Mixed‐phase stratiform clouds contain numerous liquid, mixed‐phase, and ice clusters, quantifying the cluster scales is potentially helpful to improve the parameterizations of microphysics and radiation models. However, the scales of hydrometeor clusters at different levels of stratiform clouds are not well understood. In this study, using airborne measurements and a large eddy simulation, we show that turbulence plays an important role in controlling the clusters with length of a few hundred meters, while the scales of larger clusters have stronger vertical variations from cloud base to top. The liquid clusters are the largest near the cloud top, while the lengths of ice clusters decrease from cloud base to top. The lengths of mixed‐phase clusters depend on the glaciation process, a faster glaciation results in smaller mixed‐phase clusters. The results improve our understanding on how the liquid and ice are mixed at different levels in stratiform clouds.

Detecting an unambiguous radar reflectivity signature is vital for investigating cloud-seeding impacts. The radar reflectivity change attributed to seeding depends on both the cloud conditions and the concentration of silver iodide (AgI) particles. In this study, the reflectivity change induced by glaciogenic seeding using different AgI particle concentrations is investigated under various cloud conditions using a 1D ice growth model coupled with an AgI nucleation parameterization. In addition, an algorithm is developed to estimate the minimum AgI particle concentration needed for a measurable glaciogenic cloud-seeding signature, assuming there is sufficient supercooled liquid water. The results show that the 1D model captures the ice growth habit compared to available observations and yields an unambiguous reflectivity change that is consistent with 3D model simulations and previous observational studies. Simulations indicate that seeding at a temperature of about −15 °C has the highest probability of detecting the radar seeding signature. This finding is consistent with the fact that the seeding temperature was about −15 °C or slightly warmer in most documented cases of unambiguous seeding signatures. Using the 1D model, 2500 numerical experiments are conducted, and the outputs are used to develop a parameterization to estimate the AgI particle concentration needed to detect an unambiguous seeding signature. Application of this parameterization to a real case suggests that seeding between −21 and −11 °C may possibly produce unambiguous seeding signatures and that seeding at about −15 °C requires the lowest AgI particle concentration. Seeding at warmer temperatures in precipitating clouds requires an extremely high amount of AgI and a very high content of supercooled liquid water. The results shown in this study deepen our understanding of the relationship between AgI particle concentration and radar seeding signatures under different cloud conditions. The parameterization can be used in operational seeding-based decision-making regarding the optimal amount of AgI dispersed.

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DE_LMI) were 20.41~35.53% lower than the nighttime DE_LMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DE_LMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DE_LMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr⁻¹ m⁻² nm⁻¹). However, during daytime, signals with RED less than 77.12 μJ sr⁻¹ m⁻² nm⁻¹ were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.

Accurate representation of cloud phase partitioning is critical for understanding the cloud feedback to climate change, but the supercooled liquid fraction is often underestimated in global climate models, in part due to the assumption of homogeneous distributions of hydrometeors in mixed‐phase clouds. In this study, we take into account the heterogeneous liquid‐ice mixing in modeling the ice depositional growth using airborne in situ measurements. The impact of heterogeneous liquid‐ice mixing on the Wegener‐Bergeron‐Findeisen process is parameterized as the fraction of ice that is mixed with liquid water, which is a function of liquid‐ice mixing homogeneity and liquid fraction. The liquid‐ice mixing homogeneity, quantified using the information entropy theory, is parameterized using the total condensed water content and temperature. With this observationally constrained parameterization incorporated in the Community Atmospheric Model version 6, the modeled cloud phase partitioning and cloud radiative forcing are improved.

In mixed‐phase cumulus clouds, droplets and ice crystals are inhomogeneously distributed, such spatial inhomogeneity can be enhanced by inhomogeneous entrainment as it can strengthen the particle clustering, which may further influence the phase partitioning and interactions among hydrometeors. However, the scale of particle clustering induced by inhomogeneous entrainment is not well known. Utilizing high‐resolution in‐situ aircraft measurements in mixed‐phase cumulus clouds, this study shows due to inhomogeneous entrainment‐mixing, the cluster scales of droplets and ice crystals decrease by approximately 10 m from the cloud center to the edge. Changes in the clustering are correlated with the intensity of entrainment‐mixing. Clouds that are significantly affected by entrainment exhibit stronger enhancement of particle clustering and a more noticeable reduction in cluster scale. The findings from this study improve our understanding of the scale of particle clustering induced by entrainment‐mixing, and are potentially helpful in evaluating models.