From language to meteorology: kinesis in weather events and weather verbs across Sinitic languages

The paper addresses whether kinesis can account for how meteorological events shape linguistic encoding, particularly verb selection and its variation across Sinitic languages. The context is the difficulty of obtaining long-term, large-scale environmental datasets and the complementary value of traditional ecological knowledge and linguistic evidence. Prior debates on linguistic relativism and weather terminology have not linked linguistic systems to meteorological knowledge. Sinitic languages provide an ideal testbed due to extensive typological documentation, high internal diversity with 1000+ varieties, and 3000 years of written records spanning diverse climates. The authors hypothesize that perceptual correlates of kinetic energy—mass of weather substances and speed of processes—form the cognitive basis of verb transitivity and noun–verb encoding in weather expressions. They pose the research question: Can kinesis explain choices and variations in encoding different meteorological events?

The study builds on cross-linguistic typologies of weather expressions (Eriksen et al., 2010, 2012), which distinguish formal construction types and semantic event types but do not incorporate meteorological knowledge. Work on Sinitic weather expressions (e.g., Dong 2018, 2019; Dong et al., 2020a–c; Van Hoey, 2018) reveals diverse verb types (unaccusative, unergative, transitive) and motivates the Typology of Meteorological Events (TyME), classifying events by [±Process] and [Material]. The paper also engages theories of the noun–verb dichotomy and eventivity (Givón, 1984; Huang, 2015, 2016) and adopts Hopper and Thompson’s kinesis, visibility, and effectiveness as prototypical verb properties. It proposes integrating linguistic kinesis with physical kinetics, mapping perceived mass and speed to verb transitivity in weather expressions.

The authors adopt a structured-context approach inspired by Dilthey: triangulating empirical linguistic data, experimental evidence, and meteorological distributions while minimizing confounds from historical inheritance/contact (Galton’s problem). Three components:

• Ontology-lexicon interface: mapping between traditional linguistic knowledge and formal scientific (meteorological) concepts to test implications of kinesis (mass, speed) for linguistic choices.
• Dialectal dataset: Weather expressions from 221 Sinitic languages/dialects compiled from major dialect dictionaries (Li, 1993–2003; Tao, 2007; Xu & Miyata, 1999; Zhang & Mo, 2009) for nine phenomena (rain, snow, hail, fog, dew, wind, frost, thunder, lightning). For 216 varieties with generic weather expressions, verbs were classified by transitivity (high, low, both) irrespective of word order. High-transitivity verbs include 打 dǎ ‘hit’, 拍 pāi ‘slap’, 扯 chě ‘pull’; low-transitivity verbs include 下 xià ‘fall’, 落 luò ‘fall/drop’, 起 qǐ ‘rise/begin’.
• Perception experiment: Online study testing perceived mass and speed for four verbs common in weather expressions (打 dǎ ‘hit’, 下 xià ‘fall’, 上 shàng ‘rise’, 起 qǐ ‘rise’) under two conditions: Direct (verb alone) and Context (pseudo-weather events). Participants rated Mass and Speed on 4-point scales. Quality controls excluded responses based on time outliers, invariant scale use, and non-native status. Ordinal logistic regressions (MASS in R) modeled Mass or Speed as dependent variables; Verb, Condition as predictors; Language background (north vs south) and Age as covariates. 367 valid responses remained from 564. Likelihood ratio tests assessed significance.
• Geographical correlation: Mapped exceptional regional verb distributions against meteorological data to identify functional (kinesis-driven) patterns beyond typological north–south divides. Cases examined include rain verb choice relative to rainfall isohyets, frost expressions vs frost damage zones, and hail noun morphologies vs hail damage areas.

• Distribution of verb transitivity across phenomena (percent of languages/dialects using high/low/both transitivity verbs): • Rain: High 0%, Low 100%, Both 0%. • Snow: High 0%, Low 100%, Both 0%. • Hail: High 0%, Low 97.0%, Both 3.0%. • Fog: High 7.9%, Low 92.1%, Both 0%. • Dew: High 24.2%, Low 68.2%, Both 7.6%. • Wind: High 23.3%, Low 46.5%, Both 30.2%. • Frost: High 43.9%, Low 51.0%, Both 5.1%. • Thunder: High 52.2%, Low 36.4%, Both 11.4%. • Lightning: High 86.3%, Low 12.7%, Both 1.0%.
• General rule: Weather events involving larger substance mass and/or faster processes preferentially select high-transitivity action verbs (e.g., 打 dǎ). Fog droplets (~0.01–0.02 mm) are smallest and mostly suspended; dew droplets are larger (initial ~0.035 mm to ~0.2 mm at sunrise), and frost includes ice crystals—mirroring rising high-transitivity usage from fog (7.9%) to dew (24.2%) to frost (56.1% incl. Both).
• Lightning overwhelmingly uses high-transitivity action verbs (87.3%), reflecting perceived very high speed; thunder also often uses high-transitivity verbs (52.2%), linked conceptually with lightning despite being sound.
• Precipitation (rain/snow/hail) almost uniformly takes low-transitivity fall verbs; but high-transitivity verbs emerge for heavy/intense rain in many varieties (e.g., 武汉 Wuhan 跑 ‘run’, 打 ‘hit’; 宁波 压 ‘press’; 海口 做 ‘do’). Wind shows a shift to high-transitivity verbs for strong wind; 15 varieties with dedicated ‘strong wind’ items all use high-transitivity verbs.
• Experiment: Significant effects for Mass and Speed ratings across verbs and conditions (p < 0.001). Stable hierarchy in perceived Mass/Speed: 打 dǎ > 下 xià > 上 shàng > 起 qǐ, constant across Direct and Context conditions. No Language (north/south) or Age effects, indicating shared conceptualization among Sinitic speakers.
• Geographical correlations: • Rain verbs: North of Yangtze typically uses 下 xià; south uses 落 luò. Exceptionally, Sichuan/Chongqing (north of Yangtze, Mandarin areas) use 落 luò, aligning with areas exceeding ~1000 mm annual rainfall; the even heavier rainfall zone (>1500–2000 mm) shows 落水 ‘fall-water’ instead of 落雨 ‘fall-rain’, suggesting mass/intensity-driven lexicalization. • Frost: High-transitivity 打 dǎ ‘hit’ for frost spans typologically mixed regions that overlap areas of frost damage to crops, consistent with higher perceived impact/KE. • Hail nouns: Innovations using 蛋 ‘egg’ and 冷 ‘cold’ cluster in hail damage regions/centres/zones; ‘egg’ reflects large hailstones; replacement of archaic 雹 by colloquial forms correlates with frequent severe hail experience.
• Diachrony and category shift: In Archaic Chinese, precipitation and lightning/thunder often functioned as verbs; fog/dew/frost rarely did, consistent with mass/speed. Modern Chinese shifted to more nominal or light-verb constructions (e.g., 下雨), reflecting broader lexical changes. Smog 霾: archaically denoted dynamic dust storm (verbal, high kinesis); modern 霾 denotes suspended fine particles (PM2.5/PM10), now favoring existential verbs (有/出现), consistent with very low kinesis.

Findings support the central hypothesis: perceived kinesis (mass and speed, as components of kinetic energy) predicts verb selection and grammatical encoding of weather events in Sinitic languages. Precipitation’s salient, consistent downward motion biases low-transitivity fall verbs despite large mass, whereas phenomena with inconsistent motion or rapid change (lightning, strong wind) favor high-transitivity action verbs. The perception experiment demonstrates shared mental representations of verb-associated mass/speed, aligning with linguistic distributions. Geographical analyses reveal that deviations from typological expectations track meteorological intensities (rainfall, frost and hail damage), indicating functional (kinesis-driven) pressures on lexical choice. The kinetic-energy perspective also illuminates ‘verbhood’: events with higher perceived kinesis are more likely to be encoded verbally (e.g., archaic 雷 ‘to thunder’, 电 ‘lightning flashes’), while low-kinesis events (fog, dew, frost) tend to be nominal. Diachronic shifts (e.g., smog’s semantic change and modern Chinese light-verb constructions) can be explained by changes in perceived kinesis and broader lexicalization trends rather than by inheritance alone.

The study proposes and substantiates a kinesis-based account linking meteorological event properties (mass, speed) to linguistic encoding choices in Sinitic languages. Across dialectal distributions, experimental perception, and geographical correlations, higher perceived kinetic energy corresponds to the use of high-transitivity action verbs and to verbal categorization, while lower kinesis aligns with low-transitivity or nominal encoding. This framework bridges linguistic typology and meteorology, offering an interpretive, cognition-grounded model that explains both broad patterns and regional exceptions. Future research should extend to micro-scale phenomena and additional weather types (e.g., freezing rain), and explore cross-linguistic generality beyond Sinitic with richer quantitative meteorological measures.

• Causality cannot be established experimentally across millennia; the study uses structured contexts rather than controlled causal designs.
• Verb selection reflects multiple interacting factors (inheritance, contact, general lexical change), not kinesis alone; kinetic energy is heuristic and not used for precise quantification.
• Linguistic and meteorological maps differ in data density and smoothing; exact spatial matches are not expected, and small-scale anomalies may reflect unmodeled factors.
• The perception experiment used Mandarin-common verbs; other verb inventories across dialects were not tested experimentally.
• Micro-scale, locally confined meteorological events are largely outside the current scope.