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<p>Hailstorms create considerable threats to agriculture, infrastructure, and communities; nonetheless, their climatological attributes are predominantly unrecorded in tropical areas like Thailand. This study presents a climatological analysis of hailstorms in northern Thailand, examining spatial, temporal, and atmospheric patterns over a decade (2015–2024). The dataset incorporates social media accounts, meteorological station observations, and radiosonde-based air stability indices to examine hailstorm frequency, variability, and environmental variables. Findings demonstrate that hailstorms primarily transpire in the pre-monsoon season (March–May), reaching a zenith in April, with greater activity occurring in the afternoon to early evening (15:00–19:00 local time). Spatial analysis using GIS indicates that diurnal hail frequency peaks during these hours, corresponding with solar heating that enhances convective instability. Hail frequencies were highest in Chiang Mai, Chiang Rai, and Lampang, with spatial variations attributed to orographic effects and mesoscale convective phenomena. Interannual variability was apparent, with 2023 recording the highest frequency of hailstorms (28&#xa0;days) and 2019 the lowest (6&#xa0;days), indicating possible correlations with ENSO and extensive circulation patterns. Atmospheric investigation indicates that hailstorms correlate with elevated Convective Available Potential Energy (CAPE &gt; 1,000&#xa0;J/kg) and northwesterly wind patterns, which are favorable for deep convection. The extreme hail occurrence on April 9, 2024, impacting five provinces, illustrates the influence of cold air intrusions and interactions with westerly troughs in the formation of hailstorms. These results may inform hailstorm prediction and risk management strategies in the region. Future study requires dual-polarization radar validation, reanalysis datasets, and synoptic-scale analyses to improve hailstorm predictability in tropical areas.</p>

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Spatiotemporal characteristics and diurnal patterns of pre-monsoon hailstorms in northern Thailand

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Nattapon Mahavik,
  • Apichaya Kangerd,
  • Jamorn Kunwilai,
  • Sarawut Arthayakun

摘要

Hailstorms create considerable threats to agriculture, infrastructure, and communities; nonetheless, their climatological attributes are predominantly unrecorded in tropical areas like Thailand. This study presents a climatological analysis of hailstorms in northern Thailand, examining spatial, temporal, and atmospheric patterns over a decade (2015–2024). The dataset incorporates social media accounts, meteorological station observations, and radiosonde-based air stability indices to examine hailstorm frequency, variability, and environmental variables. Findings demonstrate that hailstorms primarily transpire in the pre-monsoon season (March–May), reaching a zenith in April, with greater activity occurring in the afternoon to early evening (15:00–19:00 local time). Spatial analysis using GIS indicates that diurnal hail frequency peaks during these hours, corresponding with solar heating that enhances convective instability. Hail frequencies were highest in Chiang Mai, Chiang Rai, and Lampang, with spatial variations attributed to orographic effects and mesoscale convective phenomena. Interannual variability was apparent, with 2023 recording the highest frequency of hailstorms (28 days) and 2019 the lowest (6 days), indicating possible correlations with ENSO and extensive circulation patterns. Atmospheric investigation indicates that hailstorms correlate with elevated Convective Available Potential Energy (CAPE > 1,000 J/kg) and northwesterly wind patterns, which are favorable for deep convection. The extreme hail occurrence on April 9, 2024, impacting five provinces, illustrates the influence of cold air intrusions and interactions with westerly troughs in the formation of hailstorms. These results may inform hailstorm prediction and risk management strategies in the region. Future study requires dual-polarization radar validation, reanalysis datasets, and synoptic-scale analyses to improve hailstorm predictability in tropical areas.