSRs), where contrails can last for hours (so-called persistent contrails). Therefore, a precise prediction of the specific location and time of these regions is needed. But a prediction of the frequency and degree of ice supersaturation (ISS) on cruise altitudes is currently very challenging and associated with great uncertainties because of the strong variability in the water vapour field, the low number of humidity measurements at the air traffic altitude, and the oversimplified parameterisations of cloud physics in weather models. Since ISS is more common in some dynamical regimes than in others, the aim of this study is to find variables/proxies that are related to the formation of ISSRs and to use these in a regression method to predict persistent contrails. To find the best-suited proxies for regressions, we use various methods of information theory. These include the log-likelihood ratios, known from Bayes' theorem, a modified form of the Kullback–Leibler divergence, and mutual information. The variables (the relative humidity with respect to ice, RH iERA5 ; the temperature, T ; the vertical velocity, ω ; the divergence, DIV; the relative vorticity, ζ ; the potential vorticity, PV; the normalised geopotential height, Z ; and the local lapse rate, γ) come from ERA5, and RH iM/I , which we assume as the truth, comes from MOZAIC/IAGOS (Measurement of Ozone and Water Vapour on Airbus In-service Aircraft/In-service Aircraft for a Global Observing System; commercial aircraft measurements). It turns out that RH iERA5 is the most important predictor of ice supersaturation, in spite of its weaknesses, and all other variables do not help much to achieve better results. Without RH iERA5 , a regression to predict ISSRs is not successful. Certain modifications of RH iERA5 before the regression (as suggested in recent papers) do not lead to improvements of ISSR prediction. Applying a sensitivity study with artificially modified RH iERA5 distributions points to the origin of the problems with the regression: the conditional distributions of RH iERA5 (conditioned on ISS and non-ISS, from RH iM/I) overlap too heavily in the range of 70 %–100 %, so for any case in that range, it is not clear whether it belongs to an ISSR or not. Evidently, this renders the prediction of contrail persistence very difficult. [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere‐only, abrupt CO2 forced experiments that have prescribed sea‐surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea‐surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation. Plain Language Summary: In climate change, clouds change due to a variety of mechanisms including surface temperature, dynamical circulations, and radiative forcing. In this paper, we examine the latter: how clouds respond to radiative forcing. We study this topic using climate model simulations where the brightness of the sun is abruptly increased by 4% and compare those with simulations where CO2 concentration is abruptly quadrupled. In doing so we find that, there are differences in the cloud response to changes in solar and CO2 forcing which include the occurrence of thick and thin high cloud, as well as the amount and height of low and mid‐level clouds. Key Points: Increasing CO2 causes a reduction and lowering of mid‐level and low‐level clouds which does not occur from solar forcingThere is large reduction in optically thin high clouds from solar forcing, especially as compared with CO2Even after 150 years adjustments make a significant contribution to the total net cloud radiative effect [ABSTRACT FROM AUTHOR]

The ATLAS experiment at CERN relies on a Worldwide Distributed Computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud providers: lightweight configuration and operation, elasticity and availability of diverse infrastructures. This paper examines the seamless integration of cloud computing services as a conventional Grid site within the ATLAS workflow management and data management systems, while also offering new setups for interactive, parallel analysis. It underscores pivotal results that enhance the on-site computing model and outlines several R&D projects that have benefited from large-scale, elastic resource provisioning models. Furthermore, this study discusses the impact of cloud-enabled R&D projects in three domains: accelerators and AI/ML, ARM CPUs and columnar data analysis techniques. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

This study investigates the chemical composition and physical properties of aerosols, which play a crucial role in influencing human health, cloud physics, and local climate. Our focus centers on the hygroscopicity of ambient aerosols, a key property reflecting the ability to absorb moisture from the atmosphere and serve as cloud condensation nuclei. Employing home-built Air Quality Box (AQB) systems equipped with low-cost sensors, we assess the ambient variability of particulate matter (PM) concentrations to determine PM hygroscopicity. The AQB systems effectively captured meteorological parameters and most pollutant concentrations, with high correlations observed compared to Taiwan Environmental Protection Administration (EPA) data. With the application of κ-Köhler equation and certain assumptions, AQB-monitored PM concentrations are converted to dry particle mass concentration, showing improved correlation with EPA data and optical particles counter sensitivity correction. The derived κ values range from 0.15 to 0.29 for integrated fine particles (PM2.5) and 0.05 to 0.13 for coarse particles (PM2.5-10), consistent with results of ionic chromatography analysis for samples from a previous winter campaign nearby. Moreover, the analysis of PM10 division into PM2.5 and PM2.5-10, considering composition heterogeneity, provided improved dry PM10 concentration as the sensitivity coefficients for PM2.5-10 were notedly higher than for PM2.5. Our methodology provides a comprehensive approach to assess ambient aerosol hygroscopicity, offering significant implications for atmospheric modeling, particularly in evaluating aerosol efficiency as cloud condensation nuclei and in radiative transfer calculations. Overall, the AQB systems proved to be effective in monitoring air quality and deriving key aerosol properties, contributing valuable insights into atmospheric science. [ABSTRACT FROM AUTHOR]

Aerosols, as cloud condensation nuclei (CCN), impact cloud droplet spectrum and dispersion (ε), affecting precipitation and climate change. However, the influence of various aerosol modes on cloud physics remains controversial, and this effect varies with location and cloud type. This study uses a bin microphysics scheme (WRF-SBM) to simulate a warm stratiform cloud in Jiangxi, China. The numerical simulations reproduce the macro and microstructure of warm clouds compared with aircraft observations. Further experiments modifying the aerosol spectrum and number concentration indicate: increased aerosol concentration promotes cloud formation, raises cloud height, and broadens the cloud droplet spectrum. In contrast, a decrease in aerosol concentration suppresses cloud formation and development. Different aerosols have varying effects on the cloud droplet spectrum. Higher accumulation mode aerosol concentration increases small droplet concentration, while increased nucleation and coarse mode aerosol concentration favors larger droplet formation. Generally, the correlation between ε and volume-weighted particle size (rv) changes from positive to negative as rv increases. The transition in correlation is influenced by the relative strengths of cloud droplet collision, condensation, and activation processes. The increase in accumulation mode aerosol concentration strengthens the positive correlation between ε and rv in the rv range of 4.5–8 μm, while the decrease in concentration strengthens the negative correlation in the same range. Regardless of different coalescence intensity, ε converges with the increase in Nc. Changes in aerosol concentration for different modes do not alter the convergence trend of ε-Nc but only affect the dispersion of ε at low Nc levels. [ABSTRACT FROM AUTHOR]

The Sichuan-Xizang Railway is an important part of the railway network in China, and geological disasters, such as mountain floods and landslides, frequently occur in this region. Precipitation is an important cause of these disasters; therefore, accurate simulation of the precipitation in this region is highly important. In this study, the descriptions for uncertain processes in the cloud microphysics scheme are improved; these processes include cloud droplet activation, cloud-rain autoconversion, rain accretion by cloud droplets, and the entrainment-mixing process. In the default scheme, the cloud water content of different sizes corresponds to the same cloud droplet concentration, which is inconsistent with the actual content; this results in excessive cloud droplet size, unreasonable related conversion rates of microphysical process (such as cloud-rain autoconversion), and an overestimation of precipitation. Our new scheme overcomes the problem of excessive cloud droplet size. The processes of cloudrain autoconversion and rain accretion by cloud droplets are similar to the stochastic collection equation, and the mixing mechanism of cloud droplets is more consistent with that occurred during the actual physical process in the cloud. Based on the new and old schemes, multiple precipitation processes in the flood season of 2021 along the Sichuan-Xizang Railway are simulated, and the results are evaluated using ground observations and satellite data. Compared to the default scheme, the new scheme is more suitable for the simulation of cloud physics, reducing the simulation deviation of the liquid water path and droplet radius from 2 times to less than 1 time and significantly alleviating the overestimation of precipitation intensity and range of precipitation center. The average root-mean-square error is reduced by 22%. Our results can provide a scientific reference for improving precipitation forecasting and disaster prevention in this region. [ABSTRACT FROM AUTHOR]

High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation. Plain Language Summary: Stratocumulus clouds cover a large fraction of the globe but are very challenging to reproduce in computer simulations of Earth's atmosphere because of their unique complexity. Previous studies find the model produces too few Stratocumulus clouds as we increase the model resolution, which, in theory, should improve the simulation of important motions for the clouds. This is because the clouds are exposed to more conditions that make them evaporate away. On Earth, stratocumulus clouds reflect a lot of sunlight. In the computer model of Earth, too much sunlight reaches the surface because of too few stratocumulus clouds, which makes it warmer. This study tests two methods to thicken Stratocumulus clouds in the computer model Earth. The first method smooths out some winds, which helps reduce the exposure of clouds to the conditions that make them evaporate. The second method moves water droplets in the cloud away from the conditions that would otherwise make them evaporate. In long simulations, combining these methods helps the model produce thicker stratocumulus clouds with more water. Key Points: We improve a long‐standing stratocumulus (Sc) dim bias in a high‐resolution Multiscale Modeling FrameworkIncorporating intra‐CRM hyperviscosity hedges against the numerics of its momentum solver, reducing entrainment vicinityFurther adding sedimentation boosts Sc brightness close to observed, opening path to more faithful low cloud feedback analysis [ABSTRACT FROM AUTHOR]

This study leveraged a Lagrangian framework to examine the evolution of stratocumulus clouds under cold and warm advections (CADV and WADV) in the Community Earth System Model 2 (CESM2) against observations. We found that CESM2 simulates a too rapid decline in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) under CADV conditions, while it better aligns closely with observed LCF under WADV conditions but overestimates the increase in CLWP. Employing an explainable machine learning approach, we found that too rapid decreases in LCF and CLWP under CADV conditions are related to overestimated drying effects induced by sea surface temperature, whereas the substantial increase in CLWP under WADV conditions is associated with the overestimated moistening effects due to free‐tropospheric moisture and surface winds. Our findings suggest that overestimated drying effects of sea surface temperature on cloud properties might be one of crucial causes for the high equilibrium climate sensitivity in CESM2. Plain Language Summary: Stratocumulus clouds have extensive coverage over the oceans and modulate the climate system by efficiently reflecting incoming solar radiation back to space. However, their simulations in climate models are challenging due to complex meteorological controls, in which temperature advection is one of the most uncertain controlling factors. To enhance our understanding, we examine the stratocumulus evolution influenced by cold‐advection (CADV) and warm‐advection (WADV) in the midlatitudes in a climate model, CESM2. A too rapid decrease in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) is erroneously simulated under CADV conditions, while an increase in CLWP is substantially overestimated under WADV conditions. Using an explainable machine learning approach, these errors are found to be caused by the amplified drying or moistening effects due to improper treatments of meteorological controls on clouds in CESM2. This study suggests that these misrepresentations of cloud physics in the midlatitudes should be imperatively improved to reduce climate prediction uncertainties. Key Points: Community Earth System Model 2 simulates a too rapid decline in low‐cloud fraction and cloud liquid water path when clouds experience cold‐air advectionIt substantially overestimates increases in cloud liquid water path when clouds experience warm‐air advectionUtilizing an explainable machine learning methodology, the effects of meteorological factors on cloud evolution are assessed [ABSTRACT FROM AUTHOR]

The total aviation effective radiative forcing is dominated by non − CO2 effects. The largest contributors to the non − CO2 effects are contrails and contrail cirrus. There is the possibility of reducing the climate effect of aviation by avoiding flying through ice supersaturated regions (ISSRs), where contrails can last for hours (so-called persistent contrails). Therefore, a precise prediction of the specific location and time of these regions is needed. But a prediction of the frequency and degree of ice supersaturation (ISS) on cruise altitudes is currently very challenging and associated with great uncertainties because of the strong variability of the water vapour field, the low number of humidity measurements at air traffic altitude, and the oversimplified parameterisations of cloud physics in weather models. Since ISS is more common in some dynamical regimes than in others, the aim of this study is to find variables/proxies that are related to the formation of ISSRs and to use these for a regression method to predict persistent contrails. To find the best suited proxies for regressions, we use various methods of information theory. These include the log-likelihood ratios, known from the Bayes' theorem, a modified form of the Kullback-Leibler divergence and the mutual information. The variables (the relative humidity with respect to ice RHi ERA5, the temperature T , the vertical velocity ω, the divergence DIV , the relative vorticity ζ, the potential vorticity PV , the normalised geopotential height Z and the local lapse rate γ) come from ERA5 and RHi M/I, which we assume as the truth, comes from MOZAIC/IAGOS (commercial aircraft measurements). It turns out, that RHi ERA5 is the most important predictor of ice supersaturation, in spite of its weaknesses, and all other variables do not help much to achieve better results. Without RHi ERA5, a regression to predict ISSRs is not successful. Certain modifications of RHi ERA5 before the regression (as suggested in recent papers) do not lead to improvements of ISSR prediction. Applying a sensitivity study with artificially modified RHi ERA5 distributions point to the origin of the problems with the regression: the conditional distributions of RHi ERA5 (conditioned on ISS and non-ISS, from RHi M/I) overlap too heavily in the range 70–100 %, such that for any case in that range it is not clear whether it belongs to an ISSR or not. Evidently, this renders the prediction of contrail persistence very difficult. [ABSTRACT FROM AUTHOR]

Thunderstorms can generate intense electrical activity, hail, and result in substantial economic and human losses. The development of very short-term forecasting tools (nowcasting) is essential to provide information to alert systems in order to mobilize most efficiently the population. However, the development of nowcasting tools depends on a better understanding of the physics and microphysics of clouds and lightning formation and evolution. In this context, the objectives of this study are: (a) to describe the environmental conditions that led to a genesis of a thunderstorm that produce hail on 7 January 2012, in the Metropolitan Area of São Paulo (MASP) during the CHUVA-Vale campaign, and (b) to evaluate the thunderstorm microphysical properties and vertical structure of electrical charge. Data from different sources were used: field campaign data, such as S-band radar, and 2- and 3-dimensional lightning networks, satellite data from the Geostationary Operational Environmental Satellite-13 (GOES-13), the Meteosat Second Generation (MSG), and reanalysis of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The thunderstorm developed in a region of low-pressure due to the presence of a near-surface inverted trough and moisture convergence, which favored convection. Convective Available Potential Energy (CAPE) of 1053.6 J kg−1 at the start of the thunderstorm indicated that strong convective energy was present. Microphysical variables such as Vertically Integrated Liquid water content (VIL) and Vertically Integrated Ice (VII) showed peaks of 140 and 130 kg m−2, respectively, before the hail reached the surface, followed by a decrease, indicating content removal from within the clouds to the ground surface. The thunderstorm charge structure evolved from a dipolar structure (with a negative center between 4 and 6 km and a positive center between 8 and 10 km) to a tripolar structure (negative center between 6 and 7.5 km) in the most intense phase. The first lightning peak (100 flashes in 5 min−1) before the hail showed that there had been a lightning jump. The maximum lightning occurred around 18:17 UTC, with approximately 350 flashes 5 min−1 with values higher than 4000 sources 500 m−1 in 5 min−1. Likewise, the vertical cross-sections indicated that the lightning occurred ahead of the thunderstorm's displacement (maximum reflectivity), which could be useful in predicting these events. [ABSTRACT FROM AUTHOR]

Cloud-top phase (CTP) impacts cloud albedo and pathways for ice particle nucleation, growth, and fallout within extratropical cyclones. This study uses airborne lidar, radar, and Rapid Refresh analysis data to characterize CTP within extratropical cyclones as a function of cloud-top temperature (CTT). During the 2020, 2022, and 2023 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign deployments, the Earth Resources 2 (ER-2) aircraft flew 26 research flights over the northeast and midwest United States to sample the cloud tops of a variety of extratropical cyclones. A training dataset was developed to create probabilistic phase classifications based on Cloud Physics Lidar measurements of known ice and liquid clouds. These classifications were then used to quantify dominant CTP in the top 150 m of clouds sampled by the Cloud Physics Lidar in storms during IMPACTS. Case studies are presented illustrating examples of supercooled liquid water at cloud top at different CTT ranges (−3° < CTTs < −35°C) within extratropical cyclones. During IMPACTS, 19.2% of clouds had supercooled liquid water present at cloud top. Supercooled liquid was the dominant phase in extratropical cyclone cloud tops when CTTs were >−20°C. Liquid-bearing cloud tops were found at CTTs as cold as −37°C. Significance Statement: Identifying supercooled liquid cloud tops' frequency is crucial for understanding ice nucleation mechanisms at cloud top, cloud radiative effects, and aircraft icing. In this study, airborne lidar, radar, and model temperature data from 26 research flights during the NASA IMPACTS campaign are used to characterize extratropical cyclone cloud-top phase (CTP) as a function of cloud-top temperature (CTT). The results show that liquid was the dominant CTP present in extratropical cyclone cloud tops when CTTs were >−20°C with decreasing supercooled liquid cloud-top frequency at temperatures < −20°C. Nevertheless, liquid was present at CTTs as cold as −37°C. [ABSTRACT FROM AUTHOR]

The Department of Energy (DOE)'s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity (w') (gamma) and the damping of the w' skewness (c8) near the coast but become more sensitive to the parameter affecting the damping of the w' variance (c1) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model. [ABSTRACT FROM AUTHOR]

Cirrus dominate the longwave radiative budget of the tropics. For the first time, the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different microphysical schemes within nudged global storm‐resolving simulations from a single model, is quantified. Nudging allows us to compute radiative biases precisely using coincident satellite measurements and to fix the large‐scale dynamics across our set of simulations to isolate the influence of microphysics. We run 5‐day simulations with four commonly‐used microphysics schemes of varying complexity (SAM1MOM, Thompson, M2005 and P3) and find that the tropical average longwave CRE varies over 20 W m−2 between schemes. P3 best reproduces observed longwave CRE. M2005 and P3 simulate cirrus with realistic frozen water path but unrealistically high ice crystal number concentrations which commonly hit limiters and lack the variability and dependence on frozen water content seen in aircraft observations. Thompson and SAM1MOM have too little cirrus. Plain Language Summary: Recently, advancements in computing have made it possible for atmospheric scientists to simulate Earth's global atmosphere with higher resolution than ever before. This new generation of models, called global‐storm resolving models, have a horizontal grid spacing of just a few kilometers, which permits the formation of thunderstorms. As a result, they simulate clouds more realistically than traditionally climate and weather models and are a great tool for diagnosing cloud biases in atmospheric models. Here, we run a single global storm‐resolving model with four different representations of cloud physics called M2005, P3, SAM1MOM and Thompson. We evaluate simulated tropical cirrus, which are stratiform ice clouds at the top of the troposphere that reduce the amount of infrared radiation emitted by the Earth, with satellite and aircraft data to see which representations have the best performance. SAM1MOM and Thompson make too little cirrus causing too much infrared radiation to be emitted, M2005 makes too much cirrus, causing too little infrared radiation to be emitted, and P3 makes about the right amount. Key Points: Nudged global storm‐resolving simulations are valuable for microphysics sensitivity studiesMean tropical longwave cloud radiative effect varies over 20 W m−2 depending on microphysics schemeTwo‐moment schemes outperform simpler one‐moment and partial double‐moment schemes, and P3 has the smallest longwave radiative bias [ABSTRACT FROM AUTHOR]

Along-standing puzzle in cloud physics research is how clouds produce ice particles in concentrations that are orders of magnitude more than the concentration of ice nucleating particles (INPs), which are [...]

Results of Russian studies on cloud physics, precipitation, and weather modification in 2019–2022 are presented based on a survey prepared for the Russian National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics. Results concerning general issues of the observation and modeling of clouds and precipitation, including convective clouds; issues of studying the microphysical and optical characteristics of clouds; and the weather modification are discussed. [ABSTRACT FROM AUTHOR]

The microphysical characteristics of fog and stratiform clouds are somewhat similar. The study of the microphysical characteristics of warm fog, fog droplet spectral relative dispersion, and their influencing factors can deepen our understanding of the variability and influencing factors of cloud droplet spectral relative dispersion, while also investigating the formation and maintenance mechanisms of fog. This is currently a scientific issue that still remains controversial in cloud physics research and climate prediction. In this paper, we analyzed three months of Xishuangbanna radiation fog observations to explore the microphysical characteristics of fog. The results show followings: (1) When the autoconversion threshold (T) increased to greater than 0.4, the positive correlation between the relative dispersion of fog droplet spectrum and the volume mean diameter or water content of fog droplet weakened, also the positive correlation between relative dispersion and number concentration increased where the main mechanism needed to be integrated considering the interaction of collision-coalescence, condensation, and nucleation processes. It is found that the strength of the collision-coalescence process has a certain influence on the variation rule of dispersion. (2) The number concentration of 2–12 µm droplets in the fog constrained the relationship between the T and relative dispersion, with the number of large droplets reflects the strength of the collision-coalescence process. (3) Supersaturation changed microphysical quantities by increasing the number concentration of small droplets in the fog, which affected the variations of relative dispersion. For supersaturation greater than 0.12 %, the number concentration of droplets larger than 30 µm may be decreased due to gravitational settling. In addition, there is no significant relationship between supersaturation and relative dispersion if the initial nucleated fog droplet spectrum is narrow. [ABSTRACT FROM AUTHOR]

This study describes the Korea Meteorological Administration/National Institute of Meteorological Sciences (KMA/NIMS) Atmospheric Research Aircraft (NARA) and its observational data archive. NARA has been performing annual observation flights, which are aimed at reducing the uncertainty of atmospheric observations in the observation data gap area around the Korean peninsula, since January 2018. An online system has also been constructed to provide data management, transmission, quality control and simple visualization. The mission strategy of NARA is subdivided into four individual units, namely observations of severe weather (SW), climate monitoring (CM), environmental monitoring (EM) and cloud physics and weather modification experiments (CP). As of December 2020, NARA has been launched operationally for 325 flights (corresponding to 9.02 flights per month), typically over the West Sea, mid‐inland areas (34.4% and 25.4%, respectively), and below an altitude of 3 km (51.9%). Results of intercomparison tests confirmed that NARA measurements have reasonable offsets (<0.9%) to each other in terms of pressure and temperature. Moreover, the monthly average temperature profile in the East Sea area showed a seasonal variation was detected in monthly variation. From these results, it is evident that NARA data will contribute significantly to enhancing the level of scientific understanding of atmospheric observations and the applications (i.e. long‐term study) thereof as the amount of data accumulated increases. [ABSTRACT FROM AUTHOR]

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

On July 29, 2021, from 11:51 to 15:14, the Weather Modification Center of Hebei Province in China conducted warm cloud seeding experiments on the periphery cloud system of Typhoon "In-Fa" in the central and southern regions of Hebei Province, using a KingAir-350 aircraft to disperse warm cloud seeding agents (17 sticks of YF-1 warm cloud flares, including hygroscopic agents such as calcium chloride and potassium chloride, 400 g/stick). One hour after the operation, a band-shaped maximum precipitation area appeared downwind, indicating a significant increase in rainfall. The cloud physics detection equipment carried on the aircraft was used to detect the rainband on the periphery cloud system of the typhoon. The airborne detection data showed that the concentration of large particles (>50 µm) in warm clouds in the periphery cloud system of the typhoon was significantly higher than in other weather systems. The concentration of large particles had a higher correlation with water content than small particles. The water content of the warm clouds could reach 0.9 g/m3. After the warm cloud seeding agent was dispersed, the area where it was dispersed showed an increase in CIP (Cloud Imaging Probe, particle diameter >50 µm) concentration and a decrease in CDP (Cloud Droplet Probe, particle diameter <50 µm) concentration, indicating that the collision-coalescence process in the warm clouds was significantly enhanced. The rapid growth of large droplets due to collision with small droplets was related to the artificial seeding of hygroscopic agents. [ABSTRACT FROM AUTHOR]

Sensitivity of global temperature to rising CO2 remains highly uncertain. One of the greatest sources of uncertainty arises from cloud feedbacks associated with deep convective anvils. For deep convective clouds, their growth and characteristics are substantially controlled by mixed-phase microphysical processes. However, there remain several questions about cloud microphysical processes, especially in deep, mixed-phase clouds. Meanwhile, the representation of these processes in global climate models is limited. As such, the Deep ConvectiveMicrophysics Experiment (DCMEX) has undertaken an in-situ aircraft and ground-based measurement campaign. The data, combined with operational satellite observations and modelling, will help establish new understanding from the smallest, cloud and aerosol particle scales through to the largest, cloud-system and climate scales. DCMEX is one of four projects in the UK Natural Environment Research Council, Uncertainty in climate sensitivity due to clouds, CloudSense programme. Along with other CloudSense projects, DCMEX will support progress in reducing the uncertainty in cloud feedbacks and equilibrium climate sensitivity. This paper lays out the underpinning dataset from the DCMEX summer 2022 field campaign. Its content describes the coordinated operation and technical details of the broad range of aerosol, cloud physics, radar, thermodynamics, dynamics, electric field and weather instruments deployed. In addition, an overview of the characteristics of campaign cases illustrates the complementary operational observations available, as well as demonstrating the breadth of the campaign cases observed. [ABSTRACT FROM AUTHOR]

It is increasingly recognized that the generic climate state is a macroscopic manifestation of a nonequilibrium thermodynamic (NT) system characterized by maximum entropy production (MEP)—a generalized second law. Through a minimal tropical/polar-band model, I show that MEP would propel low clouds to polar bands to symmetrize the planetary albedo, a remarkable observation that may now be explained. The prognosed polar albedo is consistent with the current observation, which moreover is little altered during the ice age of more reflective land and the early Triassic period of symmetric land, suggesting its considerable stability through Earth's history. Climate models have not replicated the observed albedo symmetry and, given the potency of MEP in propelling clouds, it is suggested that to improve climate models, a higher premium be placed on resolving eddies—thereby encapsulating the NT—than detailed cloud physics. [ABSTRACT FROM AUTHOR]

There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations. [ABSTRACT FROM AUTHOR]

The numbers of clouds of a given size is a defining feature of the earth's atmosphere. As well as cloud area, cloud perimeter p is interesting because it represents the length of the shared interface between clouds and clear-skies across which air and buoyant energy are dissipated. A recent study introduced a first-principles expression for the steady-state distribution of cloud perimeters, measured within a quasi-horizontal moist isentropic layer, that is a scale invariant power-law n (p) ∝ p–(1+ β), where n (p) is the number density of cloud perimeters within [ p , p + dp ] and β = 1. This value of β was found to be in close agreement with output from a high-resolution, large eddy simulation of tropical convection. To further test this formulation, the current study evaluates n (p) within near-global imagery from nine full-disk and polar-orbiting satellites. A power-law is found to apply to measurements of n (p), and the value of β is observed to be remarkably robust to latitude, season, and land/ocean contrasts suggesting that, at least statistically speaking, cloud perimeter distributions are determined more by atmospheric stability than Coriolis forces, surface temperature, or contrasts in aerosol loading between continental and marine environments. However, the measured value of β is found to be 1.29 ± 0.05 rather than β = 1, indicating a relative scarcity of large clouds in satellite observations compared to theory and high-resolution cloud modeling. The reason for this discrepancy is unclear but may owe to the difference in perspective between evaluating n (p) along quasi-horizontal moist isentropes rather than looking down from space. As a test of this hypothesis, numerical simulation output shows that, while β ∼ 1 within isentropes, higher values of β are reproduced for a simulated satellite view. However, the simulated value is a function of the cloud detection sensitivity, but little such sensitivity is seen in satellite observations, suggesting a possible misrepresentation of the physics controlling cloud sizes in simulations. A power-law also applies to satellite observations of cloud areas covering a range between ∼ 3 km2 and ∼ 3 × 105 km2, a much wider range of scales than has been previously described in studies that we argue inappropriately treated the statistics of clouds truncated by the edge of a measurement domain. [ABSTRACT FROM AUTHOR]

Weather forecasting centers mainly assimilate infrared sounder data in clear‐conditions or in channels with their main sensitivity to the atmosphere well above the cloud tops. Sometimes channels with stronger cloud sensitivity are used in overcast conditions, but currently no cloud information is used from infrared sounders, and all‐sky assimilation approaches are still under development. However, cloudy radiances could already be used for validating the quality of clouds in forecasts. We illustrate this by comparing the brightness temperatures observed (obs) with AIRS (Atmospheric Infrared Sounder) to those calculated (cal) based on the clouds specified in the ECMWF (European Centre for Medium Range Weather Forecasting) Integrated Forecast System (IFS). Our analysis is based on a 12 hr ingest of AIRS data into the ECMWF assimilation system. We show that the standard deviation of (obs‐cal) using the 1,231 cm−1 atmospheric window channel is a metric of the fidelity of the clouds in the IFS. The global standard deviation of 5 K after accounting for likely space/time interpolation errors, appears to be dominated by clouds in the IFS which are not seen in the AIRS data, and vice versa. Our metric capitalizes on the unique sensitivity of infrared sounders to clouds for the routine monitoring of the fidelity of clouds in weather forecasts. Plain Language Summary: Key to a good weather forecast is clouds of the right variety in the right places at the right time. We evaluate this for 12 hr of European Centre for Medium Range Weather Forecasting (ECMWF) data by comparing the clouds created from the temperature and water vapor profiles and first principle physics with the clouds observed with the Atmospheric Infrared Sounder (AIRS). For the global mean, there is a good match, but locally the ECMWF finds deep convective clouds which are not seen in the AIRS data, and vice versa, in clusters on a scale of hundreds of kilometers. We propose a metric for the cloud fidelity, which allows monitoring and quantifying improvements as new algorithms or data are included in the Integrated Forecast System. Key Points: We compare the clouds observed from space to the clouds in a medium range weather forecast system, where clouds are created from first principle physics aloneWe propose a metric, which allows the numerical assessments of the cloud fidelity of the current forecast system to be compared with future upgradesOn average, the agreement between the forecast clouds and Atmospheric Infrared Sounder (AIRS) observations is very good, but, particularly in areas prone to deep convection, there are large cloudy areas which are not seen in the AIRS data, and vice versa [ABSTRACT FROM AUTHOR]

By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a 4-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited-area model (LAM) configuration of the UM – two regionally operational single-moment schemes (UM_RA2M and UM_RA2T) and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with >90 % mean cloud occurrence simulated between 0.15 and 1 km in all the model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud-top radiative cooling from these persistent modelled cloud layers and were consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration while improving cloud microphysical structure does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model. [ABSTRACT FROM AUTHOR]

There has been a growing concern that most climate models predict too frequent precipitation, likely due to lack of reliable sub-grid variability and vertical variations of microphysical processes in low-level warm clouds. In this study, the warm cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the DOE ARM Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017-2018. Eight-month SCM simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary-layer cloud structure, major macrophysical properties, and their transition. The improvement of warm cloud properties from CAM5 to CAM6 physics can be found compared to the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content, but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft observed ones in the summer but overestimated by a factor of two in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations of droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of sub-grid variations of cloud properties in CAM6 cloud microphysics, which further suppresses the scheme sensitivity to individual warm rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces too strong aerosol indirect effect. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations. [ABSTRACT FROM AUTHOR]

Pyrocumulonimbus (PyroCb) clouds have a complex origin dependent on fire dynamics and meteorological conditions. When a pyrocumulonimbus cloud develops and is maintained over a period of time, it can inject significant aerosol into the troposphere and lower stratosphere, resulting in a longer‐term (months to years) occurrence of aerosol in the stratosphere. In this work, we investigate the British Columbia wildfires on 12–13 August 2017 using a multi‐scale simulation framework. We use the output of a physics‐based wildfire model (FIRETEC) with parameterized energy, particle, and gas emissions to drive the upper atmospheric aerosol mass injection within a regional cloud resolving model (HIGRAD). We demonstrate that vertical motions produced by latent heat release of the condensation of ice and cloud particles within the PyroCbs induce another 5 km of lifting of the simulated aerosol plume. Primary black carbon and organic aerosols (OAs) alone may not be enough to explain the observed aerosol burden, thus we show that secondary OA produced via condensation of gases by the fires, ash, and possibly dust can enhance lofted aerosol mass. A simulation with all emission mechanisms active, driven by the observed fuel load and environmental conditions, reasonably reproduces an aerosol profile inferred from observational data. Plain Language Summary: The British Columbia 2017 summer fires injected significant amounts of aerosol into the upper troposphere and lower stratosphere. This modeling study is an attempt to represent and understand the complex combustion, aerosol, and cloud physics behind this injection. This event was partly characterized by a standard fuel type, forest, and a non‐standard fuel type, slash piles, associated with bark beetle kill and subsequent forest management practices. The burning of these two fuel types were undertaken in detailed combustion simulations that then provided aerosol and heat sources for large‐scale pyrocumulonimbus simulations. The large‐scale simulations illustrate the complex relationship between the burning fuel types and latent heat associated with cloud formation with this energy release mechanism lifting the aerosol plume to observed heights. Additionally, to represent observed aerosol amounts other aerosols such as secondary organic aerosol from condensation of gases produced by the fires, ash, and dust were included in the large‐scale simulations. Key Points: Formation of PyroCb clouds are multi‐faceted and complexPyroCbs have the potential to eject large amounts of aerosol to various layers of the atmosphereSimulating PyroCb formations requires high‐fidelity tools covering multi‐scale physics [ABSTRACT FROM AUTHOR]

Aiming at the needs of mechanism analysis of rainstorms and development of numerical prediction models in south China, the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration and the Chinese Academy of Meteorological Sciences jointly set up the Longmen Cloud Physics Field Experiment Base, China Meteorological Administration. This paper introduces the instruments and field experiments of this base, provides an overview of the recent advances in retrieval algorithms of microphysical parameters, improved understanding of microphysical characteristics, as well as the formation mechanisms and numerical prediction of heavy rainfalls in south China based on the field experiments dataset. [ABSTRACT FROM AUTHOR]

We present the largest sensitivity study to date for cloud cover using the Weather Forecasting and Research model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large-ensemble framework, ESIAS-met (Ensemble for Stochastic Integration of Atmospheric Simulations). This work demonstrates the capability and performance of ESIAS for large-ensemble simulations and sensitivity analysis. The study takes an iterative approach by first comparing over 1000 combinations of microphysics, cumulus parameterization, planetary boundary layer (PBL) physics, surface layer physics, radiation scheme, and land surface models on six test cases. We then perform more detailed studies on the long-term and 32-member ensemble forecasting performance of select combinations. The results are compared to CM SAF (Climate Monitoring Satellite Application Facility) satellite images from EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The results indicate a high sensitivity of clouds to the chosen physics configuration. The combination of Goddard, WRF single moments 6 (WSM6), or CAM5.1 microphysics with MYNN3 (Mellor–Yamada Nakanishi Niino level 3) or ACM2 (Asymmetrical Convective Model version 2) PBL performed best for simulating cloud cover in Europe. For ensemble-based probabilistic simulations, the combinations of WSM6 and SBU–YLin (Stony Brook University Y. Lin) microphysics with MYNN2 and MYNN3 performed best. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have drastically impacted the climate system since the Industrial Revolution. However, to what extent anthropogenic emissions influence the cloud droplet number concentration (Nd), the critical parameter for understanding aerosol‐cloud interactions, is poorly known on the hemispheric scale due to the considerable retrieval uncertainty. We employed multiple widely used Nd retrieval sampling methods to evaluate the long‐term trend in Nd contrast (ΔNd(NH‐SH)) between the Northern Hemisphere (NH) and Southern Hemisphere (SH). Here we show that the ΔNd(NH‐SH) was halved from 2003 to 2020 using different sampling methods and channels, even though the range of magnitudes of ΔNd(NH‐SH) from different retrieval sampling methods is large. Such dramatic changes in ΔNd(NH‐SH) are dominated by the significantly decreased Nd over the NH (∼20%) due to emission reductions compared to the relatively stable and pristine nature of the SH. Aerosol optical depth (AOD) and aerosol index (AI) correlate poorly with Nd based on long‐term trends, even though they replicate the contrast trends. This poor correlation is partly contributed by stratospheric smoke from wildfires in Australia that had little influence on Nd in the SH. The northwest Atlantic shows the largest contribution, ∼38%, to the Nd trend, whereas the northwest Pacific dominates the change in AOD and AI, contributing more than 60% to AOD and ∼50% to the AI trend in the NH. Our results imply that emission reductions significantly reduced ΔNd(NH‐SH) and provide strong observational evidence that anthropogenic activities have extensively altered liquid clouds in the NH in the last two decades. Plain Language Summary: Cloud droplet number concentration (Nd) is essential in understanding cloud physics, precipitation formation, and quantifying the effective radiative forcing associated with aerosol‐cloud interactions. Under the background of emission reductions over the past two decades, this study used global satellite observations to explore the long‐term (2003–2020) trend in Nd over the ocean and its hemispheric contrast (ΔNd(NH‐SH)) between the Northern Hemisphere and Southern Hemisphere. We found that Nd decreased ∼20% over the Northern Hemisphere, but no clear Nd trend was found over the Southern Hemisphere. As a result, the fractional change (defined as the trend divided by its climatology) of ΔNd(NH‐SH) is about −60% from 2003 to 2020. We further quantify the regional contribution and found that the northwest Atlantic shows the largest contribution, ∼38%, to the Nd trend over the Northern Hemisphere. Our results provide strong observational evidence that anthropogenic activities have extensively modified the microphysical properties of liquid clouds in the Northern Hemisphere over the past two decades. Key Points: The interhemispheric contrast of cloud droplet number concentration (Nd) was halved during 2003–2020 mainly due to emission reductionsAlthough Aerosol optical depth (AOD) and aerosol index (AI) replicated the contrast trends, they correlate poorly with NdIn the Northern Hemisphere, the northwest Atlantic contributes the most to Nd long‐term change, whereas the northwest Pacific dominates the AOD/AI trend [ABSTRACT FROM AUTHOR]

The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of the role of system properties of convective clouds in the formation of their macro- and microstructural characteristics. It is noted that research in this direction is one of the ways to develop cloud physics at the next stage. The results of numerical experiments on investigating the influence of the ambient atmosphere on the formation of macro- and microstructural characteristics of convective clouds are presented. It is the influence of the atmosphere that is one of the main structure-forming factors for clouds. The structure of the wind field in the atmosphere is considered the mechanism of the influence of the ambient atmosphere on cloud formation processes. The research is conducted on the basis of a 3D nonstationary model of thunderstorm clouds. [ABSTRACT FROM AUTHOR]

The study of particle nucleation and growth within clouds forms the fundamental microphysical component of cloud precipitation physics. Thus, we can establish cloud and fog precipitation development theories, providing a foundation for understanding weather, climate, and environmental changes. This case study, we observed ice cloud particles and meteorological elements using aircraft instruments over the North China Plain region on February 18, 2019. Focusing on aerosol–cloud interactions, we categorized clouds using altitude, vertical airflow, and cloud droplet concentration. The average number concentrations of cloud droplets (Nc), ice (Ni), and aerosols (Na) were 1.06 cm−3, 5.30 L−1, and 1.63 × 10 3 cm−3, respectively. The ice water content (IWC) in clouds was two orders of magnitude higher than the liquid water content (LWC). The differences in Nc at different heights were primarily caused by the presence of small droplets (smaller than 15 μm). Droplet concentrations were higher at 1–2 km and at the top of the clouds (5–6 km). Ice formation and growth was nonuniform in all zones, concentrated in the smallest and largest particle size ranges, with peaks at 125 and 1500 μm. Updraft significantly impacted cloud droplets, promoting their development and growth through water vapor condensation. Vertical airflow exerted a minor impact on aerosols. The Wegener–Bergeron–Findeisen process, driven by ice supersaturation and cloud liquid water subsaturation, led to a decrease in cloud droplet concentration and an increase in ice crystal concentration within the updraft region. In regions with different cloud droplet enrichment levels, the distribution of particles varied within the clouds. Zone with no vertical movement exhibited higher Nc than zone with downdraft airflow. The dense cloud droplet zone with downdraft airflow indicated narrower spectrum. Nc ranged from 2.5 to 19 μm, Ni ranged from 125 to 1350 μm, and Na ranged from 0.1 to 1 μm (no particles between 0.4 and 1 μm were observed). Ice formation and growth varied in each zone, possibly due to ice fragmentation. [Display omitted] • The sizes of ice cloud particles are predominantly found at 125 and 1500 μm. • Cloud drops in the updraft region were prone to form ice through the WBF process. • Formation and growth of ice were non-uniform due to ice fragmentation. • The dense cloud droplets zone with downdraft airflow had a narrower spectrum. [ABSTRACT FROM AUTHOR]

We proposed a method to improve the forecasts of landfalling tropical cyclones (LTCs) by constraining the "cloud physics" with Global Precipitation Measurement (GPM) satellite observations. Eight typical LTCs that are well observed by GPM satellite in the Asia‐Pacific region from 2015 to 2021 are selected to verify the feasibility of this method. Using a cloud‐resolving model, the LTCs are simulated for 3 days with both the original and modified microphysics scheme for comparison. The improvement of LTC forecasts is evaluated in terms of hydrometeor structure, amplitude, and location. Most notably, the structure forecast of condensed water improved up to 32% on average for all LTCs. The location forecast and amplitude forecast of condensed water also improved to varying degrees. Moreover, it is found that the error of LTC forecasts was reduced even more by using microphysics constraints from GPM observation than that by assimilating GPM data directly in other research. Plain Language Summary: The skill of Landfalling tropical cyclone (LTC) forecasts has increased in recent years but lacks to some degree in accuracy, refinement, and discrimination. It is still difficult for operational forecasters to accurately predict the spatial structure and precise location of condensed water in a LTC, which is exactly the need for disaster precaution and reduction. Herein, we find an approach to improve operational LTC forecasts through the fusion of Global Precipitation Measurement (GPM) measurements into cloud‐resolving models. By using GPM satellite data as constraints on cloud microphysical processes, the LTC forecasts produce significant improvements especially for the structure forecast of LTC hydrometeors. In addition, the errors of 89‐GHz brightness temperature reduce by ∼2.36 K in the forecast of Category 4 LTC Lan (2017), and ∼2.31 K in the forecast of Category 5 LTC Goni (2020), respectively. Such degree of improvements is similar to or even greater than the magnitude of direct GPM satellite data assimilation in LTC forecasts, so that constraining the microphysics model with GPM observations could be a substantial benefit for LTC predictions. Key Points: Direct constraints on cloud microphysics model are established using Global Precipitation Measurement observationGreat improvement is achieved in forecasting the spatial structure of landfalling tropical cyclone (LTC) hydrometeorsEight typical LTC cases are investigated to ensure robust improvement of their forecasts [ABSTRACT FROM AUTHOR]

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held–Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection–condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima, which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the largescale condensation and formation cycle. In addition, counterintuitive relationships}such as cloud condensate and cloud fraction responding in opposing directions}emphasize the need for careful dissection of physical mechanisms. In keeping with advection–condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain. [ABSTRACT FROM AUTHOR]

The compressional beta effect (CBE) arises in a compressible atmosphere with the nontraditional Coriolis terms (NCTs), the Coriolis force from the locally horizontal part of the planetary rotation. Previous studies proposed that the CBE speeds up the eastward propagation and slows down the westward propagation of zonal vertical circulations in a dry atmosphere. Here, we examine how the CBE affects the propagation of convectively coupled tropical waves. We perform 2D (x, z), large-domain cloud-resolving simulations with and without NCTs. This model setup mimics the atmosphere along Earth's equator, and differences between the simulations highlight the role of the CBE. We analyze precipitation, precipitable water, and surface and upper-level winds from our simulations. Gravity wave signals emerge in all fields. In the no-NCT simulation, eastward and westward gravity waves propagate at the same speed. With NCTs, eastward gravity waves propagate faster than westward gravity waves. To quantify the strength of the CBE, we then measure the difference in gravity wave speed and find that it linearly increases with the system rotation rate. This result is consistent with our theoretical prediction and suggests that the CBE can induce zonal asymmetry in propagation behaviors of convectively coupled waves. Significance Statement: The rotation of Earth turns eastward motion upward and upward motion westward, and vice versa. This effect is called the nontraditional Coriolis effect and is omitted in most of the current atmospheric models for predicting weather and climate. Using an idealized model with cloud physics, this study suggests that the inclusion of the nontraditional Coriolis effect speeds up eastward-moving rainy systems and slows down westward-moving ones. The speed change agrees with a theory without cloud physics. This study encourages restoring the nontraditional Coriolis effect to the atmospheric models since it increases the accuracy of tropical large-scale weather prediction while the cost is low. [ABSTRACT FROM AUTHOR]

For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies. Plain Language Summary: Feedbacks associated with Arctic warming are consequential for both the region and the strongly coupled global climate system. To assess the variability of the impacts of global warming and associated feedbacks in model‐based predictions, we quantified the sensitivity of the Arctic climate state to nine uncertain variables parameterizing the U.S. Department of Energy's global climate model known as the Energy Exascale Earth System Model (E3SM). Because the computational cost of repeatedly running high‐resolution configurations of E3SM was prohibitive, we used an ultra‐low resolution (ULR) configuration as a physics‐based surrogate for sensitivity analysis. Our first ever global sensitivity study of version 1 of the E3SM identified that the atmospheric parameters in E3SM's cloud physics model had the most impact on the atmosphere, sea ice, and ocean quantities of interest. This result demonstrates the importance of fully coupled climate analyses, which are necessary to identify such cross‐component influences. While we constructed confidence intervals that quantify the error in our estimates of parameter sensitivity introduced by using a limited number of ULR E3SM model runs, future investigation is needed to quantify the impact of resolution on error. Key Points: We perform the first global sensitivity analysis using the fully coupled ultra‐low resolution Energy Exascale Earth System ModelUncertainty in cloud physics parameters is found to most greatly impact Arctic climate predictionsOur inferred quantity of interest parameter correlations uncover key physical feedbacks and can guide model tuning [ABSTRACT FROM AUTHOR]

Advanced microphysics schemes (such as Eulerian bin and Lagrangian superdroplet) are becoming standard tools for cloud physics research and parameterization development. This study compares a double-moment bin scheme and a Lagrangian superdroplet scheme via large-eddy simulations of nonprecipitating and precipitating cumulus congestus clouds. Cloud water mixing ratio in the bin simulations is reduced compared to the Lagrangian simulations in the upper part of the cloud, likely from numerical diffusion, which is absent in the Lagrangian approach. Greater diffusion in the bin simulations is compensated by more secondary droplet activation (activation above cloud base), leading to similar or somewhat higher droplet number concentrations and smaller mean droplet radius than the Lagrangian simulations for the nonprecipitating case. The bin scheme also produces a significantly larger standard deviation of droplet radius than the superdroplet method, likely due to diffusion associated with the vertical advection of bin variables. However, the spectral width in the bin simulations is insensitive to the grid spacing between 50 and 100 m, suggesting other mechanisms may be compensating for diffusion as the grid spacing is modified. For the precipitating case, larger spectral width in the bin simulations initiates rain earlier and enhances rain development in a positive feedback loop. However, with time, rain formation in the superdroplet simulations catches up to the bin simulations. Offline calculations using the same drop size distributions in both schemes show that the different numerical methods for treating collision–coalescence also contribute to differences in rain formation. The stochastic collision–coalescence in the superdroplet method introduces more variability in drop growth for a given rain mixing ratio. [ABSTRACT FROM AUTHOR]

Abstract In November 2019, the GaoFen‐7(GF‐7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high‐precision long‐range three‐dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U‐Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF‐7 data processing and related research on footprint images.

Atmospheric ice-nucleating particles (INPs) play a critical role in cloud freezing processes, with important implications for precipitation formation and cloud radiative properties, and thus for weather and climate. Additionally, INP emissions respond to changes in the Earth System and climate, for example, desertification, agricultural practices, and fires, and therefore may introduce climate feedbacks that are still poorly understood. As knowledge of the nature and origins of INPs has advanced, regional and global weather, climate, and Earth system models have increasingly begun to link cloud ice processes to model-simulated aerosol abundance and types. While these recent advances are exciting, coupling cloud processes to simulated aerosol also makes cloud physics simulations increasingly susceptible to uncertainties in simulation of INPs, which are still poorly constrained by observations. Advancing the predictability of INP abundance with reasonable spatiotemporal resolution will require an increased focus on research that bridges the measurement and modeling communities. This review summarizes the current state of knowledge and identifies critical knowledge gaps from both observational and modeling perspectives. In particular, we emphasize needs in two key areas: (a) observational closure between aerosol and INP quantities and (b) skillful simulation of INPs within existing weather and climate models. We discuss the state of knowledge on various INP particle types and briefly discuss the challenges faced in understanding the cloud impacts of INPs with present-day models. Finally, we identify priority research directions for both observations and models to improve understanding of INPs and their interactions with the Earth System. [ABSTRACT FROM AUTHOR]