The study investigates the role of NWP filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of 14 different experiments, covering Central Europe. These experiments compiles different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures. As stability filter parameters from Numerical Weather Prediction (NWP) are used. The brightness temperature information results from the IR SEVIRI instrument on-board of Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60 %. The false alarms results from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70 % . A brief review and reflection of the literature clarifies that the this function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) works well as stability filter.

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight savety. Monitoring and forecast of thunderstorms are therefore important topics. In this work a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWAARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼ 10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 hours, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is with 0.64 still quite high for the 60 minute forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD) and used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms usually rely on accurate atmospheric motion vectors (AMVs) for the estimation of cooling rate of convective clouds, which corresponds to the updraft strength of the cloud objects. In this study we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose the absolute value of the vector product of two vectors derived from the observed radiances emitted by the water vapor channels in 2 subsequent satellite images is calculated. The x and y components of the vectors consists of the temporal change of the spatial brightness temperature gradients in x- and y-direction, the z component consists of the temporal variation of the brightness temperature at pixel level. The absolute value of the vector product is referred to as normalized updraft strength (NUS). The performance of the method has been investigated for 2 summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66 % for the 2016 period and 62 % for the 2017 period demonstrate the good performance of the method. The POD values for the experiments with the highest CSI values are 90.3 for 2016 period and 87.6 for the 2017 period, respectively The corresponding FAR values are 28.2% (2016) and 32.5 % (2017), respectively. In summary, the method has the potential to reduce the forecast lead time significantly, and can be quite useful in regions without a well maintained radar network.