Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel² disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow ($\rho$), snowfall rate ($S$), and ice water content ($IWC$) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors ($D_m$, $lo{g}_{10}{N}_w$, and $\mu$) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The $D_m$ decreases and $lo{g}_{10}{N}_w$ and μ increase as the density increases. The $D_m$ and $lo{g}_{10}{N}_w$ increase and μ decreases with the increase of snowfall rate. The power-law relationship between $\rho$ and $D_m$ is obtained and the relationship between $S$ and $IWC$ is also derived.

The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL²) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (D_m), the shape factor (mu), the normalized intercept parameter (N_w), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall.

Tornado is a localized, small-scale severe convective weather phenomenon characterized by extreme destructiveness. Tornado detecting and warning mainly rely on Doppler weather radar, which identifies and tracks tornadoes by recognizing the tornado vortex signature and supercells in radar data. Artificial intelligence technology has been applied to tornado recognition in recent years. However, existing monitoring methods, especially those using unsupervised learning algorithms, still have limited recognition accuracy and timely warning, and usually struggle to strike a balance between detection accuracy and false alarm rate. A novel tornado detection algorithm TDA-DARKNet has been proposed to address the aforementioned issues. The algorithm integrates a dual attention mechanism, dense residual connections, and Kolmogorov–Arnold network (KAN). A tornado dataset suitable for deep learning has been formed, which utilizes features including radial velocity, reflectivity, velocity spectrum width, differential reflectivity, and correlation coefficient in radar data. The TDA-DARKNet algorithm was trained and tested using the tornado dataset, and evaluated in tornado cases. The experimental results show that TDA-DARKNet improves the detection probability and extends the lead time to a maximum of 42 min in strong tornado situations, while achieving 97.11% accuracy, 95.08% precision, indicating strong overall identification performance. In addition, by directly leveraging radar-based data for tornado identification, the algorithm eliminates the need for manual feature engineering, simplifies data processing, reduces complexity, and further enhances detection effectiveness.

Wind turbine clutter (WTC) can severely degrade the quality of meteorological radar data, particularly under complex weather conditions. Because WTC echoes can substantially overlap with precipitation echoes, they may introduce considerable errors into weather identification and forecasting. To address this issue, a deep learning model termed WTC-CNXGRN is proposed for automatic WTC detection. The model is built on the ConvNeXtV2 architecture and is designed to address characteristic WTC features, including unstable spatial structure, pronounced local texture variations, and complex multi-channel feature coupling. Multi-parameter dual-polarization weather radar data are used as input, while local statistical features and weighted polarimetric features are incorporated to strengthen the representation of spatial echo variations. In addition, a Global Response Normalization (GRN) mechanism is integrated into the network to adaptively recalibrate channel-wise responses, thereby enhancing the representation of critical discriminative features. On the test dataset, the proposed model achieves an accuracy of 98.74%, a precision of 98.41%, a probability of detection (POD) of 98.34%, and a false alarm ratio (FAR) of 1.59%, achieving overall better performance than the evaluated baseline models. Benefiting from its efficient architecture and improved detection strategy, the model provides low computational cost and fast inference, making it suitable for near-real-time operational applications. Qualitative tests using radar data from the Nantong region further provide preliminary evidence of the model’s cross-site applicability.

Dual-band weather radar networks enable complementary multi-radar observations, improving the accuracy, three-dimensional characterization, and early warning capability for severe convective weather. S-band radar provides strong penetration and long detection range but suffers from limited spatial resolution, whereas X-band radar offers high resolution with weaker penetration, posing challenges for dual-frequency data fusion. To address the resolution mismatch and fusion modeling issues between dual-band radars, this study proposes a super-resolution reconstruction method for S-band reflectivity based on dual-frequency radar observations. S-band and X-band radar data, together with key polarimetric parameters, are jointly incorporated into a deep neural network-based fusion model to enhance the spatial resolution of S-band reflectivity. Experimental results under typical severe weather conditions demonstrate that the proposed method achieves improved detail recovery and structural reconstruction, with the model achieving PSNR 30.84, SSIM 0.8755, and MAE 0.24178, which shows obvious advantages compared with other models and effectively enhances radar network data quality, and it outperforms single S-band super-resolution approaches in both objective metrics and subjective evaluations.

Warm-season precipitation over Sichuan, China, is jointly modulated by complex terrain, monsoon water vapor transport, and local convective activities, leading to significant spatiotemporal heterogeneity. However, radar observations over mountainous areas are frequently impaired by terrain blockage, beam shielding, and insufficient network coverage, which cause missing data and spatial discontinuity, thereby restricting the accurate monitoring of precipitation systems. To alleviate these problems, this study develops an Efficient Multi-Scale Attention (EMA) U-Net model integrated with Digital Elevation Model (DEM) information, termed EMA-U-Net-DEM, to retrieve radar composite reflectivity by utilizing multi-channel observations from the Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI). In the experiments, FY-4A AGRI multi-spectral measurements were used as model inputs, while radar composite reflectivity products from the Severe Weather Automatic Nowcasting (SWAN) system were applied as reference labels. The modeling and validation were carried out using warm-season (June–August) datasets over Sichuan Province. The results indicate that the proposed EMA-U-Net-DEM exhibits better performance than the traditional U-Net and several typical attention-based benchmark models. Quantitatively, the model achieves a root mean square error (RMSE) of 6.728 dBZ, a mean absolute error (MAE) of 4.788 dBZ, a coefficient of determination $R^2$ of 0.656, a peak signal-to-noise ratio (PSNR) of 25.243 dB, and a structural similarity index measure (SSIM) of 0.793. Categorical verification further reveals that the model yields the highest critical success indices (CSI) of 0.850, 0.560, and 0.364 in the reflectivity ranges of 0–25 dBZ, 25–45 dBZ, and 45–70 dBZ, respectively, demonstrating its superior ability in characterizing weak precipitation backgrounds, moderate precipitation structures, and intense convective cores. The performance enhancements are mainly attributed to the strengthened multi-scale feature extraction by the EMA module and the effective topographic constraints introduced by DEM data. This study confirms that the fusion of FY-4A multi-spectral observations and topographic information can effectively improve radar composite reflectivity retrieval over complex terrain, providing a feasible solution for precipitation monitoring, quantitative precipitation estimation, and severe weather nowcasting in mountainous regions with limited radar coverage.