Exploring the Thunderstorm Predictors in Indonesia

Indonesia, as a tropical region and prone to frequent thunderstorms, is faced with potential risks to both human life and infrastructure. The analysis of a dataset containing observations of thunderstorms enabled us to identify variations in the vertical air temperature profiles between days experiencing thunderstorms and those characterized by no significant weather conditions (Nosig) within the layer between 1000 and 150 hPa. Examination of the relative humidity in the middle–troposphere during the thunderstorm days exhibited elevated moist layers in compared to the Nosig days across all the investigated regions. By employing stability indices that reflect the atmospheric conditions favorable for thunderstorm development, in conjunction with gridded reanalysis data and logistic regression methodologies, we ascertained that among the numerous convective stability parameters scrutinized, precipitable water (PW), K index (KI), and relative humidity in the middle troposphere (RH_Middle) emerged as the most effective parameters in distinguishing between environmental conditions conducive to thunderstorms and those devoid of significant weather phenomena. Assessment utilizing receiver operating characteristic curves illustrated that the optimal normalized thresholds for PW, KI, and RH_Middle were 0.67, 0.86, and 0.62, respectively.

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Publication Details

Journal
SOLA
Published
2024-12-19
DOI
https://doi.org/10.2151/sola.2025-007
Citations
1
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.78

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article

Exploring the Thunderstorm Predictors in Indonesia

Novvria Sagita, Tetsuya Takemi
1 citations
SOLA
Data Mining and Machine Learning Applications
0.78
article

Exploring the Thunderstorm Predictors in Indonesia

Novvria Sagita, Tetsuya Takemi
article en
1 citations

Abstract

Indonesia, as a tropical region and prone to frequent thunderstorms, is faced with potential risks to both human life and infrastructure. The analysis of a dataset containing observations of thunderstorms enabled us to identify variations in the vertical air temperature profiles between days experiencing thunderstorms and those characterized by no significant weather conditions (Nosig) within the layer between 1000 and 150 hPa. Examination of the relative humidity in the middle–troposphere during the thunderstorm days exhibited elevated moist layers in compared to the Nosig days across all the investigated regions. By employing stability indices that reflect the atmospheric conditions favorable for thunderstorm development, in conjunction with gridded reanalysis data and logistic regression methodologies, we ascertained that among the numerous convective stability parameters scrutinized, precipitable water (PW), K index (KI), and relative humidity in the middle troposphere (RH_Middle) emerged as the most effective parameters in distinguishing between environmental conditions conducive to thunderstorms and those devoid of significant weather phenomena. Assessment utilizing receiver operating characteristic curves illustrated that the optimal normalized thresholds for PW, KI, and RH_Middle were 0.67, 0.86, and 0.62, respectively.

SOLAVol. 21(0)
Kyoto University (JP), Prevention Institute (US), Meteorological, Climatological, And Geophysical Agency (ID)
Ministry of Education, Culture, Sports, Science and Technology, Lembaga Pengelola Dana Pendidikan, European Centre for Medium-Range Weather Forecasts, Japan Society for the Promotion of Science
Climate action
Openalex Percentile: Top 18%
Data Mining and Machine Learning Applications
0.78
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Exploring the Thunderstorm Predictors in Indonesia — Novvria Sagita, Tetsuya Takemi · SOLA (2024) | TGRS Research Map | TGRS