Meteorological phenomena associated with strong surface wind of convective origin (i.e. tornadoes, downbursts, straight-line winds) can cause important disruption to socio-economic activity, including injuries or even fatalities, despite their local character compared to larger-scale mid-latitude synoptic windstorms or tropical storms. For example, from 1950 to 2015, tornadoes in Europe caused 4462 injuries, 316 fatalities and economic losses of at least EUR 1 billion (). Due to their economic and social impact, a large number of works have been devoted to the study of these phenomena both from a meteorological point of view (e.g. ; ; ) and from the point of view of their consequences (e.g. ; ).

The systematic elaboration of post-event forensic field surveys is still the standard way to evaluate the damage caused by a strong-convective-wind event (; ; ), despite the recent progress on assessing wind damage using remote-sensing data such as high-resolution radar observations (; ). A detailed damage analysis from these meteorological phenomena allows us to estimate the wind intensity using a wind damage scale such as the Fujita scale (F scale; ) or the Enhanced Fujita scale (EF scale; ). Similarly to field surveys of hailstorms () or floods (; ), wind damage field studies contribute to a better characterization of the affected area, making it possible to estimate the length and width of the damage swath (e.g. ; ; ). Moreover, in situ damage surveys are especially useful to determine which phenomenon took place when there is an absence of observations by analysing damage patterns on forest and how debris is spread (; ; ; ; ). This information can be added to natural hazard databases such as the US Storm Prediction Center Severe Weather Database () or the European Severe Weather Database (), making it possible to build up robust and homogeneous datasets, improving the knowledge of spatial–temporal distribution and characteristics of tornadoes, downbursts and straight-line winds.

Currently, field studies are usually performed to assess damage of specific strong-convective-wind events (e.g. ; ; ) but rarely to analyse in detail most of the reported cases. The timing and economical costs, especially when helicopter flights are used, prevent carrying out in situ damage analysis frequently (), particularly outside of the USA. Therefore, there is a need for a methodology to conduct wind damage field surveys for high-impact weather events of convective origin that is easily reproducible anywhere and should be efficient to optimize time and economic resources, allowing the study of as many reported events as possible.

The objective of this paper is to propose a methodology to conduct in situ damage surveys of strong wind events from convective origin. It can contribute to improve the detection, mapping and characterization of wind damage in a homogeneous way, which is important to better describe specific meteorological phenomena, with the particularities associated with damage from convective local storms. Therefore, the main goal of the proposed methodology is to gather as much geo-referenced information (pictures and records) as possible about relevant damaged elements (i.e. human-made structures and vegetation) to reproduce the damage scenario. This information should be complemented with other available data, such as witness enquiries, data from automatic weather stations (AWSs) located close to the affected area, remote-sensing data and images of the phenomenon together with their location and orientation to analyse strong-convective-wind phenomena from a meteorological point of view.

The methodology presented here is based on previous studies (; ; ; ) and also on 136 wind damage surveys performed between 2004 and 2018 by the authors. All the analysed events have been recorded in Spain (south-western Europe), which includes the vast majority of the Iberian Peninsula and also the Balearic and Canary islands. Nevertheless, most of these field studies have been carried out in the Catalonia and Andalusia regions (Fig. ), where highly densely populated areas are frequently affected by tornadoes (; ; ; ; ). Three final deliverables are suggested to synthesize the data recorded: (i) a text report of the analysed event, (ii) a table consisting of detailed geolocated information, and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis.

The rest of the article is organized as follows. Firstly, in Sect. 2 an overview of previous in situ fieldwork techniques is provided. Section 3 describes, in detail, the field survey methodology proposed. In Sect. 4 specific strengths and limitations of the methodology are discussed, as well as possible uses of fieldwork data. Finally, Sect. 5 presents a summary and final conclusions of the study. In the Supplement, an example of deliverables (text report, table and KML file) of a damage survey of a recent tornadic event is provided with the aim to better illustrate the methodology proposed and to facilitate its application.

and provided guidelines to carry out strong-convective-wind damage surveys. There, the process of mapping data by locating images taken during the fieldwork was challenging due to non-digital cameras and the absence of Global Navigation Satellite System on these devices. Both documents recommended complementing surface observations with aerial images if available, and, in the second one, it was also explained how to treat direct witnesses and ask them for specific information about the event and damage.

On the other hand, the analysis of historical events such as those of and showed the utility of press references and in situ images taken by witnesses on reconstructing tornado damage paths. They pointed out the necessity of geo-referencing the locations where photos were taken and the damaged elements, using GIS tools and triangulation methods. Furthermore, provided useful indications for current field studies, such as visiting affected areas as soon as possible and also providing an estimation of the wind intensity for each damaged element given by the pair damage indicator–degree of damage (DI–DoD) from the EF scale (), similarly to other authors such as .

During the first decade of the current century, the use of GPS receivers or similar systems was extended to in situ damage assessments to geolocate gathered data, as discussed in . Moreover, aerial imagery from helicopters or aeroplanes (e.g. ; ) and high-resolution satellites (e.g. ; ) has also been frequently used to analyse damage swaths. Recently, drones have been raised as a new device which might be useful to, at least, complement surface surveys ().

The methodology to carry out damage surveys must be efficient, allowing us to visit the affected area in the shortest time possible. It must also be easily reproducible, and its results should be accurate. Geolocating damage using pictures or videos recorded with smartphones or cameras with a Global Navigation Satellite System such as GPS fulfils these conditions (). Nevertheless, as it happens with other types of damage assessments, there are inherent uncertainties that should be taken into account when analysing field data (), like possible GPS location errors or ambiguous application of intensity rating assessments due to EF scale limitations, which are discussed on Sect. 4.

In Table  the main devices needed to carry out field studies throughout the proposed methodology are summarized. Moreover, as indicated in and , water, food, comfortable footwear, a rain jacket, spare clothes and a mobile phone spare battery are recommended, because affected areas may be far away from inhabited locations. As surveyor displacements longer than a few kilometres can be required, a well-equipped, preferably all terrain, car is necessary to save time between points of damage. Nevertheless, difficult-access areas may be found along the track, because of muddy roads and fallen trees or simply because of the absence of roads. Especially in these cases, and also to study damaged areas in detail, walking is the basic way to perform the field survey.

Despite this not always being feasible, it would be ideal that the damage survey team was multidisciplinary, being formed by meteorologists, insurance inspectors, forestry engineers and architects experienced in damage assessments, preferably familiar with damage reporting systems such as the EF scale. This would facilitate an accurate and detailed analysis of the damage and the phenomenon intensity.

The proposed methodology is organized in three stages (Fig. ). The first step includes pre-in situ damage survey tasks, preparing the actual visit of the damaged area (Sect. 3.1). Secondly, the in situ fieldwork tasks, which include direct gathering of human-made structure and vegetation damage information, and also collection of direct witness experiences (Sect. 3.2). Finally, post-in situ damage assessment tasks are performed, which involve the organization of all the information collected into three deliverables (a text report of the event, a geolocated information table and a data location map; Sect. 3.3).

The proposed methodology is formulated in a convenient, feasible and detailed way so it can be readily used, but its practical application may present some weaknesses. Among the advantages of the proposed methodology is the relative simplicity of the devices required (Table ), which are neither unusual nor expensive tools, so meteorological services, public research institutions and private entities may perform systematic damage surveys of reported events, analysing even suspicious developed funnel clouds for which it is previously unknown if they reached the ground. Moreover, the easy-to-reproduce fieldwork process and the generation of the proposed three final deliverables support the main objectives of the in situ damage assessment, which are identifying the phenomenon type, estimating wind intensity, and characterizing the event and the damage swath. Besides, the methodology is also intended to optimize time during in situ measurements in order to make possible visiting the whole affected area as soon as possible to avoid the alteration of the damage scenario by clearing services, as stated in Sect. 3.1.

Surface in situ analysis provides more detailed information than aerial surveys or analysis based only on remote-sensing data. For instance, minor damage to vegetation and to human-made structures is more easily detected (e.g. ). In addition, it is possible to study in detail the soil state in forest areas and the degraded state or previous weaknesses of damaged elements, which are essential to assess the wind intensity, and measuring data of interest such as snapped trunk diameter or small windborne debris weight. On the other hand, tornado outbreaks and widespread events (such as derechos – e.g. ; ) may be cases where it is challenging to apply the methodology. Nevertheless, in Sect. 3.2 some methods to mitigate these problems have been provided, such as making a discontinuous analysis in forest areas, studying transversal stripes along the damage track every 250 to 500 m if possible. Another option would be to distribute areas to be analysed among the members of the surveyor team to carry out several field studies in parallel. Especially in those cases, and also in complex-terrain events, aerial imagery could be useful to complement surface data, providing an overview of the damaged area and information from difficult-access zones.

On the other hand, the geolocation of damaged elements and data of interest has a strong dependence on GPS signal reception. Geolocation accuracy depends on a number of factors including local terrain geometry, quality of the receiver antenna system or number of satellites observed. Photo cameras and smartphones have location errors usually ranging from 5 to 20 m, typically being the greatest in deep valleys, or close to large buildings or structures blocking satellite signals. To minimize geolocation errors, it is recommended to check the accuracy with manually selected reference locations and, if necessary, to correct damage locations on the summary map and on the geolocated information table. This is feasible in urban or peri-urban areas, where buildings or other elements are easily identifiable using high-resolution aerial images such as orthophotos, but not in forests or other natural areas without evident references where this verification may not be possible.

The estimation of wind intensity of convective origin is based on damage rating scales, which relate the damage observed with the wind speed. Despite the proposed methodology being illustrated using the EF scale, it should be noted that other intensity scales could be used such as the TORRO scale (). The practical application of the EF scale has some limitations (), in spite of the progress made some years ago by introducing a more detailed intensity rating scale () compared to the original and simpler Fujita scale (; ). The Enhanced Fujita scale, developed in the USA, is mainly based on the damage caused by wind to standard US buildings and elements (schools, hospitals, automobile showrooms, etc.), so-called damage indicators (DIs). When applied to areas outside the USA many DIs may not exist, hampering its application as discussed in detail in and . Moreover, there are elements which are susceptible to damage such as traffic signals, walls and fences, trash bins, and vehicles, which are not included on the EF scale.

The data gathered can have several uses, apart from contributing to build-up of homogeneous severe weather databases, which at the same time enhance the knowledge about tornado, downburst and straight-line wind occurrence. Insurance and reinsurance companies can be one of the major benefitted sectors from results of field studies, which usually need to know the area affected by a strong-convective-wind event and its intensity to cover compensations and, in some specific cases as in Spain, the phenomenon type ().

Data collected from damage to buildings (general photos and detailed pictures of deficiencies or previous weaknesses) may also contribute to study exposure and vulnerability of constructions in an area of interest and also to assess the failure modes (e.g. north-eastern Italy; ; ), as pointed out in . Moreover, the identification of typical damaged buildings using the information provided in the set of final deliverables can give a guideline for adapting intensity rating scales, such as the EF scale, outside of the USA, partially solving those deficiencies in assessing wind intensity. In this line of work, some authors have propounded new damage indicators to append to the above-mentioned scale using information derived from tens of field studies (); to adapt them to typical human-made structures from other countries, as recently reported in Canada () or Japan (); or even to develop a standardized international Fujita scale, as proposed in . Furthermore, in some articles it has been discussed how to assess wind intensity throughout effects on vehicles, with data given by field studies (), similarly to here. As provided in the geolocated information map, where each point of damage with its DI–DoD pair is given, it is possible to assess the degree of damage severity along the damage swath of a tornadic event. This information can be very valuable to analyse the impact of tornadoes in future projected scenarios, for example modelling damaged areas in tornado paths using the data provided by field studies and assessing the possible consequences under different expected urban conditions (; ).

In situ damage survey data are used to study the consequences of natural hazards, such as floods or damaging strong convective winds. The latter can be specifically characterized carrying out field studies, estimating the damage path length and width and also the intensity of the event. Moreover, through an analysis of the data gathered it might be possible to clarify which phenomenon caused the damage (tornado, downburst or straight-line winds) in case neither images nor direct witness reports exist.

The purpose of this article is to provide an easily reproducible methodology to carry out surface strong-convective-wind event damage surveys, which optimize time and economic resources. It is mainly based on collecting geolocated information about damaged human-made structures and vegetation, with the final aim of representing the damage scenario to study the event from a meteorological point of view. Complementary data from AWSs close to the affected area and witness reports should also be gathered if available, and remote-sensing data should be used to get a deeper understanding of the convective storm event. With all this information, three final deliverables are generated (a standardized text report of the event, a table consisting of detailed geolocated information, and a map or a file in KML format).

This methodology is based on previous studies and has been refined during the elaboration of 136 strong-convective-wind damage surveys carried out in Spain between 2004 and 2018. Known limitations of its application include geolocation errors of damage, applicability of the EF scale outside the USA and difficulties in analysing extensive events or complex topography areas. Nevertheless, surface-based detailed data provided, such as previously degraded state of damaged elements, minor damage to human-made structures and vegetation, snapped tree trunk diameter, and soil state in forest areas, might be helpful to better analyse event consequences compared to other methodologies. In any case, the field survey data obtained are valuable for further analysis, complementing detailed meteorological case studies based on operational remote sensing such as Doppler weather radar data, surface observations and numerical weather prediction model output. Moreover, the methodology proposed may contribute to standardize detailed field surveys, which are essential to build up and maintain robust and homogeneous databases of severe weather phenomena.