Operational cloud seeding has been implemented to alleviate local precipitation shortages in China for over half a century. Here, we present quantitative evidence for the effect of AgI seeding on supercooled layer clouds with a top cloud temperature of −15°C in China, as documented for the first time by a combination of radar, satellite, and disdrometer observations. A radar signature appeared 18 min after seeding, shortly followed by a visible glaciated seeding track. The seeding signature expanded horizontally at a rate of ∼1.4 and ∼0.3 m s⁻¹ before and after 04:25 UTC. The radar signature descended to the surface 40 min after seeding. A disdrometer captured the precipitation of the first seeded raindrops that reached maximum diameter of 2.75 mm compared to the maximum diameter of 1 mm of the light background rain. The enhanced surface rainfall was observed within the subsequent 100 min. A conceptual model for the formation and expansion of the seeding track is presented. Although the precipitation was light, it is a promising step toward the goal of quantifying the impact of cloud seeding in China.

To accurately identify ideal operating conditions for scientific cloud seeding and to collect evidence of the effects of seeding, a set of three flights, with figure‐8‐like paths, was designed for supercooled liquid water detection. These flights were conducted near the cloud top before seeding, during seeding, and upon re‐entry. Using both in situ aircraft and satellite remote sensing observations, a comprehensive analysis was conducted to understand the response of cloud properties to seeding within supercooled cloud tops. Satellite observations showed clear icing cloud tracks after seeding with abundant supercooled liquid water near the cloud top. Radar observations confirmed enhanced radar reflectivity echoes in cloud‐seeding regions. For regions cloud seeded with supercooled liquid water, the liquid droplets quickly froze into ice crystals, which further grew into large ice crystals via vapor deposition, riming, and collision‐coalescence processes. With these processes, large ice crystals formed and rapidly fell downward, causing the sinking of cloud tops and precipitation to fall to the ground. By contrast, there were no such phenomena in the regions without supercooled liquid water or in those regions with supercooled liquid, but otherwise without seeding. In addition, radar reflectivity echoes quickly decreased in all regions without seeding. The evolutionary characteristics of radar echoes and in situ observed cloud and precipitation properties suggest that the response of cloud microphysical characteristics to seeding varies with time and location due to various seeding potentials, leading to different formation conditions of both cloud‐top icing and cloud tracks.

The advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius r_e; calculates cloud-base temperature T_b based on the warmest cloudy pixels; calculates cloud-base height H_b and pressure P_b based on T_b and meteorological data; calculates cloud-base updraft W_b based on H_b; calculates cloud-base adiabatic cloud drop concentrations N_d,a based on the T–r_e relationship, T_b, and P_b; calculates cloud-base maximum vapor supersaturation S based on N_d,a and W_b; and defines N_d,a/1.3 as the cloud condensation nuclei (CCN) concentration N_CCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.

Cold clouds are the main operation target of artificial precipitation enhancement, and its key is to find a supercooled cloud water area where the catalyst can be seeded to promote the formation of precipitation particles and increase precipitation to the ground. Based on the multi-spectral characteristics of the Fengyun-4A (FY-4A) satellite, a methodology for identifying supercooled cloud water is developed. Superimposed by a cloud top brightness temperature of 10.8 µm, a combination of 0.46 µm, 1.6 µm, and 2.2 µm red–green–blue (RGB) composites are used to identify the cloud phase and to obtain the real-time supercooled cloud water distribution every 5 min and in a 2 km resolution for the whole coverage of China. Based on the RGB composition, the supervised machine learning method K-mean clustering was applied to classify the cloud top phase. The results were validated extensively with Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). It is worthwhile to highlight that the corresponding hit rate reached 87% over the full disk domain for both the summer and winter seasons. Furthermore, on 29 November 2019, microphysical properties were measured, and the data of supercooled cloud droplets and ice crystals were obtained using YUN-12 transport aircraft in Taiyuan. After simultaneously matching the satellite with an airborne track, the cloud particle image data were obtained near the cloud top and within the clouds during the climb and descending stages of the flight. The phase obtained from the microphysical properties of supercooled cloud droplets and ice crystals was compared with cloud phase results identified by FY-4A and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud phase products. The case study and comparison show that (1) the supercooled water clouds and ice particles identified by FY-4A are in good agreement with those from the airborne measurement at the cloud top and within the cloud and (2) the positions and shapes of water clouds and ice clouds identified by FY-4A correspond well with MODIS cloud phase products. However, there is a small deviation in the extent of ice clouds, which is mainly located in the transition area between ice clouds and water clouds. The extent of ice clouds identified by FY-4A is slightly larger than that of MODIS products. Combined with airborne detection, the comparison shows that the ice clouds identified by the FY-4A satellite are consistent with aircraft detection. The supercooled cloud water identified by FY-4A can meet the needs of the operational precipitation enhancement of cold clouds, improve operational effectiveness, and promote the application of satellite technology for weather modification.

The ice crystal habits, distributions and growth processes in two snowfall cloud cases on 29 November 2009 and 3 March 2012 in northern China were compared and analyzed with aircraft data. The results showed that ice crystal habits were affected by the height of ice clouds. Ice crystals in clouds with cloud top temperatures of −12.6 °C were predominantly needle, plate, dendrite and irregular. When the cloud top temperature was lower than −19.5 °C, plates, dendrites and irregular ice crystals were observed in addition to needles, capped-column crystals were observed in the lower and middle layers of clouds, and column crystals were observed in the upper layer of clouds. The liquid water content of the two snowfall processes was lower than 0.1 g·m⁻³. Ice particles grew mainly via deposition, riming and aggregation processes. On 29 November, the liquid water content of the stratospheric mixed snowfall cloud was distributed in the lower part of the cloud. The maximum values of particle concentration and ice water content detected by a cloud imaging probe were 187 L⁻¹ and 1.05 g·m⁻³, which were at −8.7 °C, and the ice water content was higher. On 3 March, the liquid water content of snowfall in stratiform clouds was located in the middle layer, and the maximum ice water was low, which was only 0.052 g m⁻³. The ice water value on 29 November was higher, which was mainly due to the convective zone embedded in the cumulus mixed cloud containing a large number of riming and aggregated snow crystals. Using an exponential function to fit the crystal spectrum of the two snowfall processes, N₀ and λ were 10⁹−10¹¹ m⁻⁴ and 10⁸−10¹⁰ m⁻⁴ and 10³−10⁴ m⁻¹ and 10⁴ m⁻¹, respectively. Compared with 3 March, N₀ on 29 November was larger and the variation range of λ was one more order of magnitude. N₀ and λ conformed to a power function distribution. By analyzing the scatter plot of the correlation coefficient and slope, it was found that the exponential function can accurately express the crystal spectrum of snow clouds.

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.