A winter precipitation-type prediction is a challenging problem due to the complexity in the physical mechanisms and computability in numerical modeling. In this study, we introduce a new method of precipitation-type prediction based on the machine learning approach LightGBM. The precipitation-type records of the in situ observations collected from 32 national weather stations in northern China during 1997–2018 are used as the labels. The features are selected from the conventional meteorological data of the corresponding hourly reanalysis data ERA5. The evaluation results of the model performance reflect that randomly sampled validation data will lead to an illusion of a better model performance. Extreme climate background conditions will reduce the prediction accuracy of the predictive model. A feature importance analysis illustrates that the features of the surrounding area with a –12 h offset time have a higher impact on the ground precipitation types. The exploration of the predictability of our model reveals the feasibility of using the analysis data to predict future precipitation types. We use the ECMWF precipitation-type (ECPT) forecast products as the benchmark to compare with our machine learning precipitation-type (MLPT) predictions. The overall accuracy (ACC) and Heidke skill score (HSS) of the MLPT are 0.83 and 0.69, respectively, which are considerably higher than the 0.78 and 0.59 of the ECPT. For stations at elevations below 800 m, the overall performance of the MLPT is even better.

On May 17, 2019, a rare severe convective weather occurred in Beijing, accompanied by local heavy rainstorm, hail, thunderstorm and gale. This severe convective weather occurred significantly earlier than normal years, bringing great challenge to the forecast. Using multiple observation data and radar four-dimensional variational assimilation products to analyze the triggering and development evolution of this severe convection. Under the conditions of no obvious weather scale system and local high potential unstable energy, the eastward advancement of the sea breeze front was the main factor triggering strong convection. As the northwest wind in the air increasing, the environmental conditions became stronger vertical wind shear, which was beneficial for the storm to maintain for a longer period of time. The supercell was the main cause of the convective weather. During the development of storms, they split into two parts and moved counterclockwise. The southern echo gradually weakened as it moved northward, while the northern echo moved southward, strengthening and developing into a super cell accompanied by a mesocyclone. The significant fluctuations in the height of the 0 ℃ layer within a small range resulted in different melting rates of hail during its descent, leading to the formation of spiky hail.