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Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) Model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are con-ducted for two cold seasons (2018 and 2019) centered on the Colorado Rockies’;750-km2 East River watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that micro-physical parameterization choice alone has a modest impact on total precipitation on the order of 63% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15% 6 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8 and 17 cm, and percent biases of 3%–15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization but conclude that snow lidar is a valuable nontraditional indicator of model performance.

Airframe icing caused by interactions with supercooled cloud droplets and precipitation can pose a risk to aviation operations and life safety. The In-Cloud Icing and Large-drop Experiment (ICICLE) was conducted in January–March 2019 to capture measurements in freezing conditions in support of the Federal Aviation Administration (FAA) Terminal Area Icing Weather Information for NextGen (TAIWIN) program. The National Research Council of Canada's Convair-580 research aircraft fulfilled the airborne data collection requirements for the ICICLE campaign and sampled icing clouds and atmospheric conditions over the midwestern United States. ICICLE flight 18, conducted on 17 February 2019, collected cloud and precipitation measurements during a widespread storm that generated supercooled small drops and freezing drizzle (FZDZ) within both liquid and mixed-phase regions. Supercooled liquid water content (LWC) typically ranged 0.30–0.45 g m−3 and exceeded 0.70 g m−3 in one instance. Maximum FZDZ diameters of 300–400 μm were commonly sampled near the base of clouds. Missed approaches performed at four Illinois airfields provided measurements of conditions from near ground level to above cloud top and supplied information regarding FZDZ formation and evolution. FZDZ was found to form at altitudes featuring relatively high LWC and sufficiently low droplet number concentrations. FZDZ formation zones were sometimes collocated with regions of atmospheric instability and/or wind shear. Flight through highly variable supercooled cloud droplet and FZDZ conditions resulted in significant Convair-580 airframe icing, highlighting the risk that icing conditions can pose to aircraft safety. [ABSTRACT FROM AUTHOR]

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

This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions. Significance Statement: This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions. [ABSTRACT FROM AUTHOR]

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the "black box" scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary: Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the "black box" reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points: Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processesThe proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemesThe partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method [ABSTRACT FROM AUTHOR]

Numerous Earth system models exhibit excessive aerosol effective forcing at the top of the atmosphere (TOA), including the Department of Energy's Energy Exascale Earth System Model (E3SM). Here, in the context of the E3SM version 3 effort, the predicted particle property (P3) stratiform cloud microphysics scheme and an enhanced deep convection parameterization suite (ZM_plus) are implemented into E3SM. The ZM_plus includes a convective cloud microphysics scheme, a multi‐scale coherent structure parameterization for mesoscale convective systems, and a revised cloud base mass flux formulation considering impacts of the large‐scale environment. The P3 scheme improved cloud and radiation particularly over the Northern Hemisphere and the frequency of heavy precipitation over the tropics, and the ZM_plus improved clouds in the tropics. P3 decreases aerosol effective forcing by 0.15 W m−2, while the ZM_plus increases it by 0.27 W m−2, resulting from excessive direct (0.31 W m−2) and indirect forcing (−1.79 W m−2). The excessive aerosol forcings are due to aerosol overestimation associated with insufficient aerosol wet removal. By improving the physical treatments in the aerosol wet removal, we effectively mitigate anthropogenic aerosol overestimation and thus attenuate direct (0.09 W m−2) and indirect aerosol forcing (−1.52 W m−2). Adjustment to primary organic matter hygroscopicity reduces direct and indirect forcing to more reasonable values: −0.13 W m−2 and −1.31 W m−2, respectively. On climatology, improved aerosol treatments mitigate overestimation of aerosol optical depth. Plain Language Summary: The Energy Exascale Earth System Model (E3SM) exhibits strong direct and indirect aerosol forcings, after advanced stratiform and convective cloud treatments are implemented. This study identifies the primary cause of these excessive aerosol forcings as the significant overestimation of anthropogenic aerosols due to insufficient removal of aerosols by precipitation. To address this issue, we made aerosol wet removal representation more physical, which effectively reduced the overestimation of aerosols, bringing both direct and indirect forcings to the expected ranges. Furthermore, cloud and aerosol climatology are notably improved as the result of these developments in cloud and aerosol treatments. Key Points: The new cloud microphysics scheme P3 improves simulations of cloud properties in the NH and aerosol forcingInsufficient wet removal leads to an overly strong aerosol forcing after incorporating the enhanced deep convection parameterization suiteThe aerosol wet removal improvements lead to more reasonable aerosol forcing and improved aerosol climatology in Energy Exascale Earth System Model [ABSTRACT FROM AUTHOR]

Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance Statement: Understanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement: The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes. [ABSTRACT FROM AUTHOR]

The article provides an overview of the use of Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) measurements for weather prediction and applications. It summarizes a workshop that brought together participants from various countries to discuss the status and potential of PRO. The article highlights the benefits of PRO in improving weather forecasts and validating cloud microphysical processes. It also discusses ongoing experiments and the potential use of PRO observations in weather forecasting, climate modeling, and evaluating satellite precipitation products. The article also mentions the consideration of future constellations of small satellites for PRO measurements. [Extracted from the article]

Bin and bulk schemes are the two primary methods to parameterize cloud microphysical processes. This study attempts to reveal how their structural differences (size-resolved vs. moment-resolved) manifest in terms of cloud and precipitation properties. We use a bulk scheme, the Arbitrary Moment Predictor (AMP), which uses process parameterizations identical to those in a bin scheme but predicts only moments of the size distribution like a bulk scheme. As such, differences between simulations using AMP's bin scheme and simulations using AMP itself must come from their structural differences. In one-dimensional kinematic simulations, the overall difference between AMP (bulk) and bin schemes is found to be small. Full-microphysics AMP and bin simulations have similar mean liquid water path (mean percent difference

The determination of whether a cloud will evolve into a thunderstorm is beneficial for understanding thunderstorm formation and important for ensuring the safety of society. However, a clear understanding of the microphysics in clouds for the occurrence of lightning activity has not been attained. Vast field observations and laboratory experiments indicate that graupel, which is rimed ice, is a vital hydrometeor for lightning generation, and is the foundation of riming electrification. In this study, polarimetric radar and lightning observations are used to compare the ice microphysics associated with graupel between 57 isolated thunderstorms and 39 isolated non-thunderstorms, and the differences in radar parameters are quantified. Our results for the occurrence of lightning activity in clouds showed the following results: 1) the maximum difference in graupel volume on the −10 °C isotherm height between thunderstorms and non-thunderstorms reached approximately 7.6 km³; 2) the graupel particles approached spherical shapes with a mean ZDR value of 0.3 dB, which likely indicated heavily rimed graupel was present; and 3) 98.2 % of thunderstorms were equipped with the ZDR column, and the mean depth was ~2.5 km³. Our study deepens our understanding of lighting physics and thunderstorm formation. [ABSTRACT FROM AUTHOR]

This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation. [ABSTRACT FROM AUTHOR]

Cloud seeding is considered a practical but unproved method to enhance precipitation or suppress hail, due to insufficient knowledge of ice formation and evolution after seeding clouds with ice nucleating particles. This study investigates the size effects on the immersion freezing of aerosol produced from commercial silver iodide (AgI) containing flares at mixed‐phase cloud temperatures from 243 to 267 K. Flare‐generated aerosol exhibited comparable ice nucleation ability (INA) to pure AgI particles in the size range of 200 and 400 nm. Non‐AgI impurities reduced the INA of flare‐generated particles ≤90 nm, which is lower than pure AgI particles ≤40 nm. The critical mass ice‐active site density of the generated aerosols (critical‐nm) was derived, indicating the minimum mass of AgI particles required for efficient ice nucleation. The new parameterization to predict critical‐nm can serve as a reference to optimize the effectiveness of cloud‐seeding materials for practical use. Plain Language Summary: Ice‐forming aerosol is commonly added to clouds, expecting precipitation enhancement via promotion of ice production. In this work, silver iodide (AgI) containing aerosol was generated from commercial cloud‐seeding products under different wind speed conditions. Its ice‐forming ability was studied at mixed‐phase cloud temperatures. The lower size limit for effective ice‐forming ability of the cloud‐seeding particles (90 nm) is higher than that of pure AgI particles (40 nm). The non‐AgI components produced by cloud‐seeding products are hypothesized to decrease the ice‐forming ability of smaller particles, as the mass fraction of ice‐nucleating AgI decreases. To estimate the minimum mass of AgI in a particle required for efficient ice nucleation under cloud‐seeding relevant conditions, we derived the critical ice‐activated mass fraction of the generated aerosols. These findings provide valuable insights into the optimization of cloud‐seeding practices for enhanced precipitation. Key Points: Silver iodide (AgI) containing cloud‐seeding aerosols exhibit comparable ice‐forming abilities to pure AgI at sizes of 200 and 400 nmNon‐AgI impurities produced from flare burning decrease the ice nucleation ability of particles smaller than 90 nmA new parameterization is presented to estimate the minimum mass of AgI particles required to maximize glaciogenic seeding [ABSTRACT FROM AUTHOR]

Lightning stroke strength, characterized by energy and peak currents, over the Tibetan Plateau (TP), is investigated by utilizing datasets from the World Wide Lightning Location Network and the Chinese Cloud-to-Ground Lightning Location System during 2016–2019. Focused on the south-central (SC) and southeast (SE) of the TP, it reveals that SE-TP experiences strokes with larger average energy and peak currents. Strong strokes (energy ≥ 100 kJ or peak currents ≥ |100| kA), exhibiting bimodal distribution in winter and summer, are more frequent and have larger average values over the SE-TP than the SC-TP, with diurnal distribution indicating peaks in energy and positive strokes in the middle of the night and negative strokes peaking in the morning. Utilizing the ECMWF/ERA-5 and MERRA-2 reanalysis, we find that stronger strokes correlate with thinner charge zone depths and larger CIWCFs but stable warm cloud depths and zero-degree levels over the SC-TP. Over the SE-TP, stronger strokes are associated with smaller CIWCFs and show turning points for warm cloud depths and zero-degree levels. Thicker charge zone depths correlate with stronger negative strokes but weaker positive strokes. Generating strokes of similar strength over the SC-TP requires larger CIWCFs, thinner warm cloud depths, and lower zero-degree levels than over the SE-TP. [ABSTRACT FROM AUTHOR]

Abstract Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models.

Abstract Numerous Earth system models exhibit excessive aerosol effective forcing at the top of the atmosphere (TOA), including the Department of Energy's Energy Exascale Earth System Model (E3SM). Here, in the context of the E3SM version 3 effort, the predicted particle property (P3) stratiform cloud microphysics scheme and an enhanced deep convection parameterization suite (ZM_plus) are implemented into E3SM. The ZM_plus includes a convective cloud microphysics scheme, a multi‐scale coherent structure parameterization for mesoscale convective systems, and a revised cloud base mass flux formulation considering impacts of the large‐scale environment. The P3 scheme improved cloud and radiation particularly over the Northern Hemisphere and the frequency of heavy precipitation over the tropics, and the ZM_plus improved clouds in the tropics. P3 decreases aerosol effective forcing by 0.15 W m−2, while the ZM_plus increases it by 0.27 W m−2, resulting from excessive direct (0.31 W m−2) and indirect forcing (−1.79 W m−2). The excessive aerosol forcings are due to aerosol overestimation associated with insufficient aerosol wet removal. By improving the physical treatments in the aerosol wet removal, we effectively mitigate anthropogenic aerosol overestimation and thus attenuate direct (0.09 W m−2) and indirect aerosol forcing (−1.52 W m−2). Adjustment to primary organic matter hygroscopicity reduces direct and indirect forcing to more reasonable values: −0.13 W m−2 and −1.31 W m−2, respectively. On climatology, improved aerosol treatments mitigate overestimation of aerosol optical depth.

The warm zone blizzard are both infrequent and highly destructive， making their accurate prediction a challenging and crucial focus.This study utilized four distinct cloud microphysics schemes （Lin， Thompson， WDM6， and WSM6） within the WRF mesoscale model to conduct a numerical simulation of a typical warm zone blizzard process in the northern Xinjiang in the middle of November 2016.The research objectives encompassed the evaluation of the model's capacity to simulate the warm zone blizzard， the selection of an optimal parameterization scheme， an analysis of the vertical distribution and evolution of hydrometeors during the snowstorm， and an exploration of the developmental patterns of related mesoscale systems contributing to the snowstorm.The analysis yielded the following key findings： （1） Among the diverse cloud microphysics parameterization schemes tested， the Lin scheme demonstrated the most favorable performance， effectively simulating snowfall magnitudes， spatial distributions， and trends.（2） In the cloud， all kinds of water condensate particles are active in the lower and middle troposphere， with graupel and snow being the most.Ice crystals， snow， cloud water and graupel particles are distributed from the upper layer to the lower layer.Near the windward slope of Altai Mountain is the center of the large concentration of each water condensate particle.The vertical alignment of the high value center of the four kinds of cloud water condensate particles in the strong snowfall area is conducive to the transformation of each particle.（3） High-humidity systems upstream moved westward， with the intensification of low-level southward jet streams resulting in pronounced moisture convergence.The western foothills of the Altai Mountains acted as a barrier， promoting moisture convergence by blocking the windward side； The low-level southerly jet also provides a continuous updraft and unstable condition for the generation of the blizzard.Strong snowfall is located in a wide updraft area between two groups of secondary circulations.The explosive growth of vertical movement is conducive to triggering the release of unstable energy， providing strong dynamic lifting conditions for the development and maintenance of the blizzard.

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

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users' decision making. [ABSTRACT FROM AUTHOR]

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Recent studies from the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) demonstrated definitive radar evidence of seeding signatures in winter orographic clouds during three intensive operation periods (IOPs) where the background signal from natural precipitation was weak and a radar signal attributable to seeding could be identified as traceable seeding lines. Except for the three IOPs where seeding was detected, background natural snowfall was present during seeding operations and no clear seeding signatures were detected. This paper provides a quantitative analysis to assess if orographic cloud seeding effects are detectable using radar when background precipitation is present. We show that a 5-dB change in equivalent reflectivity factor Ze is required to stand out against background natural Ze variability. This analysis considers four radar wavelengths, a range of background ice water contents (IWC) from 0.012 to 1.214 g m−3, and additional IWC introduced by seeding ranging from 0.012 to 0.486 g m−3. The upper-limit values of seeded IWC are based on measurements of IWC from the Nevzorov probe employed on the University of Wyoming King Air aircraft during SNOWIE. This analysis implies that seeding effects will be undetectable using radar within background snowfall unless the background IWC is small, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Significance Statement: Operational glaciogenic seeding programs targeting wintertime orographic clouds are funded by a range of stakeholders to increase snowpack. Glaciogenic seeding signatures have been observed by radar when natural background snowfall is weak but never when heavy background precipitation was present. This analysis quantitatively shows that seeding effects will be undetectable using radar reflectivity under conditions of background snowfall unless the background snowfall is weak, and the seeding effects are large. It therefore remains uncertain whether seeding had no effect on cloud microstructure, and therefore produced no signature on radar, or whether seeding did have an effect, but that effect was undetectable against the background reflectivity associated with naturally produced precipitation. Alternative assessment methods such as trace element analysis in snow, aircraft measurements, precipitation measurements, and modeling should be used to determine the efficacy of orographic cloud seeding when heavy background precipitation is present. [ABSTRACT FROM AUTHOR]

This study aims to characterize the shapes and fall speeds of ice pellets formed in various atmospheric conditions and to investigate the possibility to use a laser-optical disdrometer to distinguish between ice pellets and other types of precipitation. To do so, four ice pellet events were documented using manual observations, macrophotography, and laser-optical disdrometer data. First, various ice pellet fall speeds and shapes, including spherical, bulged, fractured, and irregular particles, were associated with distinct atmospheric conditions. A higher fraction of bulged and fractured ice pellets was observed when solid precipitation was completely melted aloft while more irregular particles were observed during partial melting. These characteristics affected the diameter–fall speed relations measured. Second, the measurements of particles' fall speed and diameter show that ice pellets could be differentiated from rain or freezing rain. Ice pellets larger than 1.5 mm tend to fall > 0.5 m s−1 slower than raindrops of the same size. In addition, the fall speed of a small fraction of ice pellets was < 2 m s−1 regardless of their size, as compared with a fall speed > 3 m s−1 for ice pellets with diameter > 1.5 mm. Video analysis suggests that these slower particles could be ice pellets passing through the laser-optical disdrometer after colliding with the head of the instrument. Overall, these findings contribute to a better understanding of the microphysics of ice pellets and their measurement using a laser-optical disdrometer. Significance Statement: Ice pellets are challenging to forecast and to detect automatically. In this study, we documented the fall speed and physical characteristics of ice pellets during various atmospheric conditions using a combination of a laser-optical disdrometer, manual observations, and macrophotography images. Relationships were found between the shape and fall speed of ice pellets. These findings could be used to refine the parameterization of ice pellets in atmospheric models and, consequently, improve the forecast of impactful winter precipitation types such as freezing rain. Furthermore, they will also help to physically interpret laser-optical disdrometer data during ice pellets and freezing rain. [ABSTRACT FROM AUTHOR]

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy's Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance. [ABSTRACT FROM AUTHOR]

The search for an explanation of correlations of weather and climate with solar activity has uncovered some subtle effects of the varying solar inputs, which include irradiance, energetic particles, electric fields induced by the solar wind, and the modulation of the galactic cosmic ray flux. Relevant phenomena have been identified in stratospheric chemistry and dynamics and in aerosols, atmospheric electricity, and clouds. The effects are small and appear intermittently in statistical analyses of observations. Correlations and theory permit responses to several solar inputs effective on the decadal time scales, making it difficult to distinguish them observationally. No mechanism for tropospheric decadal change can be considered to be definitely established, but for the day-to-day time scale there is clear observational evidence for clouds responding to solar wind-induced current flow (JZ) in the global electric circuit, due to changes in ionospheric potential, and independently from Forbush decreases of the cosmic ray flux. The responses to JZ also correlate separately with ionospheric potential changes due to changes in day-to-day thunderstorm activity. The identified mechanism is for electric charge effects on in-cloud scavenging, and this implies a decadal response, in view of decadal changes in JZ. However, a complete and quantitative model of the inferred electrically induced changes in cloud microphysics is not yet available. The failures, successes, and controversies of the search illustrate the somewhat chaotic process of scientific discovery. [ABSTRACT FROM AUTHOR]

Measurements from the South Dakota School of Mines and Technology T-28 hail-penetrating aircraft are analyzed using recently developed data processing techniques with the goals of identifying where the large hail is found relative to vertical motion and improving the detection of hail microphysical properties from radar. Hail particle size distributions (PSD) and environmental conditions (temperature, relative humidity, liquid water content, air vertical velocity) were digitally collected by the T28 between 1995 and 2003 and synthesized by Detwiler et al. The PSD were forward modeled by Cecchini et al. to simulate the radar reflectivity of the PSD at multiple radar wavelengths. The T-28 penetrated temperatures primarily between 08 and 2108C. The largest hailstones were sampled near the updraft/downdraft interface. Liquid water contents were highest in the updraft cores, whereas total (liquid 1 frozen) water contents were highest near the updraft/downdraft interface. The fitted properties of the PSD (intercept and slope) are directly related to each other but do not show any dependence on the region of the hailstorm where sampled. The PSD measurements and the radar reflectivity calculations at multiple radar wavelengths facilitated the development of relationships between the PSD bulk properties-hail kinetic energy and kinetic energy flux-and the radar reflectivity. Rather than using the oft-assumed sphericity and solid ice physical properties, actual measurements of hail properties are used in the analysis. Results from the maximum estimated size of hail (MESH) and vertical integrated liquid water (VIL) algorithms are evaluated based on this analysis. SIGNIFICANCE STATEMENT: Hailstorms in the United States have caused over $10 billion in damage for each of the last 14 years, according to insurance industry estimates (Heymsfield and Giammanco 2020). Algorithms have been developed to identify the presence and size of hail from radar. Numerical simulations of hailstorms have improved significantly since the 1970s, and further improvements will provide better resolution and more accurate estimates of the sizes of hailstones falling to the ground. Measurements of the properties of hailstones-their mass and terminal velocities-have been improved in recent years but in general are not incorporated in the algorithms developed for radar estimates of hail sizes or for the properties of hail used in the model simulations. This study synthesizes in situ aircraft data, computed radar backscatter cross sections, together with recent estimates of the physical characteristics of hailstones to improve the representation of hail in numerical models and quantitative assessment hail properties in storms using weather radar. [ABSTRACT FROM AUTHOR]

Secondary ice productions (SIPs) can produce ice crystals with a number concentration much higher than that of ice nucleating particles in mixed-phase clouds and therefore influence cloud glaciation and precipitation. For midlatitude continental mesoscale convective systems (MCSs), how SIPs affect the microphysical properties and precipitation is still not clear. There are few studies of SIPs in midlatitude continental MCSs. This study investigates the roles of three SIPs (rime splintering, freezing drop shattering, and ice-ice collisional breakup) on a squall line case in North China on 18 August 2020 using the WRF model with a modified Morrison double-moment bulk microphysical scheme. Including SIPs, especially ice-ice collisional breakup, in the model simulations markedly improves the simulated convective area and convective precipitation rate of the squall line, while slightly improving the area and precipitation of the stratiform region. Within the mixed-phase layer in both the convective and stratiform regions of the squall line, ice-ice collisional breakup is the dominant process to generate ice crystals. In contrast, rime splintering generates an order of magnitude fewer ice crystals than ice-ice collisional breakup, while freezing drop shattering plays a negligible role due to the lack of large drops. Ice multiplication through ice-ice collisional breakup and rime splintering produces numerous snowflakes and graupel. This leads to enhanced depositional growth and weaker riming, which in turn weakens rime splintering. It is recommended to add SIP parameterization to the model. [ABSTRACT FROM AUTHOR]

Abstract Cloud seeding is considered a practical but unproved method to enhance precipitation or suppress hail, due to insufficient knowledge of ice formation and evolution after seeding clouds with ice nucleating particles. This study investigates the size effects on the immersion freezing of aerosol produced from commercial silver iodide (AgI) containing flares at mixed‐phase cloud temperatures from 243 to 267 K. Flare‐generated aerosol exhibited comparable ice nucleation ability (INA) to pure AgI particles in the size range of 200 and 400 nm. Non‐AgI impurities reduced the INA of flare‐generated particles ≤90 nm, which is lower than pure AgI particles ≤40 nm. The critical mass ice‐active site density of the generated aerosols (critical‐nm) was derived, indicating the minimum mass of AgI particles required for efficient ice nucleation. The new parameterization to predict critical‐nm can serve as a reference to optimize the effectiveness of cloud‐seeding materials for practical use.

To address problems including underestimation of heavy precipitation, rapid decay of synoptic systems and low computational efficiency in operational forecast of CMA-GFS V3.3, some key technologies related to physics and dynamics of the model are developed and applied.A suite of graupel-related microphysical processes is adopted in the cloud microphysics scheme to improve the forecast performance of heavy precipitation. These processes include graupel colliding with cloud water, ice crystals and snow, automatic conversions of ice crystals to graupel and snow to graupel, melting process of graupel to raindrop and sublimation process of graupel. In addition, the evaporation rate of cloud and rainwater is restricted, which can increase the liquid water content in warm areas and improve precipitation efficiency.In the convection parameterization scheme, the role of the sub-cloud environmental relative humidity to convection triggers is considered, and the unreasonable occurrence of convections in dry environment is suppressed. Also, the sensitivity of the entrainment rate of the convective updraft to the relative humidity outside the cloud is enhanced to weaken the convections in dry environment. At the same time, the quasi-equilibrium closure scheme is optimized to improve the accuracy in calculating cloud-base mass flux which is related to the convection intensity.To solve the problem of the mass loss in long time integration, a mass conservation correction method is introduced to the model dynamic framework. The method is developed to ensure the mass conservation by adjusting mass in each grid box according to different weight coefficients which are determined by the change of total atmospheric mass of the current time step relative to the previous step.In terms of computational efficiency, the two-dimensional reference profile algorithm is developed. Without losing calculation accuracy, the model integration time step is extended from 240 s to 300 s using the new profile instead of the original three-dimensional reference profile. Meanwhile, the PCSI method is adopted instead of the GCR method, which reduces the time consuming of solving Helmholtz equation. In addition, the radiation scheme and predictor-corrector algorithms are also optimized to improve the computational efficiency.Through the application of the above key technologies, the forecasting skills for weather pattern and precipitation of CMA-GFS are significantly improved. And its computational efficiency is increased by about 1/3, which meets operational time requirement for the model with 0.125° horizontal resolution. Based on the improved model and the research achievements in other aspects of the forecast system, CMA-GFS is upgraded to V4.0 with a significantly improved comprehensive performance.

Lightning activity poses threats to human life and property safety. A large volume studies indicate that aerosol plays a significant role in lightning activity. To explore the effect of different types of aerosols on lightning activity in the Yangtze River Delta and its surrounding areas, cloud-to-ground (CG) lightning location data and aerosol optical depth from reanalysis dataset is analyzed in the target area(27.5°-35°N, 115°-122.5°E) during 2015-2021.The spatial distribution of aerosol optical depth (AOD) with CG lightning condition and without CG lightning condition, and monthly variation of aerosol concentration under two conditions are investigated. All the grids in the target region show that sulfate AOD is higher, while dust AOD is lower with CG lightning. In summer there are little differences between the AOD with CG lightning and without CG lightning conditions. In other months, more sulfate aerosol and less dust aerosol are found with CG lightning condition.The spatial distribution of CG lightning density difference under higher and lower AOD conditions is given. The results show that when the sulfate AOD is high, the CG lightning density of most grids is higher. The CG lightning density is significantly lower when the dust AOD is high.The correlation coefficient between CG lightning density and the AOD for different types of aerosols is calculated for the months when CG lightning is active. When the concentration of sulfate aerosol is low, the correlation coefficient between sulfate AOD and CG lightning density is significant. When the sulfate aerosol concentration exceeds a certain threshold, there is no significant correlation between CG lightning density and sulfate AOD. The positive correlation may be due to the fact that the cloud microphysical effect of sulfate aerosols can promote the development of convection. With sufficient water vapors, sulfate aerosols form cloud condensation nuclei and promote CG lightning activity. When the concentration of sulfate aerosols is higher than the threshold value, the cloud microphysical effect and the radiation effect of aerosol may be counterbalanced, which may lead to weak correlation between CG lightning density and aerosol concentration. The results also show a weak negative correlation between dust aerosol and CG lightning density in April, May, June, and no significant correlation in July, August, September. Considering the low concentration of dust aerosols in the Yangtze River Delta and its surrounding areas, the inhibitory effect of dust aerosols on CG lightning activity may not be just explained by the radiation effect. The large particle size of dust aerosol may play a significant role in suppressing CG lightning activity.

This research paper explores S M Nazmuz Sakib’s hypothesis of aerosol-sea ice feedback and its implications for climate system dynamics. The hypothesis suggests that changes in aerosol emissions significantly impact sea ice concentration and thickness in the Arctic, which, in turn, affect aerosol transport and deposition over the Tibetan Plateau. The paper presents a comprehensive analysis of the hypothesis, including the underlying facts, a proposed formula for the aerosol-sea ice feedback, and the potential variations of this feedback based on regional and temporal patterns of aerosol emission changes. Furthermore, the paper introduces the Sea Ice-Aerosol-Cloud Feedback (SIACF) Index and its application to historical incidents related to aerosol emission changes. The SIACF Index provides a quantitative measure to evaluate the influence of changes in aerosol emissions on sea ice concentration and thickness. The paper concludes by emphasizing the importance of testing this hypothesis through global aerosol models, observations, and historical incidents to gain a deeper understanding of aerosol-climate interactions and develop effective mitigation strategies.

Affected by a trough and shear line, a Meiyu heavy rainfall event occurred in Hubei Province from June 27th to 28th in 2020, resulting in severe disasters and significant economic losses.. To investigate the impact of microphysical processes on Meiyu front heavy rainfall and improve the forecasting and warning capabilities, the heavy rainfall case was simulated utilizing the regional model WRF 3.4.1, and the effect of cloud microphysical characteristics on the heavy rainfall was discussed. Results are as follows: (1) The heavy rainfall area and evolution of area-averaged precipitation were simulated well with a little overestimation. At the same time, the mesoscale systems were reproduced well through model results. (2) When rainfall strengthened rapidly, melting of ice phase hydrometeors (Melt) contributed more to the growth of rain drop than accretion of cloud droplet by rain drop (CLcr) and the composite radar echo developed rapidly. When the rainfall strengthened a little more smoothly, the amount of CLcr was larger than that of Melt. All the hydrometeors grew significantly and the composite radar echo intensified dramatically. Ice crystals grew more rapidly than snow and graupel. Before the peak rainfall, the content of hydrometeors (except snow), updraft and composite radar echo had reached extreme values. The center of updraft is more consistent with the area of large values of ice-phase particles and cloud droplets content, which is favorable to the riming process. Ice, graupel and the amount of water vapor condensation reached their maximum content and the composite radar echo began to decay before the strongest precipitation occurred. (3) Less ice phase hydrometeors collected cloud droplet was produced than CLcr. Auto conversion of cloud droplet to rain increased significantly when the rainfall process decayed. The main pathway of ice phased hydrometeors was Bergeron process in the initial stage of the precipitation. With the growth of ice phase hydrometeors, riming process between cloud droplet and ice phase hydrometeors was dominant until the heavy rainfall process ended.

The National Center for Atmospheric Research released a global atmosphere model named Community Atmosphere Model version 5.0 (CAM5), which aimed to provide a global climate simulation for meteorological research. Among them, the cloud microphysics scheme is extremely time-consuming, so developing efficient parallel algorithms faces large-scale and chronic simulation challenges. Due to the wide application of GPU in the fields of science and engineering and the NVIDIA's mature and stable CUDA platform, we ported the code to GPU to accelerate computing. In this paper, by analyzing the parallelism of CAM5 cloud microphysical schemes (CAM5 CMS) in different dimensions, corresponding GPU-based one-dimensional (1D) and two-dimensional (2D) parallel acceleration algorithms are proposed. Among them, the 2D parallel algorithm exploits finer-grained parallelism. In addition, we present a data transfer optimization method between the CPU and GPU to further improve the overall performance. Finally, GPU version of the CAM5 CMS (GPU-CMS) was implemented. The GPU-CMS can obtain a speedup of 141.69 × on a single NVIDIA A100 GPU with I/O transfer. In the case without I/O transfer, compared to the baseline performance on a single Intel Xeon E5-2680 CPU core, the 2D acceleration algorithm obtained a speedup of 48.75 × , 280.11 × , and 507.18 × on a single NVIDIA K20, P100, and A100 GPU, respectively. [ABSTRACT FROM AUTHOR]

SIGNIFICANCE STATEMENT: A novel experimental approach to conduct laboratory-type experiments in natural clouds is introduced within the CLOUDLAB project: a unique combination of uncrewed aerial vehicles (UAVs) and state-of-the-art instrumentation is used to perform targeted cloud seeding experiments in persistent supercooled stratus clouds. The results will enhance the process understanding of mixed-phase clouds and improve the ice-phase parameterizations in weather and climate models, ultimately leading to more reliable precipitation forecasts and climate projections. In addition, the gained knowledge will be important to quantify the consequences of artificial weather modification and climate interventions. Ice formation and growth processes play a crucial role in the evolution of cloud systems and the formation of precipitation. However, the initial formation and growth of ice crystals are challenging to study in the real atmosphere resulting in uncertainties in weather forecasts and climate projections. The CLOUDLAB project tackles this problem by using supercooled stratus clouds as a natural laboratory for targeted glaciogenic cloud seeding to advance the understanding of ice processes: Ice nucleating particles are injected from an uncrewed aerial vehicle (UAV) into supercooled stratus clouds to induce ice crystal formation and subsequent growth processes. Microphysical changes induced by seeding are measured 3–15 min downstream of the seeding location using in situ and ground-based remote sensing instrumentation. The novel application of seeding with a multirotor UAV combined with the persistent nature of stratus clouds enables repeated seeding experiments under similar and well-constrained initial conditions. This article describes the scientific goals, experimental design, and first results of CLOUDLAB. First, the seeding plume is characterized by using measurements of a UAV equipped with an optical particle counter. Second, the seeding-induced microphysical changes observed by cloud radars and a tethered balloon system are presented. The seeding signatures were detected by regions of increased radar reflectivity (>−20 dBZ), which were 10–20 dBZ higher than the natural background. Simultaneously, high concentrations of seeding particles and ice crystals (up to 2,000 L−1) were observed. A cloud seeding case was simulated with the numerical weather model ICON to contextualize the findings. [ABSTRACT FROM AUTHOR]

The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement: For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)'s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied. [ABSTRACT FROM AUTHOR]

This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact. Significance Statement: Winter orographic cloud seeding has been conducted for decades over the Snowy Mountains of Australia for securing water resources. However, this study is the first to perform cloud-seeding simulation for a robust, event-based seeding impact evaluation. A state-of-the-art cloud-seeding model (WRF-WxMod) was used to simulate the cloud seeding and quantified its impact on the region. The Southern Hemisphere, due to low aerosol emissions and highly pristine cloud conditions, has distinctly different cloud microphysical characteristics than the Northern Hemisphere, where WRF-WxMod has been successfully applied in a few regions over the United States. The results showed that WRF-WxMod could accurately capture the clouds and precipitation in both the natural and seeded conditions. [ABSTRACT FROM AUTHOR]

Entrainment of dry air into clouds strongly influences cloud optical and precipitation properties and the response of clouds to aerosol perturbations. The response of cloud droplet size distributions to entrainment-mixing is examined in the Pi convection-cloud chamber that creates a turbulent, steady-state cloud. The experiments are conducted by injecting dry air with temperature (Te) and flow rate (Qe) through a flange in the top boundary, into the otherwise well-mixed cloud, to mimic the entrainment-mixing process. Due to the large-scale circulation, the downwind region is directly affected by entrained dry air, whereas the upwind region is representative of the background conditions. Droplet concentration (Cn) and liquid water content (L) decrease in the downwind region, but the difference in the mean diameter of droplets (Dm) is small. The shape of cloud droplet size distributions relative to the injection point is unchanged, to within statistical uncertainty, resulting in a signature of inhomogeneous mixing, as expected for droplet evaporation times small compared to mixing time scales. As Te and Qe of entrained air increase, however, Cn, L, and Dm of the whole cloud system decrease, resulting in a signature of homogeneous mixing. The apparent contradiction is understood as the cloud microphysical responses to entrainment and mixing differing on local and global scales: locally inhomogeneous and globally homogeneous. This implies that global versus local sampling of clouds can lead to seemingly contradictory results for mixing, which informs the long-standing debate about the microphysical response to entrainment and the parameterization of this process for coarse-resolution models. [ABSTRACT FROM AUTHOR]

This study selected three heavy-rainfall events of different types in Taiwan's Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model's capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model's performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable. [ABSTRACT FROM AUTHOR]

This paper examines the controls on supercooled liquid water content (SLWC) and drop number concentrations (Nt,CDP) over the Payette River basin during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. During SNOWIE, 27.4% of 1-Hz in situ cloud droplet probe samples were in an environment containing supercooled liquid water (SLW). The interquartile range of SLWC, when present, was found to be 0.02-0.18 g m23 and 13.3-37.2 cm23 for Nt,CDP, with the most extreme values reaching 0.40-1.75 g m23 and 150-320 cm23 in isolated regions of convection and strong shear-induced turbulence. SLWC and Nt,CDP distributions are shown to be directly related to cloud-top temperature and ice particle concentrations, consistent with past research over other mountain ranges. Two classes of vertical motions were analyzed as potential controls on SLWC and Nt,CDP, the first forced by the orography and fixed in space relative to the topography (stationary waves) and the second transient, triggered by vertical shear and instability within passing synoptic-scale cyclones. SLWC occurrence and magnitudes, and Nt,CDP associated with fixed updrafts were found to be normally distributed about ridgelines when SLW was present. SLW was more likely to form at low altitudes near the terrain slope associated with fixed waves due to higher mixing ratios and larger vertical air parcel displacements at low altitudes. When considering transient updrafts, SLWC and Nt,CDP appear more uniformly distributed over the flight track with little discernable terrain dependence as a result of time and spatially varying updrafts associated with passing weather systems. The implications for cloud seeding over the basin are discussed. [ABSTRACT FROM AUTHOR]

The western United States region, an economic and agricultural powerhouse, is highly dependent on winter snowpack from the mountain west. Coupled with increasing water and renewable electricity demands, the predictability and viability of snowpack resources in a changing climate are becoming increasingly important. In Idaho, specifically, up to 75% of the state's electricity production comes from hydropower, which is dependent on the timing and volume of spring snowmelt. While we know that 1 April snowpack is declining from SNOTEL observations and is expected to continue to decline as indicated by GCM predictions, our ability to understand the variability of snowfall accumulation and distribution at the regional level is less robust. In this paper, we analyze snowfall events using 0.9-km-resolution WRF simulations to understand the variability of snowfall accumulation and distribution in the mountains of Idaho between 1 October 2016 and 31 April 2017. Various characteristics of snowfall events throughout the season are evaluated, including the spatial coverage, event durations, and snowfall rates, along with the relationship between cloud microphysical variables—particularly liquid and ice water content—on snowfall amounts. Our findings suggest that efficient snowfall conditions—for example, higher levels of elevated supercooled liquid water—can exist throughout the winter season but are more impactful when surface temperatures are near or below freezing. Inefficient snowfall events are common, exceeding 50% of the total snowfall events for the year, with some of those occurring in peak winter. For such events, glaciogenic cloud seeding could make a significant impact on snowpack development and viability in the region. Significance Statement: The purpose and significance of this study is to better understand the variability of snowfall event accumulation and distribution in the Payette Mountains region of Idaho as it relates to the local topography, the drivers of snowfall events, the cloud microphysical properties, and what constitutes an efficient or inefficient snowfall event (i.e., its ability to convert atmospheric liquid water into snowfall). As part of this process, we identify how many snowfall events in a season are inefficient to determine the number of snowfall events in a season that are candidates for enhancement by glaciogenic cloud seeding. [ABSTRACT FROM AUTHOR]

This study analyzed the macro- and microphysical response characteristics of a typical multicell hailstorm after seeding on 27 April 2019 in Weining, China, using X-band dual-polarization radar (YLD1-D) data. An improved X-pol hydrometeor identification algorithm was employed for hydrometeor identification. According to the diffusion of the seeding agents, the seeded hailstorm was graded into three study regions, and the evolution of the seeded hailstorm was divided into four periods. The response characteristics of the seeded hailstorm in each region and period were compared and analyzed. The results show that 1) macroscopically, the decrease in the reflectivity and the height of strong echo mainly occurred in the seeded period, whereas the decrease in the echo top and the height of the storm was mainly in the postseeded period; the echo height variation of the unseeded hailstorm is obviously different from that of the seeded hailstorm; and 2) from the microscopic perspective, the decrease in low-density graupel and supercooled water and the increase in ice crystals and aggregates in the seeded region mainly occurred in the seeded period, which was consistent with the time required for the "benefit competition" after the silver iodide completed nucleation. When compared with the seeded region, the hydrometeors in the unseeded region had an opposite trend (or an in-phase trend with an obviously lower changing rate), which further indicated the impact of the artificial ice nuclei on microphysical processes in the seeded region. For hailstorms with a high content of supercooled droplets and graupel, the key mechanisms of hail suppression are "cloud water glaciation," "beneficial competition," and "early rainout." [ABSTRACT FROM AUTHOR]

Lightning stroke strength, characterized by energy and peak currents, over the Tibetan Plateau (TP), is investigated by utilizing datasets from the World Wide Lightning Location Network and the Chinese Cloud-to-Ground Lightning Location System during 2016–2019. Focused on the south-central (SC) and southeast (SE) of the TP, it reveals that SE-TP experiences strokes with larger average energy and peak currents. Strong strokes (energy ≥ 100 kJ or peak currents ≥ |100| kA), exhibiting bimodal distribution in winter and summer, are more frequent and have larger average values over the SE-TP than the SC-TP, with diurnal distribution indicating peaks in energy and positive strokes in the middle of the night and negative strokes peaking in the morning. Utilizing the ECMWF/ERA-5 and MERRA-2 reanalysis, we find that stronger strokes correlate with thinner charge zone depths and larger CIWCFs but stable warm cloud depths and zero-degree levels over the SC-TP. Over the SE-TP, stronger strokes are associated with smaller CIWCFs and show turning points for warm cloud depths and zero-degree levels. Thicker charge zone depths correlate with stronger negative strokes but weaker positive strokes. Generating strokes of similar strength over the SC-TP requires larger CIWCFs, thinner warm cloud depths, and lower zero-degree levels than over the SE-TP.

During near-0°C surface conditions, diverse precipitation types (p-types) are possible, including rain, drizzle, freezing rain, freezing drizzle, ice pellets, wet snow, snow, and snow pellets. Near-0°C precipitation affects wide swaths of the United States and Canada, impacting aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Fundamental challenges remain in observing, diagnosing, simulating, and forecasting near-0°C p-types, particularly during transitions and within complex terrain. Motivated by these challenges, the field phase of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted from 1 February to 15 March 2022 to better understand how multiscale processes influence the variability and predictability of p-type and amount under near-0°C surface conditions. WINTRE-MIX took place near the U.S.–Canadian border, in northern New York and southern Quebec, a region with plentiful near-0°C precipitation influenced by terrain. During WINTRE-MIX, existing advanced mesonets in New York and Quebec were complemented by deployment of 1) surface instruments, 2) the National Research Council Convair-580 research aircraft with W- and X-band Doppler radars and in situ cloud and aerosol instrumentation, 3) two X-band dual-polarization Doppler radars and a C-band dual-polarization Doppler radar from the University of Illinois, and 4) teams collecting manual hydrometeor observations and radiosonde measurements. Eleven intensive observing periods (IOPs) were coordinated. Analysis of these WINTRE-MIX IOPs is illuminating how synoptic dynamics, mesoscale dynamics, and microscale processes combine to determine p-type and its predictability under near-0°C conditions. WINTRE-MIX research will contribute to improving nowcasts and forecasts of near-0°C precipitation through evaluation and refinement of observational diagnostics and numerical forecast models. [ABSTRACT FROM AUTHOR]

Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 µm and CTT, 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 mm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties. [ABSTRACT FROM AUTHOR]

The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP² Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent’s complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon, Philippines, in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment with its R/V Sally Ride stationed in the northwestern tropical Pacific, CAMP² Ex documented diverse biomass burning, industrial and natural aerosol populations, and their interactions with small to congestus convection. The 2019 season exhibited El Niño conditions and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP² Ex’s preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high-resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP² Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud, and radiation processes as well as a crucible for the design of future observing systems. [ABSTRACT FROM AUTHOR]

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates.

In-cloud vertical air motion is a key parameter to describe cloud dynamics and lifecycles. Short-wavelength ($Ka$- and $W$-band) radars are often used to observe clouds and extract the vertical air motion from the radar-measured Doppler velocity. However, the vertical air motion retrieval techniques developed using ground-based radar Doppler spectrum may be problematic for airborne cloud radars due to nonideal radar system performance (e.g., receiver saturation) and the Doppler velocity measurement uncertainties caused by aircraft motion, aircraft speed, and the large aircraft motion induced horizontal wind. This article presents a new and simple approach for estimating the in-cloud vertical air motion using airborne $W$-band radar measurements, which is applicable to cloud measurements without precipitation or with weak precipitation. In particular, a power-law relation between cloud and precipitation particle fall speed and attenuation corrected radar reflectivity is established first. Then, the particle fall speeds estimated from radar reflectivity using the established power-law relation are compared with the radar-measured Doppler velocities to derive the vertical air motions. This technique is demonstrated with $W$-band airborne radar measurements from the National Oceanic and Atmospheric Administration Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign from January to February 2020, which was designed to investigate atmospheric shallow convection and air-sea interaction in the tropical North Atlantic east of Barbados. The retrieved in-cloud air motion is compared with results reported in the literature for a nearby domain, suggesting that this simple retrieval technique performs reasonably well. Since this approach is applicable for airborne radar measurements with high-frequency noises, it can be used as an effective tool for investigating the mean profile of vertical air motion.

Abstract We describe a collaborative analysis study involving numerical models and observation data for the Tokyo metropolitan area called the ULTra-sIte for Measuring Atmosphere of Tokyo Metropolitan Environment (ULTIMATE) project. It evaluates cloud microphysics schemes of numerical models using extensive observation data for the Tokyo area. We have access to various remote sensing and in situ data for the Tokyo area for operational and research purposes, particularly by enhancing observations for ground validation of the EarthCARE satellite, which is set to launch in 2023. This study focuses on using the dual-polarization Doppler weather radar, operated by the Japan Meteorological Agency. In terms of numerical models, we use and compare multi-models with single-moment (SM) and double-moment (DM) cloud microphysics schemes; the global non-hydrostatic model, Non-hydrostatic ICosahedral Atmospheric Model (NICAM) and the two regional models with A System based on a Unified Concept for Atmosphere (ASUCA) and Scalable Computing for Advanced Library and Environment (SCALE) are used. In particular, because NICAM can be used as both a global and a regional model, we can immediately test the improved scheme on a global scale for its effect on climatology and the evaluation of climate sensitivity. This paper introduces the methodology for evaluating numerical models by the dual-polarization radar using the observation simulator and compares numerical model results with observations. In particular, we evaluate the simulated rain in the lower level near the ground and the large ice particles just above the melting level. The simulation with NICAM-DM reproduces the comparable polarimetric radar characteristics of rain as the observation. However, the simulations with NICAM-SM and ASUCA-SM show larger raindrop sizes in stronger rain areas compared to the observation. For the larger ice particles just above the melting level around 4 km, NICAM-DM and ASUCA-SM overestimate particle sizes of graupel or snow, while NICAM-SM has a similar size of the ice particles. In future studies, we will use the present results to improve the cloud microphysics scheme, which will be tested on a global model.

Abstract Determining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal parameters is essential for reducing model error. Recently, automatic parameter estimation using the ensemble Kalman filter (EnKF) has been tested instead of conventional manual parameter tuning. To maintain uncertainty for the parameters to be estimated and avoid filter divergence in EnKF-based methods, some inflation techniques should be applied to parameter ensemble spread (ES). When ES is kept constant through the estimation using an inflation technique, the precision and convergence speed of the estimation vary depending on the ES assigned to estimated parameters. However, there is debate over how to determine an appropriate constant ES for estimated parameters in terms of precision and convergence speed. This study examined the dependence of precision and convergence speed of an estimated parameter on the ES to establish a reliable and efficient method for EnKF-based parameter estimation. This was carried out by conducting idealized experiments targeting a parameter in a cloud microphysics scheme. In the experiments, there was a threshold value for ES where any smaller values did not result in any further improvements to the estimation precision, which enabled the determination of the optimal ES in terms of precision. On the other hand, the convergence speed accelerates monotonically as ES increases. To generalize the precision and convergence speed, we approximated the time series of parameter estimation with a first-order autoregression (AR(1)) model. We demonstrated that the precision and convergence speed may be quantified by two parameters from the AR(1) model: the autoregressive parameter and the amplitude of random perturbation. As the ES increases, the autoregressive parameter decreases, while the random perturbation amplitude increases. The estimation precision was determined based on the balance between the two values. The AR(1) approximation provides quantitative guidelines to determine the optimal ES for the precision and convergence speed of the EnKF-based parameter estimation.