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<p>This study identifies a few macro and microphysical parameters associated with cloudburst events that occurred during Indian Summer Monsoon season of 2022 and 2023 over the North West Himalayan (NWH) region. A synergistic approach of integrating remote sensing observations from geostationary(GEO) and low Earth orbit(LEO) satellites is employed to characterize key macro and microphysical parameters governing these cloudbutrsts. Analysis of INSAT-3D data identifies Outgoing Longwave Radiation (OLR), Cloud Top Height (CTH), and Cloud Top Temperature (CTT) as primary macrophysical variables associated with cloudbursts. Statistical assessment reveals a second-order power-law relationship between OLR and rainfall, whereas CTT and CTH exhibit strong linear correlation at 95% confidence level. During cloudbursts, OLR and CTT decrease significantly, with minimum values of 106.39&#xa0;W/m<sup>2</sup> and 194.22&#xa0;K, respectively. Majority of the cloudbursts are characterized by deep convective clouds with CTH ranging between 15 and 20&#xa0;km, generating intense rainfall intensity. After the cloudbursts, convective systems dissipate predominantly northeastward. The contrasting correlations between OLR-rainfall and CTT-CTH effectively link macrophysical and microphysical cloud properties. Further investigation utilizing GPM satellite data identifies cloud reflectivity, mass-weighted mean diameter, and normalized scaling parameters as key microphysical signatures of cloudburst events. Hydrometeor profiling reveals precipitable water and cloud liquid water as dominant components, enhancing orographic rainfall. Precipitable water extends up to 6&#xa0;km, while cloud liquid water reaches altitudes of 10&#xa0;km at most of the cloudburst sites. The novel outcome of this study is the identification of critical macro- and microphysical precursors of cloudbursts, providing valuable insights for nowcasting and improving early warning systems for extreme weather events in mountainous regions.</p>

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Macro and microphysical parameters associated with cloudburst events over North West Himalayan region: remote sensing based observations and analysis

  • 期刊论文

  • 发表日期：

    2025年6月7日

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  • Ahana Mukhopadhyay,
  • Charu Singh

摘要

This study identifies a few macro and microphysical parameters associated with cloudburst events that occurred during Indian Summer Monsoon season of 2022 and 2023 over the North West Himalayan (NWH) region. A synergistic approach of integrating remote sensing observations from geostationary(GEO) and low Earth orbit(LEO) satellites is employed to characterize key macro and microphysical parameters governing these cloudbutrsts. Analysis of INSAT-3D data identifies Outgoing Longwave Radiation (OLR), Cloud Top Height (CTH), and Cloud Top Temperature (CTT) as primary macrophysical variables associated with cloudbursts. Statistical assessment reveals a second-order power-law relationship between OLR and rainfall, whereas CTT and CTH exhibit strong linear correlation at 95% confidence level. During cloudbursts, OLR and CTT decrease significantly, with minimum values of 106.39 W/m2 and 194.22 K, respectively. Majority of the cloudbursts are characterized by deep convective clouds with CTH ranging between 15 and 20 km, generating intense rainfall intensity. After the cloudbursts, convective systems dissipate predominantly northeastward. The contrasting correlations between OLR-rainfall and CTT-CTH effectively link macrophysical and microphysical cloud properties. Further investigation utilizing GPM satellite data identifies cloud reflectivity, mass-weighted mean diameter, and normalized scaling parameters as key microphysical signatures of cloudburst events. Hydrometeor profiling reveals precipitable water and cloud liquid water as dominant components, enhancing orographic rainfall. Precipitable water extends up to 6 km, while cloud liquid water reaches altitudes of 10 km at most of the cloudburst sites. The novel outcome of this study is the identification of critical macro- and microphysical precursors of cloudbursts, providing valuable insights for nowcasting and improving early warning systems for extreme weather events in mountainous regions.