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Based on turbulence measurements from sonic anemometers instrumented at multiple levels on a 356 m-tall meteorological tower located on the south coast of China, an observation study of the turbulent dissipation rate (ε) in a landfalling tropical cyclone boundary layer (TCBL) is conducted. Three indirect methods (i.e., the power spectra, the 2nd- and the 3rd-order structure functions) are compared for the calculation of ε. The 3rd-order structure function computes the smallest ε among the 3 methods, but shows the largest uncertainty. The 2nd-order structure function gives similar ε estimates as the power spectra, and is adopted for its reduced uncertainty. The measured ε in the landfalling TCBL is of O(10−1) m2 s−3, much greater than typical atmospheric boundary layer values as well as oceanic TCBL values. ε is found to scale with the local friction velocity rather than the surface friction velocity, implying a highly localized nature of turbulence. Conventional parameterizations of ε are evaluated against observations. Process-based ε models assuming a local balance between shear production and dissipation prove inadequate, as shear production merely accounts for half of the dissipation away from the surface. In comparison, scaling-based ε models used by planetary boundary layer (PBL) schemes are more advantageous. With both tuning of the model coefficients and adjustment of the dissipation length scale, the performance of an ε model in a widely used PBL scheme is shown to produce similar values to the observations.

Since strong convective weather is closely related to heavy precipitation, the nowcasting of convective weather, especially the nowcasting based on weather radar data, plays an essential role in meteorological operations for disaster prevention and mitigation. The traditional optical flow method and cross-correlation method have a low forecast accuracy and a short forecast leading time, while deep learning methods show remarkable advantages in nowcasting. However, most of the current forecasting methods based on deep learning suffer from the drawback that the forecast results become increasingly blurred as the forecast time increases. In this study, a weather radar nowcasting method based on the Temporal and Spatial Generative Adversarial Network (TSGAN) is proposed, which can obtain accurate forecast results, especially in terms of spatial details, by extracting spatial-temporal features, combining attention mechanisms and using a dual-scale generator and a multi-scale discriminator. The case studies on the forecast results of strong convective weather demonstrate that the GAN method performs well in terms of forecast accuracy and spatial detail representation compared with traditional optical flow methods and popular deep learning methods. Therefore, the GAN method proposed in this study can provide strong decision support for forecasting heavy precipitation processes. At present, the proposed method has been successfully applied to the actual weather forecasting business system.

The Three Gorges Dam (TGD) is one of the largest hydroelectric projects in the world. Monitoring the spatiotemporal distribution of extreme precipitation offers valuable information for adaptation and mitigation strategies and reservoir management schemes. This study examined variations in extreme precipitation over the Three Gorges Reservoir area (TGRA) in China to investigate the potential role of climate warming and Three Gorges Reservoir (TGR). The trends in extreme precipitation over the TGRA were investigated using the iterative-based Mann–Kendall (MK) test and Sen’s slope estimator, based on weather station daily data series and TRMM (Tropical Rainfall Measuring Mission) data series. The mean and density distribution of extreme precipitation indices between pre-dam and post-dam, pre-1985 and post-1985, and near and distant reservoir area were assessed by the Mann–Whitney test and the Kolmogorov–Smirnov test. The ratio of extreme precipitation to non-extreme precipitation became larger. The precipitation was characterized by increases in heavy precipitation as well as decreases in light and moderate rain. Comparing extreme precipitation indices between pre-1985 (cooling) and post-1985 (warming) indicated extreme precipitation has changed to become heavier. Under climate warming, the precipitation amount corresponding to more than the 95th percentile increased at the rate of 6.48%/°C. Results from comparing extreme precipitation for the pre- and post-dam, near reservoir area (NRA) and away from the reservoir area (ARA) imply an insignificant role of the TGR on rainfall extremes over the TGRA. Moreover, the impoundment of TGR did not exert detectable impacts on the surface relative humidity (RH) and water vapor pressure (WP).

To improve the accuracy of short-term (0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System (HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) three-dimensional variational data assimilation (3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station (AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting (QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score (FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events.