DOI: 10.5281/zenodo.10688746
title: Thunderstorm outflows in the Mediterranean Sea area

description: In the context of the European projects “Wind and Ports” (grant No. B87E09000000007) and “Wind, Ports and Sea” (grant No. B82F13000100005), an extensive in-situ wind monitoring network was installed in the main ports of the Northern Mediterranean Sea. An unprecedent number of wind records has been acquired and systematically analyzed. Among these, a considerable number of records presented non-stationary and non-Gaussian characteristics that are completely different from those of synoptic extra-tropical cyclones, widely known in the atmospheric science and wind engineering communities. The cross-checking with meteorological information allows to identify which of these events can be defined as thunderstorm winds, i.e., downbursts and gust fronts.

The scientific literature of the last few decades has demonstrated that downbursts, and especially micro-bursts, are extremely dangerous for the natural and built environment. Furthermore, recent trends in climate change seem to preview drastic future scenarios in terms of intensification and frequency increase of this type of extreme events. However, the limited space and time structure of thunderstorm outflows makes them still difficult to be measured in nature and, consequently, to build physically reliable and easily applicable models as in the case of extra-tropical cyclones. For these reasons, the collection and publication of events of this type represents a unique opportunity for the scientific community.

The dataset here presented was built in the context of the activities of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC), Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa). It collects 29 thunderstorm downbursts that occurred between 2010 and 2015 in the Italian ports of Genoa (GE) (4), Livorno (LI) (14), and La Spezia (SP) (11), and were recorded by means of ultrasonic anemometers (Gill WindObserver II in Genoa and La Spezia, Gill WindMaster Pro in Livorno). All thunderstorm events included in the database were verified by means of meteorological information, such as radar (CIMA Research Foundation is gratefully acknowledge for providing with most of the radar images), satellite, and lightning data. In fact, (i) high and localized clouds typical of thunderstorm cumulonimbus, (ii) precipitations, and (iii) lightnings represent reliable indicators of the occurrence of the thunderstorm event.

Some events were recorded by multiple anemometers in the same port area – the total number of signals included in the database is 99. Despite the limited number of points (anemometers), this will allow the user to perform cross-correlation analysis in time and space to eventually retrieve size, position, trajectory of the storm, etc.

The ASCII tab-delimited file ‘Anemometers_location.txt’ reports specifications of the anemometers used in this monitoring study: port code (Port code – Genoa-GE, Livorno-LI, La Spezia-SP); anemometer code (Anemometer code); latitude (Lat.) and longitude (Lon.) in decimal degree WGS84; height above the ground level (h a.g.l.) in meters; Instrument type. Bi-axial anemometers were used from the ports of Genoa and La Spezia, recording the two horizontal wind speed components (u, v). Three-axial ultrasonic anemometers were used in the port of Livorno, also providing the vertical wind speed component w (except bi-axial anemometers LI06 and LI07). All anemometers acquired velocity data at sampling frequency 10 Hz, sensitivity 0.01 m s-1 (except anemometers LI06 and LI07 with sensitivity 0.1 m s-1) and were installed at various heights ranging from 13.0 to 75.0 m, as reported in the file ‘Anemometers_location.txt’.

The ASCII tab-delimited file ‘List_DBevents.txt’ lists all downburst records included in the database, in terms of: event and record number (Event | record no.); port code (Port code); date of event occurrence (Date) in the format yyyy-mm-dd; approximate time of occurrence of the velocity peak (Time [UTC]) in the format HH:MM; anemometer code (Anemometer code). 

The database is presented as a zip file (‘DB-records.zip’). The events are divided based on the port of occurrence (three folders GE, LI, and SP). Within each folder, the downburst events that were recorded in that specific port are reported as subfolders (name format ‘[port code]_yyyy-mm-dd’) and contain the single anemometers signals as TAB-delimited text files (name format ‘[port and anemometer code]_yyyy-mm-dd.txt’). Each sub-dataset (file) contains 3(4) columns and 360.000 rows. The first column shows the 10-h time vector (t, ISO format) in UTC, while the remaining 2(3) columns report the 10-h time series of 10-Hz instantaneous horizontal (zonal west-to-east u, meridional south-to-north v) and, where available, vertical (positive upward w) wind speed components, centred around the time of maximum horizontal wind speed (vectorial sum of u and v). The choice of representation of the wind speed in a large time interval (10 hours) allows the user to perform a more comprehensive and detailed analysis of the event by taking into account also the wind conditions before and after the onset of the downburst phenomenon. 'Not-a-Number' (‘NaN’) values are reported in wind velocity signals when the instrument did not record valid data. Some wind speed records show noise in discrete intervals of the signal, which reflects in an increase of the wind speed standard deviation. A modified Hampel filter was employed to remove measurement outliers. For each wind speed signal, every data sample was considered in ascending order, along with its adjacent ten samples (five on each side). This technique calculated the median and standard deviation within the sampling window using the median absolute deviation. Elements deviating from the median by more than six standard deviations were identified and replaced with 'NaN'. The tuning of the filter parameters involved finding a balance between overly agressive and insufficient removal of outliers. Residual outliers were subsequently manually removed through meticulous qualitative inspection. The complexity and subjectivity of this operation provide users with the opportunity to explore alternative approaches. Consequently, the published dataset includes two versions: an initial version (v1) comprising the original raw data with no filtering applied, and a second "cleaned" version (v2).

The presented database can be further used by researchers to validate and calibrate experimental and numerical simulations, as well as analytical models, of downburst winds. It will also be an important resource for the scientific community working in the wind engineering field, in meteorology and atmospheric sciences, as well as in the risk management and reductions of losses related to thunderstorm events (i.e., insurance companies).
description: The dataset here presented was built with the support of the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 741273) for the project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures—awarded with an Advanced Grant 2016. The data collected in the database were recorded by the wind monitoring network set up for the European Projects "Wind and Ports" (grant no. B87E09000000007) and "Wind, Ports and Sea" (grant no. B82F13000100005), funded by the European Territorial Cooperation Objective, Cross-border program Italy-France Maritime 2007–2013. The International Center for Environmental Monitoring (CIMA Research Foundation) is gratefully acknowledged for providing the meteorological data necessary to confirm the nature of thunderstorm events included in the dataset. This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).