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The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modelling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting High rEsolution ReAnalysis over Italy (SPHERA) reanalysis predictors describing hail-favourable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing-level height. A hail frequency estimate over the extended summer season (April–October) in south-central Europe is presented for a test period of 5 years (2016–2020). OT-derived hail frequency peaks at around 15:00 UTC in June–July over the pre-Alpine regions and the northern Adriatic Sea. The hail proxy statistically matches with ∼63 % of confirmed ESWD reports, which is roughly 23 % more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights the enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstone diameters ≥ 3 cm). Further, signatures for missed small-hail occurrences are identified, which are characterized by lower instability and organization and warmer cloud top temperatures. [ABSTRACT FROM AUTHOR]

Featured Application: Results provide essential information for the initial design and adaptation potential of Nature-Based Solutions. It may serve as guidance to practitioners and data providers. Flooding events, like in Germany in 2021, highlight the need for re-naturalising banks of rivers and streams to naturally mitigate future flooding. To identify potential areas for Nature-Based Solutions (NBS), the NBS Toolkit—a decision-support tool for Europe—was developed within the H2020 OPERANDUM project. The tool builds on suitability mapping, which is progressively adopted for pre-assessing areas for Nature-Based Solutions. The NBS Toolkit operates with European open-source data, which is available at different spatial resolutions. In this study, we performed a GIS-based analysis to examine the impact of different resolution data on the resulting suitability maps. The results suggest that for large-scale measures such as riparian forest buffers, coarser resolutions are sufficient and may save processing time and capacities. However, fine resolution datasets can bring added value to urban suitability mapping and are of greater importance for small-scale, local Nature-Based Solutions. [ABSTRACT FROM AUTHOR]

The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring. [ABSTRACT FROM AUTHOR]

The challenges associated with reliably observing and simulating hazardous hailstorms call for new approaches that combine information from different available sources, such as remote sensing instruments, observations, or numerical modeling, to improve understanding of where and when severe hail most often occurs. In this work, a proxy for hail frequency is developed by combining overshooting cloud top (OT) detections from the Meteosat Second Generation (MSG) weather satellite with convection-permitting SPHERA reanalysis predictors describing hail-favorable environmental conditions. Atmospheric properties associated with ground-based reports from the European Severe Weather Database (ESWD) are considered to define specific criteria for data filtering. Five convection-related parameters from reanalysis data quantifying key ingredients for hailstorm occurrence enter the filter, namely: most unstable convective available potential energy (CAPE), K index, surface lifted index, deep-layer shear, and freezing level height. A hail frequency estimate over the extended summer season (April-October) in south-central Europe is presented for a test period of 5 years (2016-2020). OT-derived hail frequency peaks at around 15 UTC in June-July over the pre-Alpine regions and the northern Adriatic sea. The hail proxy statistically matches with ~ 62% of confirmed ESWD reports, which is roughly 22% more than the previous estimate over Europe coupling deterministic satellite detections with coarser global reanalysis ambient conditions. The separation of hail events according to their severity highlights enhanced appropriateness of the method for large-hail-producing hailstorms (with hailstones diameters > 3 cm). Further, signatures for small-hail missed occurrences are identified, which are characterized by lower instability and organization, and warmer cloud-top temperatures. [ABSTRACT FROM AUTHOR]

This paper investigates the surface-layer processes associated with the morning transition from nighttime downslope winds to daytime upslope winds over a semi-isolated massif. It provides an insight into the characteristics of the transition and its connection with the processes controlling the erosion of the temperature inversion at the foot of the slope. First, a criterion for the identification of days prone to the development of purely thermally driven slope winds is proposed and adopted to select five representative case studies. Then, the mechanisms leading to different patterns of erosion of the nocturnal temperature inversion at the foot of the slope are analyzed. Three main patterns of erosion are identified: the first is connected to the growth of the convective boundary layer at the surface, the second is connected to the descent of the inversion top, and the third is a combination of the previous two. The first pattern is linked to the initiation of the morning transition through surface heating, and the second pattern is connected to the top-down dilution mechanism and so to mixing with the above air. The discriminating factor in the determination of the erosion pattern is identified in the partitioning of turbulent sensible heat flux at the surface. Significance Statement: The purpose of this study is to improve our understanding of the thermally driven slope circulations with a focus on the unsteady processes associated with the morning transition and the erosion patterns of the nocturnal temperature inversion, so far in the literature less investigated and understood than the evening transition. Understanding this diurnal process will advance our abilities to model it and to improve the accuracy of weather forecasting in complex terrain. [ABSTRACT FROM AUTHOR]

Regional reanalyses allow us to better describe weather patterns related to rapidly evolving high‐impact events thanks to substantially finer detailing than global datasets. However, most regional datasets still do not permit the explicit representation of deep convection. SPHERA (High rEsolution ReAnalysis over Italy) is a new high‐resolution convection‐permitting reanalysis centred over Italy. It covers 26 years (1995–2020), is based on the non‐hydrostatic limited‐area model COSMO, and is produced by dynamically downscaling the global reanalysis ERA5. A nudging data assimilation scheme steers the model toward observations. The fine horizontal grid spacing of 2.2 km allows us to switch off deep‐convection parametrization. This study reports the added value of SPHERA over ERA5 in representing rainfall over Italy, particularly for severe precipitation, using rain‐gauge observations during 2003–2017 as reference. Concerning the 95th percentile of spatial rainfall distributions, ERA5 presents dry estimates with biases reaching −12 mm·day−1 over mountainous regions. At the same time, the enhanced locally driven effects of SPHERA produce seasonal biases ranging from wet in JJA (up to +12 mm·day−1) to dry in DJF (down to −9 mm·day−1). For daily maximum rates, the regional reanalysis shows better skill in detecting occurred events (with hit rates higher than ERA5 by roughly 0.4 points in the range of 15–80 mm·day−1) and frequency biases closer to 0 at all intensities when coming to daily averages. Similarly, for hourly maximum accumulations, improved adherence to observations is detected for SPHERA at all intensities, conversely to the underprediction of the global driver (with frequency biases <1 starting from 1.5 mm·hr−1). Additionally, the analyses of two specific events reveal the enhancements of SPHERA in simulating extreme precipitation, with a maximum intensity underestimation on the order of 24% versus the 73% detected for ERA5. Further improvements include the spatial detailing, timing, and temporal evolution of the events. [ABSTRACT FROM AUTHOR]