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The use of SAR data for flood mapping is well established. However, the problem of the missed detection of rapidly evolving floods remains. Recently, the Italian Space Agency deployed the COSMO-SkyMed Second Generation (CSG) constellation, with an on-demand capability that makes it possible to reduce the number of missed floods. However, for on-demand SAR acquisitions, pre-flood images are generally not available, and change-detection methods, commonly adopted for flood mapping using SAR, cannot be applied. This study focused on the high-resolution CSG images of a flood that occurred in central Italy. An accurate analysis of the radar responses of the different targets included in the scenes observed by GSG was performed. Then, a methodology to detect floods using high-resolution single SAR images was developed. The methodology was based on image segmentation and fuzzy logic. Image segmentation allowed us to analyze homogeneous areas in the CSG images. Fuzzy logic was used to integrate the SAR data with ancillary information that was useful when change-detection methods could not be applied. A comparison with the maps produced by the Copernicus Emergency Service, using high-resolution optical images, demonstrated the reliability of the methodology. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

Dealing with lightning performance computation, researchers usually consider only negative downward first return strokes and the relevant channel-base peak current distribution. The aim of this article is to account for the lightning flash mechanism, which means to include also downward negative subsequent strokes and downward positive strokes, taking into account available distributions of the number of strokes per flash (i.e., the flash multiplicity) and of the distance among the impact points of the strokes within the same flash (i.e., the channel terminations distance). In this regard, to the best of the authors’ knowledge, the present study is the first one taking into account the multiple ground channel terminations in the lightning performance assessment. The obtained results show that both the negative subsequent strokes and multiple channel termination determine a significant increase in the estimated number of flashovers per year, and they need to be considered in the lightning performance assessment. On the other hand, considering positive downward strokes, a less remarkable increase in the estimated number of flashovers per year is observed. [ABSTRACT FROM AUTHOR]

This article proposes a method for the derivation of a formula that links the maximum lightning-induced-overvoltage occurring on an overhead distribution line with the variables that characterize the lightning return stroke, such as the peak current, front duration, and stroke location. The coefficients of such formulas are obtained thanks to a limited number of lightning field-to-line coupling simulations, thus guaranteeing a significant computational time saving with respect to already existing methods. The method is presented first with a preliminary analysis to get some insight into the functional relationship between each of such variables and the overvoltage. Then, the method is presented and validated against numerical calculations performed with a dedicated code, both in terms of punctual evaluations and overvoltage distribution. [ABSTRACT FROM AUTHOR]

The cumulus parameterization is widely recognised as a crucial factor in tropical meteorology: this paper intends to shed further light on the effects of convection parameterization on tropical cyclones’ numerical predictions in the “grey zone” (10–1 km grid spacing). Ten experiments are devised by combining five different convection treatments over the innermost, 5 km grid spacing, domain, and two different global circulation model datasets (IFS and ERA-Interim). All ten experiments are finally analysed and compared to observations provided by the National Hurricane Center’s best track record and multisatellite rainfall measurements. Results manifestly point to the superiority of employing no convective parameterization at the scale of 5 km versus the usage of any of those provided by WRF to reproduce the case study of Hurricane Tomas, which hit the Lesser Antilles and Greater Antilles in late October and early November 2010. [ABSTRACT FROM AUTHOR]

Extreme hydrometeorological events such as flash floods have caused considerable loss of life and damage to infrastructure over recent years. Flood events in the Mediterranean region between 1990 and 2006 caused over 4,500 fatalities and cost over €29 billion in damage, with Italy one of the worst affected countries. The Distributed Computing Infrastructure for Hydro-Meteorology (DRIHM) project is a European initiative aiming at providing an open, fully integrated eScience environment for predicting, managing, and mitigating the risks related to such extreme weather phenomena. Incorporating both modeled and observational data sources, it enables seamless access to a set of computing resources with the objective of providing a collection of services for performing experiments with numerical models in meteorology, hydrology, and hydraulics. The purpose of this article is to demonstrate how this flexible modeling architecture has been constructed using a set of standards including the NetCDF and WaterML2 file formats, in-memory coupling with OpenMI, controlled vocabularies such as CF Standard Names, ISO19139 metadata, and a Model MAP (Metadata, Adaptors, Portability) gateway concept for preparing numerical models for standardized use. Hydraulic results, including the impact to buildings and hazards to people, are given for the use cases of the severe and fatal flash floods, which occurred in Genoa, Italy in November 2011 and October 2014. [ABSTRACT FROM AUTHOR]

Many meteorological organizations plan to substantially increase the resolution of the limited-area models used for severe weather prediction. Such an approach does not guarantee a priori the reduction of the uncertainty of the decision maker in the prediction of severe weather impact. A deep moist convective process, a supercell, is studied in a simplified atmospheric scenario by means of high-resolution numerical simulations with the Consortium for Small-Scale Modeling (COSMO) model. Different turbulence closure models and their impact on the spatiotemporal properties of storm processes are discussed. In the range of grid spacing between 1 km and 100 m, also termed ''terra incognita,'' the simulations of a supercell converge with respect to flow field structure, transport properties, and precipitation fields when a turbulence closure derived from large-eddy simulation (LES) is used. In contrast, more simplified turbulence closures such as 1D (vertical) boundary layer approximations yield substantially worse results than the 0.2-km LES reference simulation. [ABSTRACT FROM AUTHOR]

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed. [ABSTRACT FROM AUTHOR]

This paper presents a new method, based on clustering and thresholding, to automatically perform binary change detection in multitemporal spectral indices. The method is denoted as Buffer-From-Cluster Approach (BFCA). To estimate the distributions of changed and unchanged pixels, as needed for the purpose of a reliable thresholding of a spectral index, a clustering algorithm is preliminarily applied to identify image objects possibly corresponding to areas where significant changes occurred. Then, a buffer zone is created around the selected cluster to identify unchanged areas surrounding changed ones. The cluster and the buffer zone are jointly analyzed to estimate the distributions of changed and unchanged pixels and to verify that they can be distinguished from each other. Finally, the results of thresholding and clustering are combined to generate the binary change map. The BFCA has been conceived to map the extent of the areas affected by a natural disaster like wildfire. To validate the proposed method, burned area maps produced by applying the BFCA to spectral indices derived from Sentinel-2 data have been compared to maps produced by the Copernicus Emergency Management Service. For testing the multi-hazard detection capability, the same kind of exercise has been carried out for a flooding test case too. The positive results of the comparison have confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%. [ABSTRACT FROM AUTHOR]