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Due to technical progress in sensor technology and computer power, observation and model data are operationally available today with a high spatial and temporal resolution, suitable for thunderstorm detection and prediction. However, nowcasting the remaining lifetime of an observed thunderstorm is still a challenge to date. To improve nowcasting of deep convective cells, we developed the algorithm LOC-lifetime that predicts the remaining lifetime of thunderstorms based on life-cycle signatures present in satellite, radar, lightning and numerical weather prediction model data. We use the mathematical method “fuzzy logic” to combine this multi-source input and to categorize the thunderstorm evolution into the life-cycle sets growth and decay. We analyzed a data set of almost 1,800 thunderstorms that occurred during the summer months June 2016, May, June, July 2017 and June 2018. The data reveal highly variable life cycles which make it difficult to predict the remaining lifetime on basis of life-cycle statistics. Nevertheless, LOC-lifetime offers an improved nowcasting quality compared to a simpler nowcasting method as it increases the probability of correct prediction and reduces the root-mean-square error. Therefore, we propose the lifetime prediction via LOC-lifetime as a useful tool in combination with other existing algorithms to nowcast and forecast thunderstorms.

The fourth European Nowcasting Conference took place as an online event from 21 to 24 March 2022, organized by the EUMETNET (European National Meteorological and Hydrological Services Network) Nowcasting Program (E‑NWC), and kindly supported by EUMETCAL (EUMETNET Education and Training Collaborative Network of the National Meteorological Services within Europe). More than 110 participants attended the conference. 46 conference's presentations were given within the 0) opening session, a session on 1) observation as a basis for nowcasting, 2) seamless prediction with a special focus on Artificial Intelligence (AI), 3) nowcasting systems, products, and techniques and 4) verification, impacts on society, as well as applications and aspects of users. This report summarizes the scientific contributions presented and the discussed scientific questions.

Tornadic storms with a tornado intensity of F2 or stronger on the Fujita scale that occurred in recent years (between 2013 and 2020) in Germany were analysed in detail. The 20 tornadoes, which developed on 17 different days, occurred in various parts of Germany. The majority of the analysed tornadoes occurred from May to early September. The other four events are typical cold-season cases that differ from the warm-season cases in some aspects which are discussed where applicable. Similarities and differences of the prevailing synoptic and mesoscale conditions are assessed in addition to the convective environment of the events. On the majority of the analysed tornadic days, the event happened on the forward flank of a long wave trough that was slowly propagating eastward. The second typical situation is a short wave trough passing rather fast over the area of interest from West to East. All cold-season cases occurred in high shear, low CAPE environments. Furthermore, the type of convection is analysed using a multi-source approach to best characterise the events. For this purpose, radar reflectivity and rotation data were combined with lightning detection in order to analyse the tornadic storms with respect to storm mode and storm evolution as well as lightning and rotation characteristics. The most common convective mode is linearly organised convection followd by multicellular storms. In many of the warm-season cases, radar radial wind data showed a persistent rotation track. The lightning activity of the warm-season season tornadic storms was high. In many cases, a lightning jump occurred between 5 and 120 minutes before the event.

This study analyses characteristics of deep moist convection (DMC) over Germany with the aim to select relevant parameters that have the skill to improve the identification of current life cycle phase and the forecast of a lifetime of DMCs in an operational weather forecasting environment. No differentiation between thunderstorm organization types is done, since no simple differentiation method is available in an operational environment. In contrast to previous analyses, multiple data sources are used synchronously to explore an extensive data set of DMCs at high resolution in space and time. Basis of our analysis are all DMC detections in satellite data (using Cb‑TRAM – Thunderstorm Tracking and Monitoring) in a five month period (June 2016, May/June/July 2017, and June 2018). For each of these DMCs the time series of selected parameters from satellite, ground-based radar, lightning detection, and numerical weather prediction (NWP) model data are inspected. In search for clear signatures of expectable lifetime and differences between short- and long-lived DMCs, all thunderstorm systems are sorted by their lifetime. In addition, they are separated into four life cycle phases: 1. early growth, 2. advanced growth, 3. maturity, and 4. decay. Generally, it turns out that satellite, radar, and lightning data are the most suitable data to determine the actual life cycle phase of a DMC. It is shown that long-lived DMCs are, on average, related to lower minimum cloud top temperature and mid-level relative humidity and higher maximum of coverage area, vertically integrated water and lightning activity during their life cycle than short-lived systems. The NWP model parameters have a diagnostic potential to identify the remaining lifetime in connection with the observational data, but do not contain information about the actual life cycle phase. The results obtained in this study will be used for an investigation of their potential application in a nowcasting model, in order to determine the current phase of an observed DMC and to predict its remaining lifetime.

The third European Nowcasting Conference took place in Madrid, Spain, from 24 to 26 April 2019. The conference was structured into four thematic sessions i) observations as basis for nowcasting, ii) seamless prediction, iii) nowcasting techniques, systems and products, iv) verification, societal impacts, applications and user aspects. This report summarizes the scientific contributions presented and the discussed scientific questions.

The 2nd European Nowcasting Conference took place in Offenbach, Germany, on 3–5 May 2017. The conference was structured into four thematic sessions i) observations as basis for nowcasting, ii) nowcasting techniques and systems, iii) application, user aspects and verification, and iv) combination of numerical weather prediction and nowcasting. This report summarises the scientific contributions presented and the open scientific questions discussed at the conference.

Two severe summer-time convective events in Germany are investigated which can be classified by the prevailing synoptic conditions into a strong and a weak forcing case. The strong forcing case exhibits a larger scale precipitation pattern caused by frontal ascent whereas scattered convection is dominating the convective activity in the weak forcing case. Other distinguished differences between the cases are faster movement of convective cells and larger regions with significant loss mainly due to severe gusts in the strong forcing case. A comprehensive set of various observations is used to characterise the two different events. The observations include measurements from a lightning detection network, precipitation radar, geostationary satellite and weather stations, as well as information from an automated cell detection algorithm based on radar reflectivity which is combined with severe weather reports, and damage data from insurances. Forecast performance at various time scales is analysed ranging from nowcasting and warning to short-range forecasting. Various methods and models are examined, including human warnings, observation-based nowcasting algorithms and high-resolution ensemble prediction systems. The analysis shows the advantages of a multi-sensor and multi-source approach in characterising convective events and their impacts. Using data from various sources allows to combine the different strengths of observational data sets, especially in terms of spatial coverage or data accuracy, e.g. damage data from insurances provide good spatial coverage with little meteorological information while measurements at weather stations provide accurate but pointwise observations. Furthermore, using data from multiple sources allow for a better understanding of the convective life cycle. Several parameters from different instruments are shown to have a predictive skill for convective development, these include satellite-based cloud-top cooling rates as measure for intensive convective growth, 3D-radar reflectivity, mesocyclone detection from doppler radar, overshooting top detection or lightning jumps to evaluate storm intensification and formation of severe weather. This synergetic approach can help to improve nowcasting algorihtms and thus the warning process. The predictability of the analysed severe convective events differs with different types of forcing which is reflected in both, convective-scale ensemble prediction system forecasts and human weather warnings. Human warnings show larger false alarm rates in the weak forcing case. Ensemble predictions are able to capture the characteristics of the convective precipitation. The forecast skill is connected strongly to the synoptic situation and the presence of large-scale forcing increases the forecast skill. This has to be considered for potential future warn-on-forecast strategies.

The Hans-Ertel Centre for Weather Research is a network of German universities, research institutes and the German Weather Service (Deutscher Wetterdienst, DWD). It has been established to trigger and intensify basic research and education on weather forecasting and climate monitoring. The performed research ranges from nowcasting and short-term weather forecasting to convective-scale data assimilation, the development of parameterizations for numerical weather prediction models, climate monitoring and the communication and use of forecast information. Scientific findings from the network contribute to better understanding of the life-cycle of shallow and deep convection, representation of uncertainty in ensemble systems, effects of unresolved variability, regional climate variability, perception of forecasts and vulnerability of society. Concrete developments within the research network include dual observation-microphysics composites, satellite forward operators, tools to estimate observation impact, cloud and precipitation system tracking algorithms, large-eddy-simulations, a regional reanalysis and a probabilistic forecast test product. Within three years, the network has triggered a number of activities that include the training and education of young scientists besides the centre's core objective of complementing DWD's internal research with relevant basic research at universities and research institutes. The long term goal is to develop a self-sustaining research network that continues the close collaboration with DWD and the national and international research community.