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Abstract Cloud seeding has been widely used for enhancing wintertime snowfall, particularly to augment water resources. This study examines microphysical responses to airborne glaciogenic seeding with silver iodide (AgI) during a specific case from the Seeded and Natural Orographic Wintertime Clouds: Idaho Experiment (SNOWIE) on 11 January 2017. Ground-based and airborne remote sensing and in situ measurements were employed to assess the impact of cloud seeding on cloud properties and precipitation formation. On 11th January, AgI propagated downwind along prevailing winds, and any potential ice and snow particles created from it were identified by ground-based radar as zigzag lines of enhanced reflectivity compared to background reflectivity. As the aircraft flew several times through these seeded clouds, microphysical properties within seeded clouds can be compared to those observed in unseeded clouds. The results indicate that seeded clouds exhibited significantly enhanced ice water content (IWC; reaching up to 0.20 g m⁻³) and precipitating-size (>400μm) ice particle concentrations (>7 L⁻¹) relative to unseeded clouds. Additionally, seeded clouds exhibited a 30% decrease in the mean liquid water content (LWC) and cloud droplet concentrations, indicating efficient glaciation processes influenced by AgI. Precipitating snow development in seeded clouds occurred within 15–40 min following AgI release, marked by a transition from mixed-phase clouds with abundant supercooled liquid water (SLW) to ice clouds, with lidar-measured linear depolarization ratio (LDR) increasing to >0.3. These findings underscore the effectiveness of cloud seeding in enhancing snowfall by facilitating ice initiation and growth. Significance StatementThis study investigates the microphysical response of wintertime orographic clouds to airborne glaciogenic seeding, highlighting its role in enhancing precipitation. By introducing silver iodide (AgI) into clouds with supercooled liquid water, the seeding process facilitates ice particle formation, leading to increased snowfall. Through a detailed analysis of microphysical conditions using advanced in situ and remote sensing instruments, the study reveals enhanced ice water content and efficient conversion of liquid water to ice in seeded clouds. These findings provide critical insights into cloud-seeding efficacy, particularly in regions with abundant supercooled liquid water, offering a scientific foundation for enhancing snowpack in water-scarce mountainous areas. 

Abstract It is not uncommon for layers within the warm conveyor belt in a frontal system to become potentially unstable, releasing elevated convection. The present study examines this destabilization process over complex terrain, and resulting precipitation, with a focus on the surface coupling, orographic ascent, and the initiation and evolution of convective cells. This study uses detailed observations combined with numerical modeling of a baroclinic system passing over the Idaho Central Mountains in the United States on 7 February 2017. The data were collected as part of the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE). Specifically, observations from a ground-based scanning X-band radar and an airborne profiling Doppler W-band radar along ~100 km long flight tracks aligned with the wind describe the development and evolution of convective cells above shallow stratiform orographic clouds. Convection-permitting numerical simulations of this event, with an inner domain grid resolution of 0.9 km, capture the emergence and vertical structure of the convective cells. Therefore, they are used to describe the advection of warm, moist air over a retreating warm front, cold air pooling within the Snake River Basin and adjacent valleys, destabilization in a moist layer above this shallow stable layer, and instability release in orographic gravity wave updrafts. In this case, the convective cells topped out near 6 km ASL, and the resulting precipitation fell mostly leeward of the ridge where convection was triggered, on account of strong cross-barrier flow. Sequential convection initiation over terrain ridges and rapid downwind transport led to banded precipitation structures. 

Abstract Cloud seeding of wintertime orographic clouds in the western United States has been attempted to enhance snow production and snowpack. Due to the scarcity of long-term, high-resolution cloud and precipitation observations over complex terrain, few studies have explored variations in orographic snowfall amounts by comparing environmental conditions and cloud characteristics with surface snowfall distribution and quantity. This study analyzes the environmental conditions and cloud characteristics in relation to surface snowfall patterns for the 24 snowfall events observed during the 2017 Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The investigation aims to understand: 1) What is the influence, if any, of wind, turbulence, and updraft strength on snowfall amounts, rates, and distribution? 2) What is the relationship, if any, of cloud properties and precipitation-forming effectiveness? and 3) Can cloud seeding modify controlling cloud characteristics sufficiently to increase precipitation in otherwise inefficient orographic clouds? The analysis over a 7200-km²observational domain revealed that the accumulated liquid-equivalent snowfall was <0.9 × 10⁷m³and snowfall rates were <0.45 mm h⁻¹for about half of the events. Low snowfall events were characterized by cloud-top temperatures >−20°C, fewer larger droplets, higher liquid water content, and lower ice water content compared to the other events. Cases with minimal background natural snowfall also permitted radar observation of seeding lines. In these cases, cloud seeding was mainly responsible for snowfall. The amount of silver iodide (AgI) released during cloud seeding did not correlate well with snowfall amount and rate. Significance StatementThis study illustrates the complexities of estimating snowfall in wintertime orographic clouds, underscoring the frequent inefficiency of these clouds in generating snowfall—a pivotal concern for regions dependent on snowpack for water resources. By analyzing environmental and cloud characteristics against snowfall patterns during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE), the research provides critical insights into the complexities of precipitation formation. The findings, particularly on the impact of cloud seeding in enhancing snowfall under specific conditions, contribute significantly to our understanding of weather modification techniques. This research not only is vital for advancing scientific knowledge in understanding wintertime mountain cloud systems but also holds profound implications for water resource management, agriculture, and disaster preparedness in snow-dependent regions. 

Plant intracellular nucleotide-binding leucine-rich repeat (NLR) receptors with an N-terminal Toll/interleukin-1 receptor (TIR) domain sense pathogen effectors to initiate immune signaling. TIR domains across different kingdoms have NADase activities and can produce phosphoribosyl adenosine monophosphate/diphosphate (pRib-AMP/ADP) or cyclic ADPR (cADPR) isomers. The lipase-like proteins EDS1 and PAD4 transduce immune signals from sensor TIR-NLRs to a helper NLR called ADR1, which executes immune function. We report the structure and function of anArabidopsisEDS1-PAD4-ADR1 (EPA) heterotrimer in complex with pRib-AMP/ADP activated by plant or bacterial TIR signaling. 2′cADPR can be hydrolyzed into pRib-AMP and thus activate EPA signaling. Bacterial TIR domains producing 2′cADPR also activate EPA function. Our findings suggest that 2′cADPR may be the storage form of the unstable signaling molecule pRib-AMP. 

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

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

Abstract This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties. 

Immune pathways of plants and prokaryotes are activated by distinct TIR-generated metabolites.