Characteristics of Deep Convective Systems and Initiation during Warm Seasons over China and Its Vicinity

The spatiotemporal statistical characteristics of warm-season deep convective systems, particularly deep convective systems initiation (DCSI), over China and its vicinity are investigated using Himawari-8 geostationary satellite measurements collected during April-September from 2016 to 2020. Based on a satellite brightness temperature multiple-threshold convection identification and tracking method, a total of 47593 deep convective systems with lifetimes of at least 3 h were identified in the region. There are three outstanding local maxima in the region, located in the southwestern, central and eastern Tibetan Plateau and Yunnan-Guizhou Plateau, followed by a region of high convective activities in South China. Most convective systems are developed over the Tibetan Plateau, predominantly eastward-moving, while those developed in Yunnan-Guizhou Plateau and South China mostly move westward and southwestward. The DSCI occurrences become extremely active after the onset of the summer monsoon and tend to reach a maximum in July and August, with a diurnal peak at 11–13 LST in response to the enhanced solar heating and monsoon flows. Several DCSI hotspots are identified in the regions of inland mountains, tropical islands and coastal mountains during daytime, but in basins, plains and coastal areas during nighttime. DCSI over land and oceans exhibits significantly different sub-seasonal and diurnal variations. Oceanic DCSI has an ambiguous diurnal variation, although its sub-seasonal variation is similar to that over land. It is demonstrated that the high spatiotemporal resolution satellite dataset provides rich information for understanding the convective systems over China and vicinity, particularly the complex terrain and oceans where radar observations are sparse or none, which will help to improve the convective systems and initiation nowcasting.

The airport-sCAle seveRe weather nowcastinG prOject (CARGO)

<p>Monitoring and predicting extreme atmospheric events, such as deep convective systems, is very challenging especially when they develop locally in a short time range. Despite the great improvement in model parametrization and the use of satellite measurements, there are still <strong>l</strong>arge uncertainties on the knowledge of the dynamical processes of deep convective systems at local scale.</p><p>We use an innovative approach integrating a dense network of in situ measurements and satellite-based observations/products for the improvement of meteorological nowcasting at airport spatial scale focusing on the Malpensa airport (Italy). We add to the standard atmospheric parameters analysis, the information of integrated water vapour and lightning spatio-temporal behaviour (potential heavy rain precursors) during heavy rain phenomena detected by meteorological radars. The study is based on the anomaly of each atmospheric parameter during a convective event in comparison to its climatology in non-pre-convective environment, so that we are able to detect the variation with respect to the “standard” conditions. The ground based GNSS receivers (allowing the determination of the integrated water vapour trend before and during the storm), together with the lightning detectors, the weather stations (providing the trend of temperature, humidity and wind fields), the radiosondes and the GNSS radio occultations (allowing the estimation of vertical profiles of temperature, pressure and humidity) provide information on the pre-convective and non-pre-convective environment as a 3D picture of the atmospheric conditions.</p><p>The final goal is the test of a severe weather events nowcasting algorithm with high spatial resolution, and based on neural networks, for improving aviation safety. This is followed by the development of a user-friendly tailored final product, easily understandable by the Air Traffic Management stakeholder.</p><p>We have collected more than 600 cases suitable to develop the neural network algorithm. We show here the algorithm implementation and the meteorological characterization of deep convection usually developing on the Malpensa airport area.</p>

Analysis and nowcasting of deep convective systems over Germany in multi-source data

<p>By using a multi-source data set consisting of high resolution satellite, radar, lightning, and model data this study presents the analysis of characteristics of deep convective systems over Germany and first results of a new model to predict the remaining lifetime of existing thunderstorms. Contrary to previous studies, the analysis was performed for the full mixture of observed convective systems regardless of their organization type, since our focus is an operational forecasting environment where no simple method is available to differentiate organization types. Basis for the study are all deep convective cell detections in satellite data (using Cb-TRAM, Thunderstorm Tracking and Monitoring) in a five month period (June 2016, May, June, and July 2017, and June 2018). The lifetimes of all cells are normalized, averaged and separated into life cycle phases to investigate the behavior of the parameters from the different data sources during the detected lifetime. Furthermore, the thunderstorm cells are sorted by their lifetime to determine differences between the characteristics of long- and short-lived convective systems. Parameters with predictive skill are then combined with fuzzy logic to determine the actual stage of a thunderstorm, and to nowcast its remaining lifetime. It will be shown that the new lifetime prediction model contributes to an improvement of the thunderstorm nowcasting.</p>