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Cloud seeding models are essential for understanding seeding mechanisms, yet their reliability remains insufficiently verified due to limited cases with confirmed seeding effects. On 19 March 2017, significant seeding signals were observed by multiple instruments following airborne cloud seeding over a stratiform cloud system with abundant supercooled water in northern China. This study performed an ensemble simulation of the case using two cloud microphysics schemes and three silver iodide (AgI) nucleation parameterizations, successfully replicating the vertical structure and evolution of the seeding‐induced cloud. The simulated seeding impact area, precipitation intensity, and changes in raindrop spectra closely aligned with observations. Results indicate that cloud seeding increased ice crystal amounts primarily through the deposition nucleation of AgI particles, activated the auto‐conversion of ice crystals to snow, enhanced snow deposition and riming processes, and ultimately increased surface precipitation through enhanced snow melting.

Condensational growth of cloud droplets due to supersaturation fluctuations is investigated by solving the hydrodynamic and thermodynamic equations using direct numerical simulations (DNS) with droplets being modeled as Lagrangian particles. The supersaturation field is calculated directly by simulating the temperature and water vapor fields instead of being treated as a passive scalar. Thermodynamic feedbacks to the fields due to condensation are also included for completeness. We find that the width of droplet size distributions increases with time, which is contrary to the classical theory without supersaturation fluctuations, where condensational growth leads to progressively narrower size distributions. Nevertheless, in agreement with earlier Lagrangian stochastic models of the condensational growth, the standard deviation of the surface area of droplets increases as t1∕2. Also, for the first time, we explicitly demonstrate that the time evolution of the size distribution is sensitive to the Reynolds number, but insensitive to the mean energy dissipation rate. This is shown to be due to the fact that temperature fluctuations and water vapor mixing ratio fluctuations increase with increasing Reynolds number; therefore the resulting supersaturation fluctuations are enhanced with increasing Reynolds number. Our simulations may explain the broadening of the size distribution in stratiform clouds qualitatively, where the mean updraft velocity is almost zero.

A theoretical model with a single prognostic variable, column-integrated moist static energy (MSE), was constructed to understand the dynamics of MJO eastward propagation and planetary scale selection. A key process in the model is the interaction of MSE-dependent convection with free-atmospheric first, second baroclinic modes and planetary boundary layer. Under a realistic parameter regime, the model reproduces the most unstable mode at zonal wavenumber 1 with a slow eastward phase speed of about 5 ms−1. The slow eastward phase speed in the model arises from the competition of eastward moving MSE tendencies caused by horizontal MSE advection, vertical MSE advection and boundary layer moistening with westward moving tendencies contributed by surface latent heat flux and atmospheric longwave radiative heating. The planetary scale selection is primarily attributed to the phase lag of longwave radiative heating associated with upper-level stratiform clouds that occur in the rear of MJO deep convection.

Cloud Vertical Structure (CVS) plays a crucial role in determining atmospheric circulation and the hydrological cycle. We analyzed the CVS in Stratiform Clouds with Embedded Convection (SCEC) occurring in the mei-yu front over central-eastern China based on the conjunction of the S-band Doppler weather radar, the C-band Frequency Modulation Continuous Wave (C-FMCW) rad ar, and the Microrain Radar (MRR). Our results showed that both the melting layers and the rain rate were unevenly distributed in the three SCEC cases, and there was a thicker melting layer and a larger rain rate in the embedded convection. In the stratiform regions, the vertical velocity of particles in the upper region of the melting layer was generally in the range of 0–4 m·s−1, and increased rapidly to 4–12 m·s−1 near the bottom of the melting layer. In the case of June 28, due to the vigorous development of embedded convection, the cloud particles in the upper layer showed upward movement, and the growth rate of the particles in this region was faster than that in the surrounding stratiform regions. The vertical distributions of Drop Spectrum Distributions (DSDs) showed that the average concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km, and the collision–coalescence process of drops in the embedded convection was stronger.

In this study, we investigated the macro- and microstructures of layered precipitation clouds in spring in Jilin Province, China. The premise of the campaign was to observe cloud particles in the melting layer (ML). The weather was developed under the influence of the Mongolia cyclone, which brought a large range of precipitation to the northeast. Combining the Droplet Measurement Technology (DMT) and Particle Measuring Systems (PMS) data, small particles accounted for the majority of all particles at each level above and below the ML. In our observations, both ice crystals (50–300 μm) and snowflakes (>300 μm) had two peaks between −5 and −2 °C. The high concentration of ice crystals at a temperature of −2.65 °C (4865 m) attained a maximum value of 287 L−1 and snowflakes with 47 L−1, which was similar to the previous studies. The Hallett–Mossop ice multiplication process operated most effectively at the temperature of −5 °C in this study. Even at the cloud dissipation stage, new droplets were still generated between −5 and −6 °C, providing abundant liquid water content (LWC) for the upper cloud. Although irregulars were observed, needles and spheres dominated in the observed cloud region of low LWC (<0.1 g m−3) at temperatures of −6 to −3 °C. These cloud conditions fit into the Hallett–Mossop criteria.

This study investigates the size distribution, the mean diameter, and the concentration of ice particles within stratiform clouds by using in situ observations from 29 flights in Hebei, China. Furthermore, it examines the empirical fitting of ice particle size distributions at different temperatures using Gamma and exponential functions. Without considering the first three bins of ice particles, the mean diameter of ice particles (size range 100–1550 µm) is found to increase with temperature from −15 to −9 °C but decrease with temperature from −9 to 0 °C. By considering the first three bins of ice particles using the empirical Gamma fitting relationship found in this study, the mean diameter of ice particles (size range 25–1550 µm) shows a similar variation trend with temperature, while the turning point changes from −9 to −10 °C. The ice particle number concentration increases from 13.37 to 50.23 L−1 with an average of 31.27 L−1 when temperature decreases from 0 to −9 °C. Differently, the ice concentration decreases from 50.23 to about 22.4 L−1 when temperature decreases from −9 to −12 °C. The largest mean diameter of ice particles at temperatures around −9 and −10 °C is most likely associated with the maximum difference of ice and water supersaturation at that temperature, making the ice particles grow the fastest. These findings provide valuable information for future physical parameterization development of ice crystals within stratiform clouds.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Due to their fast evolution and large natural variability in macro- and microphysical properties, the accurate representation of boundary layer clouds in current climate models remains a challenge. One of the regions with large intermodel spread in the Coupled Model Intercomparison Project Phase 6 ensemble is the western North Atlantic Ocean. Here, statistically representative in situ measurements can help to develop and constrain the parameterization of clouds in global models. To this end, we performed comprehensive measurements of boundary layer clouds, aerosol, trace gases, and radiation in the western North Atlantic Ocean during the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. In total, 174 research flights with 574 flight hours for cloud and precipitation measurements were performed with the HU-25 Falcon during three winter (February–March 2020, January–April 2021, and November 2021–March 2022) and three summer seasons (August–September 2020, May–June 2021, and May–June 2022). Here we present a statistical evaluation of 16 140 individual cloud events probed by the fast cloud droplet probe and the two-dimensional stereo cloud probe during 155 research flights in a representative and repetitive flight strategy allowing for robust statistical data analyses. We show that the vertical profiles of distributions of the liquid water content and the cloud droplet effective diameter (ED) increase with altitude in the marine boundary layer. Due to higher updraft speeds, higher cloud droplet number concentrations (Nliquid) were measured in winter compared to summer despite lower cloud condensation nucleus abundance. Flight cloud cover derived from statistical analysis of in situ data is reduced in summer and shows large variability. This seasonal contrast in cloud coverage is consistent with a dominance of a synoptic pattern in winter that favors conditions for the formation of stratiform clouds at the western edge of cyclones (post-cyclonic). In contrast, a dominant summer anticyclone is concomitant with the occurrence of shallow cumulus clouds and lower cloud coverage. The evaluation of boundary layer clouds and precipitation in the Nliquid ED phase space sheds light on liquid, mixed-phase, and ice cloud properties and helps to categorize the cloud data. Ice and liquid precipitation, often masked in cloud statistics by a high abundance of liquid clouds, is often observed throughout the cloud. The ACTIVATE in situ cloud measurements provide a wealth of cloud information useful for assessing airborne and satellite remote-sensing products, for global climate and weather model evaluations, and for dedicated process studies that address precipitation and aerosol–cloud interactions.

Below 0° ^{\\circ}$C, cloud droplets can freeze, altering a cloud's optical and radiative properties and thereby affecting Earth's energy balance. The microphysical mechanisms that govern this process, known as glaciation, are expected to act on minute timescales. Nevertheless, stratiform clouds can persist in the mixed‐phase temperature range (from 0° ^{\\circ}$C to −38° ^{\\circ}$C) for hours, thus glaciation events remain poorly characterized. Here, we analyze satellite observations of individual cloud tops to track their temporal phase evolution and to quantify the extent of glaciation. We find that most glaciation events do not result in complete freezing; rather, they induce a sustained shift in cloud properties while the clouds remain in the mixed‐phase regime. Our results indicate that higher hemispheric and seasonal concentrations of ice‐nucleating particles correlate with glaciation occurrence rate. Future studies can utilize our phase‐evolution and glaciation data sets to evaluate how well weather and climate models simulate mixed‐phased cloud evolution and phase heterogeneity.