Before the introduction of instrumental and radar measurements, textual sources were often the only records of mesoscale weather events such as thunderstorm and hail events . They describe the course, intensity and extent of an event, as well as the damage it causes e.g. and remain the most important basis for investigating the variability and risk management of extreme weather events .

The use of written weather observations is limited not so much by the availability of sources as by the effort involved in processing them: each event must be manually identified, contextualised and classified. Consequently, only parts of large corpora have been analysed to date. Transformer-based language models such as BERT are potentially capable of carrying out high-quality and scalable automated analysis of historical texts on the topics of climate, weather and risks see, e.g.,.

Training such models requires data that has been validated from a linguistic perspective and checked for climatological plausibility. Historical climate records are written in the language of their era and differ fundamentally from modern weather observations . They consist of descriptions whose level of detail, temporal and spatial precision, and meteorological interpretation vary considerably . If these characteristics, as well as the physical plausibility of the manual classifications, are not taken into account when generating the model, the results cannot be interpreted. Consistent and validated data, in terms of both their linguistic and climate plausibility, are therefore a prerequisite for automation using Transformer-based language models.

The analysis of thunderstorm and hail events based on historical textual sources is particularly under-represented. In existing studies, historical events are classified according to frequency, intensity or damage see, none of them provides a formally defined, reproducible framework that links the linguistic variability of historical descriptions with meteorological interpretation.

To bridge this gap, we are developing a text-based classification method that enables climate-relevant information on thunderstorm and hail events to be systematically recorded and categorised. To this end, a classification scheme based on associated phenomena is defined ex ante and applied to a corpus of 6999 written observations from Central European sources dating from 1000 to 1817. The data are checked at the text level for consistency and quality and checked for physical plausibility against the DWD normal periods (1961–1990 and 1991–2020).

The dataset prepared in this way is then used to train Transformer-based language models. These models can analyse further text sources on the basis of the classifications made. All models are made publicly available via Hugging Face.

This study draws on corpora that have already been normalised and temporally and geographically referenced, and focuses on their classification. Below, we provide an overview of the data and the process by which it was generated.

The dataset comprises text extracts (quotes) relating to thunderstorm and hail events in Central Europe between 1000 and 1817 . It is based mainly on the HISKLID2 dataset , which has been compiled since the 1980s on the virtual research platform tambora.org (https://www.tambora.org, last access: 30 September 2026), primarily for the reconstruction of temperature and precipitation time series.

Quotes referring to thunderstorm and hail events were extracted from this collection. The dataset used (version 2.2) goes beyond the original HISKLID2 corpus and additionally includes linguistically normalised texts, standardised time data and spatial georeferencing. The complete dataset is publicly available on GitLab (https://gitlab.com/reservoirdog/hist_thunderstorm_hail_central_europe, last access: 30 September 2026).

In total, the dataset contains 6999 quotes from 494 sources, documenting 6157 thunderstorm events and 2006 hail events. 99.1 % of the quotes come from HISKLID2 (483 out of 494 sources), with the remainder from eleven additional sources, primarily 18th-century newspapers and a diary. The corpus thus represents a sample drawn from a general climate database and not a collection specifically compiled for extreme events. Of the 51 833 data records in HISKLID2, 44 834 (86.5 %) mention neither thunderstorm nor hail events, meaning that a selective choice favouring extreme events is structurally ruled out at the corpus level.

The bulk of the corpus consists of chronicles, historiographies, annals and administrative records, followed by weather records, almanacs and compilations (see Fig. a). In their role as observers of local environmental conditions, the authors show parallels with the early instrumental observers of the 18th and early 19th centuries (see Fig. b). This continuity points to comparable social conditions underpinning observational activity, although the nature of data collection differs fundamentally.

The quotes span four language stages: Middle High German, Early New High German, New High German and Contemporary German. Latin quotes were translated into Contemporary German prior to normalisation. When standardising the German-language quotes, grammatical modernisation was avoided; instead, lexical and orthographic normalisation was carried out. Idiomatic expressions were left in their original form in order to preserve semantic authenticity . Obsolete or dialect-specific vocabulary was resolved using the Wörterbuchnetz (https://woerterbuchnetz.de, last access: 30 September 2026). This normalisation is a prerequisite for a consistent lexical basis and thus for the application of modern language models .

Table  provides a quantitative overview of the distribution of documented events by century and language stage. The data illustrate the linguistic heterogeneity of the corpus, as well as the transition from Latin and Middle High German sources of the Middle Ages to Early and New High German sources from the 16th century onwards. The total number of events in the corpus increases in parallel with the number of textual sources. According to , the transmission of textual sources was shaped by the invention of printing (1445), the Reformation (1517) and Humboldt's educational reform (1810). A comparison of the corpus with these historical events can be found in Fig. .

The dates given in the textual sources, including those based on religious or regional calendars, were deciphered using Grotefend . The place names were georeferenced using GeoNames (https://geonames.org, last access: 30 September 2026) and historical place name directories e.g.. The spatial distribution of the events is concentrated in German-speaking countries, although the location from which a source originates does not necessarily correspond to the reported observation sites. In particular, newspapers report on events in several regions, often across territorial and linguistic boundaries. As shown in Fig. , the observations cover the climatically most relevant zones of Central Europe according to the Köppen–Geiger classification . The dataset thus spans several climatic regimes, including the temperate oceanic climate (Cfb), the warm-summer continental climate (Dfb) and the Mediterranean-influenced climate (Csa, Csb), as well as their maritime, continental and orographic influences.

The classification method described in this study, including its validation, forms part of a comprehensive workflow for source evaluation (see Fig. ). Each level consists of formally defined steps, which are documented in detail in .

The normalised quotes, together with the attributes “time” (T) and “location” (L), form the basis for the classification process, which is followed by a plausibility check. The quotes and the classified thunderstorm and hail events (EI) make up the training set.

The underlying conceptual and formal model of the classification is set out in more detail below. It is based on the approach outlined in , which describes weather and climate events using a quadruplet comprising event type (ET), time (T), location (L) and event intensity (EI). In this study, EI corresponds to the classification of thunderstorm and hail events. Formally, EI is the result of a mapping fEI that maps a quote q∈Q to a class c∈C from a finite set of uniquely defined event intensity classes C: 1fEI:Q→C,EI=fEI(q) Classification is carried out in several stages: linguistic indications of associated phenomena are systematically recorded and analysed for their causal relationships. On this basis, the events are categorised into intensity classes. Finally, the results are linguistically validated and their physical plausibility is statistically assessed using observational data from the German Weather Service (DWD).

The study shows that, despite widely varying source density and a heterogeneous source base, historical reports of thunderstorm and hail events exhibit a remarkably stable seasonal signal. The reconstructed annual pattern is dominated by a pronounced summer maximum. The maximum identified closely matches the observational data from the DWD normal periods in terms of location, shape and amplitude. This pattern remains consistent for both the entire period and the period with the highest density of observations (1624–1654).

The high Spearman rank correlations and small variations in the normalised monthly proportions indicate that the historical observations examined in this study are consistently related to real meteorological processes at the aggregated level. Furthermore, the seasonal pattern is largely independent of observation density: significant fluctuations in the number of quotes only minimally alter the shape of the annual pattern.

A comparison with independent studies further supports these findings. The seasonal cycle shows a clear correspondence with the independent reconstructions by and . This agreement is evident both for the entire time series and for the period with the highest observation density, 1624–1654 (Fig. ). The correlation analysis supports this agreement (Hist vs. Lenke: Spearman 0.86; Hist vs. Camuffo: 0.71), with almost identical results yielded for the most densely observed period (1624–1654). This points to a robust seasonal signature of thunderstorm activity across different time periods, regions and source types, and highlights the plausibility of the reconstructed annual pattern.

Differences are evident for the winter: the ratio of summer to winter components (JJA vs. DJF) in the reconstructed time series of this study is approximately 3.0–3.5, whilst the values according to (6.6) and in (13.2) are more pronounced. One possible explanation for the increased observation of thunderstorm events in winter could be source-specific perceptions of winter thunderstorm and hail events, which were regarded as unusual e.g.. Whether this is attributable to the composition of the corpus cannot be conclusively determined here.

The ratio between summer and winter (JJA / DJF) remains stable above the unit line throughout the entire observation period: over more than four centuries, there are no systematic trends that would suggest changes in source density or documentation practices. The consistently pronounced summer maximum suggests that the observed seasonal pattern is primarily attributable to actual convective processes and is not due to effects of transmission or the selection of quotes.

Discernible, physically consistent seasonality is a crucial criterion for the plausibility of historical observations. The seasonality observed here shows that the reconstructed time series of thunderstorms and hail contain climatologically meaningful signals at an aggregated level. However, agreement with modern and historical reference series does not confirm the classification of individual events. Whether a particular report has been assigned to the correct intensity class can only be determined by examining linguistic evidence. The individual signals from the thunderstorm and hail classes therefore provide a more nuanced picture.

First, we consider the linguistic evidence. This shows that the classified hail events are of a consistently high quality across all periods. The majority of hail events classified as H2 have a confidence index of ≥2. The distinction between classes H1 and H2 can be clearly made on the basis of hailstone size or the intensity of the hail, which leaves little room for interpretation. Hail events with an index level of 0 are found only in class H1. In such quotes, the term “hail” stands alone and leaves no room for interpretation.

The quality of the classification of thunderstorm events differs from that of hail events: whilst the TS1 and TS3 classes exhibit a stable composition of evidence classes, TS2 shows significantly greater variability and thus greater uncertainties in interpretation. Individual TS1 events with evidence class C3 are difficult to classify due to a lack of descriptions, but do not significantly affect the overall signal. Furthermore, for a small proportion of thunderstorm events (2.9 %), the causal link between the event and the reported damage is absent, meaning that the damage does not contribute to validating the classification. Overall, it is clear that the classification of thunderstorm events is more challenging, particularly in the middle class TS2. The classification is therefore asymmetric: the high-intensity classes (TS3 and H2) are based on direct, verifiable evidence, whereas the threshold-free middle class (TS2) remains transitional with weaker evidential support. As with hail events, the composition of the confidence index remains stable across the entire period and across all classes.

The analysis of the ordering relation shows a clear dominance of thunderstorm class TS1 throughout the seasonal cycle. Class TS2 occurs at a lower but stable proportion throughout much of the year, while class TS3 achieves a higher proportion, particularly during the summer months. During the transitional seasons, the ordering relation is predominantly TS1 > TS2 > TS3. However, in the summer months, the secondary ranking repeatedly shifts to TS1 > TS3 > TS2 in the medium-range class proportions. An exception is February, during the dense observation period 1624–1654, when TS2 dominates at 51 %. However, given the small number of winter events and the correspondingly wide confidence intervals, this single outlier is not statistically significant.

These findings contradict the assumption frequently put forward in the literature that historical sources primarily record extreme weather events cf.. However, such a general dominance of extreme classes cannot be confirmed for thunderstorm and hail events. Under this assumption, an ordering relation of TS3 > TS2 > TS1 would have been expected. In fact, the observations in this study predominantly follow the physical frequency distribution, with a clear dominance of weaker thunderstorm events (TS1). This suggests that, at least for thunderstorm events, there is no systematic over-representation of extreme intensities.

The differing seasonal cycles of hail classes H1 and H2 can be explained by a combination of physical and source-related effects. During the summer months, high convective energy means that hail events tend to be more intense when they occur, whilst light hail either occurs less frequently meteorologically or is documented less often due to its lower visibility. In winter and transitional seasons, by contrast, hail events predominantly occur in weaker forms, but are recorded even at low intensities due to the generally lower event density and increased sensitivity to observation. The transitional months of April and September consistently mark the change of season. This is consistent with the fact that the seasonal increase in hail events begins earlier than that for thunderstorm events, with higher proportions already evident in March and April. Noteworthy is the high correlation between the H1 data and the DWD observational data (ρ(H1, DWD) = 0.87–0.92), which generally only record light hail, whilst H2 shows a typical peak in summer. This underlines both the coherence of the class assignment at seasonal scale and the plausibility of real-world observations.

The relationship between source type and intensity class (Sect. ) is significant for both thunderstorm and hail events, although to varying degrees: for thunderstorm events, the effect is small (explained variance approximately 4 %), whereas for hail events it is moderate (approximately 18 %). The reporting patterns of the source types influence the classification. However, they are not strong enough to be interpreted as a primary effect. In particular, narrative sources such as chronicles tend to document more intense hail events (H2) disproportionately, whilst weather records more frequently record weaker hail events (H1). In documentary sources, hail events appear to be considered newsworthy primarily when they cause damage or take on unusual forms. Continuous observation formats, by contrast, systematically record even lower intensities. However, the stability of the confidence index throughout the entire observation period shows that the description of thunderstorm and hail events hardly differs in terms of type and composition. This suggests that only a synoptic analysis across all sources leads to plausible results.

The results of the transformer-based language model can be contextualised within the previously obtained findings on the seasonal structure of convective events. The high data quality of the hail events and the uncertainties in thunderstorm classification influence the quality of the model. Whilst the automatic classification of hail events and thunderstorm classes TS1 and TS3 is accurate, slightly lower F1 scores are achieved for thunderstorm class TS2. The results of the model training thus reflect the results of the confidence index.

However, the results also demonstrate that the patterns observed in historical sources can be consistently reproduced at the semantic-linguistic level. Despite being trained exclusively on texts and not including any additional meteorological variables, the model reproduces the characteristic ordering relation (e.g. TS1 > TS2 > TS3 during the seasonal cycle) and the summer intensification of more severe events. These results are consistent with the corpus being coherent in terms of content: the historical texts contain sufficient meteorologically relevant information to capture both seasonal differences and differences in event severity. Since the model was trained on the same quotes, this does not constitute an independent test of the classification.

The remaining misclassifications occur almost exclusively at the boundaries between neighbouring classes, a pattern well-recognised in both meteorological practice and source-critical analysis. Our study aligns with these observations, although serious errors remain rare. Consequently, the results indicate that the model does not simply memorise formulations (overfitting), but rather identifies recurring semantic patterns. Such behaviour is consistent with a clear separation of the relevant linguistic signals within the classification scheme.

Both models produce stable and reproducible results and are suitable for the automated classification of historical weather descriptions from this corpus. In doing so, they learn the implicit structures of the historical record, including source-specific selection mechanisms, rather than smoothing them out, and thus operate in a manner consistent with the characteristics of the corpus. The observed differences in performance between thunderstorm and hail classification can be explained primarily by the different class structures and semantic discriminative power of the respective target phenomena.

Despite their varying source types and linguistic heterogeneity, the study shows that historical textual sources offer great potential for the quantitative reconstruction of convective weather events. A consistent database was created by combining source-critical analysis, rule-based classification, and linguistic validation and physical-statistical plausibility assessment. Converting qualitative descriptions into structured evidence classes and developing a confidence index made it possible to assess the data quality. Based on linguistic evidence, the classification is highly reliable for hail events and for light and severe thunderstorm events. However, there are still uncertainties regarding the moderate thunderstorm class and winter thunderstorm events, which appear to be over-represented in the sources. The qualitative composition has remained remarkably stable for over 800 years. This demonstrates that historical observations of thunderstorm and hail events exhibit minimal linguistic variation, allowing typical patterns to be identified in the descriptions. This makes automated classification all the more relevant, as demonstrated by the successful use of a multilingual BERT model for detecting and classifying thunderstorm and hail events. Both models can classify unseen quotes from the corpus.

Overall, this study shows that Transformer-based language models can extract climatologically plausible information from systematically processed historical data. The formally defined classification procedure with an evidence-based quality assessment provides a reproducible framework that explicitly considers both the variability in the language used to describe historical events and the meteorological interpretation required for climatological analysis. This extends existing approaches in a useful way.

The high quality of the observations can be explained by the significant social role of thunderstorm and hail events in pre-industrial agrarian societies, among other things. Such events posed an existential threat in these societies and were therefore documented in great detail. This dataset provides new insights into the study of convective weather in the context of climate change. As instrumental and radar-based observation series only span a few decades, long-term reference data from the pre-industrial climate regime is necessary to contextualise the natural variability of thunderstorm and hail activity. The period covered here, which extends back to the year 1000, provides such a basis. However, a key question remains: how does the internal variability of convective activity in the natural climate regime compare with today's anthropogenically influenced conditions? This dataset and the classification tools it provides form a basis for answering this question, which can be further developed through expanded spatial and temporal coverage in future work. The empirical results apply to the German-speaking source corpus of Central Europe. The classification method itself is designed to be language-independent. However, it must be recreated using the same corpus-based procedure for each new target language. The next step is therefore to apply the method to comparable corpora in other languages, such as French or English.