Using convection‐resolving Weather Research and Forecasting simulation, this work studies a heavy rainfall event that occurred in the Shanxi Plateau, North China on 21–22 May 2017. Of particular interest is the influence of multiscale orography on the initiation and maintenance of the precipitating convective system. Results showed that the initial convection was triggered over the small‐scale Taiyue Mountain in the Shanxi Plateau. In the nighttime, with a stable boundary layer, orographic gravity waves were generated by the Taiyue Mountain. The gravity wave‐induced lifting cooled and saturated the lower troposphere adiabatically, thereby producing a moist absolutely unstable layer favorable for subsequent convection initiation. In the daytime, a mountain–plains solenoid (MPS) was established due to the differential solar heating between the Taiyue Mountain and Changzhi Basin. The MPS upslope wind increased the low‐level convergence above the mountain, which eventually promoted the convection. This convection moved eastward and grew into a quasi‐stationary, quasi‐linear convective system in the eastern Shanxi Plateau, which was significantly impacted by the daytime MPS caused by the Shanxi Plateau and North China Plain. The low‐level easterlies associated with the plateau‐scale MPS hindered the eastward propagation of the precipitating system and enhanced the low‐level vertical wind shear ahead of the quasi‐linear convective system, resulting in an overall balance with the system cold pool circulation. These findings provide some new insights into the development of a precipitating convective system under the influence of multiscale orography in a relatively pure environment of weak synoptic forcing and convective available potential energy.

Significant variability of raindrop size distributions (DSDs) has been observed in the “21·7” Henan extremely heavy rainfall event (the “21·7” Henan EHR event), while the capability of model to reproduce such complicated heavy rainfall DSDs is yet unclear. This study primarily evaluates the simulated DSDs of multiple microphysics schemes by comparing with the observations from a network of 50 disdrometers. Constrained DSD variability is identified in most schemes that the simulated raindrop mean sizes are gradually restricted around specific values as the growth of heavy rainfall intensity. The schemes are also incapable of reproducing the different raindrop mean sizes from deep convection and shallow convection. Moreover, simulations show unrealistic evolutions of raindrop mean size standard deviations as height declines. By investigating the empirical formula and performing sensitivity experiment, the constrained DSD variability in heavy rainfall is largely blamed on the insufficient parameterizations of the self‐collection (breakup) processes.

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.

By analyzing observations and simulations from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), we investigated instant and delayed responses of large-scale atmospheric circulations and precipitation to biomass burning (BB) aerosols over the Indochina Peninsula (ICP) in the peak emission of March. The results show that the BB aerosols inhibit precipitation over the ICP in March and promote precipitation from early April to mid April. Specifically, the March BB aerosols over the ICP can induce mid- to lower-tropospheric heating and planetary boundary layer cooling to enhance local atmospheric stability; meanwhile, the perturbation heating can trigger an anomalous low in the lower troposphere to moisten the mid troposphere. However, the convection suppression due to the stabilized atmosphere dominates over the favorable water-vapor condition induced by large-scale circulation responses, leading to overall reduced precipitation over the ICP in March. For the delayed effect, the anomalous low can provide more water vapor as the monsoon advances in early April, although it becomes much weaker without BB aerosols' strong heating. On the other hand, the convective instability above 850 hPa is enhanced by more water vapor, resulting in enhanced precipitation over the ICP, northern South China Sea, and southern China. Thereafter, the condensational latent heating gradually takes over from the BB aerosol radiative heating, acting as the main driver for maintaining the anomalous circulation and thus the delayed effect in mid April.

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%.

Microphysical characteristics of heavy rainfall during the Meiyu season are revealed using observations from a novel network of 293 disdrometers collected over four Meiyu seasons and Global Precipitation Measurement Dual‐Frequency Precipitation Radar data spanning nine seasons. Heavy rainfall events are classified into the warm‐sector (Warm) and frontal (Front) types based on their locations relative to the Meiyu fronts. Compared to the Front type, the Warm type shows a significantly deeper convective structure with a larger number of large‐size hydrometeors. This results in lower number concentration and larger mean size of surface raindrops. Further analysis combining diurnal cycles and environmental thermodynamic and dynamic conditions reveals that convection associated with the Warm type tends to develop in the afternoon due to solar heating, whereas that of the Front type primarily occurs at night, driven by strong low‐level moisture convergence and dynamical lifting near the frontal zone.