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One approach to improve classic advection methods is Short-Term Ensemble Prediction System (STEPS). STEPS decomposes the precipitation field into different spatial scales and filters those having a short lifetime. The latter is achieved by using an auto-regressive (AR) model that considers a sequence of recent observations. However, such a model tends to smooth the nowcasting fields especially in small but convective precipitation areas and at longer lead-times. With focus on the deterministic configuration of STEPS, i.e., the spectral prognosis model (SPROG), this work 1) extends the STEPS approach by estimating spatially localized parameters of the AR process, 2) conducts a sensitivity analysis of the SPROG model to the order of the AR process, the spatial decomposition levels, and post-processing, and 3) analyzes the forecast skill of the extended STEPS. For such purpose, the performance of the localized AR model was demonstrated and evaluated at several precipitation thresholds and window sizes using a varied set of precipitation events collected by the radar network of the German Weather Service. The statistical results exhibited an improved performance of the localized AR model over SPROG when both are evaluated at precipitation thresholds and window sizes larger than 0.1 mm h −1 and 1 km, respectively, and for lead-times up to 2 h. Moreover, the analysis suggested a first-order AR process, six cascades levels, and a mean adjustment post-processing procedure. Our results show a key role of the localization aspect when generating nationwide forecasts in scenarios that include large precipitation areas which are non-uniformly distributed having isolated convective features.

<p>Since major hail events are quite rare in Germany, there is a lack of information in hail occurrence, size and its spatio-temporal distribution. As hailstorms are often locally very limited events, the hail distribution is hard to analyze precisely. Hail reports can only give a first intuition about the amount of hail overall. There might be a bias in the amount of reports towards too many reports in highly populated areas, which could lead to an underrepresentation of reports in rural and sparsely populated areas. Areal information from weather radar networks can overcome this issue with a high spatio-temporal resolution. As an addition, data from the German Insurance Association (GDV) about damages through hail serve as a very certain source for hail occurrence.</p> <p>The German radar network consists of 17 dual-polarimetric radar systems, which cover Germany more or less completely. For the analysis of the hail distribution, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used to estimate the hail size. Those sizes are reduced to thresholds to obtain where hail is reasonable or have a significant large size. The results of MESH and VII are finally compared to the eyewitness reports sent to the European Severe Weather Database and the WarnWetter-App. An important comparison are the loss data by the GDV. It can give further insides into the amount and the size of hail.</p>

<p>Hail is a pronounced natural hazard in Germany. Nevertheless, major hail events are quite rare and there is a lack of information in hail occurrence and size and its spatiotemporal distribution. Measurement sensors that are able to detect hail (e.g. disdrometers) are in principle available in Germany, but the spatial density of those stations is far lower than the typical spatial extent of hail events. Furthermore, sensors for hail size estimation are still in evaluation stage and currently only located at a few selected places. Hail reports based on professional and particularly amateurish eyewitness become increasingly important. But besides a certain degree of subjectivity in the reported hail size, highly populated areas might be overrepresented compared to rural and sparsely populated areas. Areal information from weather radar networks can overcome this issue with a high spatiotemporal resolution. Because of the high update frequency and fast availability of radar data, an automatic hail detection and hail size estimation might provide valuable hints to forecasters and supports the warning decision process.       <br />The Deutscher Wetterdienst (DWD) utilizes a C-Band dual-polarimetric weather radar network consisting of 17 radar stations that provide ten volume scans and a terrain-following low-elevation scan every five minutes. The operationally used hydrometeor classification algorithm HYMEC processes data of reflectivity, differential reflectivity and co-polar correlation coefficient to distinguish between hail and other hydrometeors. With this classification a hail distribution over Germany can already be derived. For the analysis of hail sizes, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used. The latter method is motivated by a linear relation between maximum hail size and VII proposed by our forecasters based on their practical experience.        <br />This contribution will give an overview on the statistics of hail occurrence and hail size using the aforementioned algorithms in Germany during the convective seasons 2021 and 2022. Also, selected case studies are discussed in more detail. The results are compared against hail observations from manned and automatic weather stations, reports from the European Severe Weather Database and user reports from DWD’s WarnWetter-App.</p>