Cluster analysis of rainfall patterns in Mamminasata: Validation of climate hazards group infrared precipitation with station dataset using observational comparisons

This study aims to apply the cluster method to objectively classify rainfall patterns in the Mamminasata monsoon region based on observational data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. Validation was carried out using dichotomous and numerical comparisons with in situ observational data to strengthen the validity of CHIRPS data. The results showed that observational and CHIRPS data showed three different rainfall patterns. The first pattern shows rainfall at the beginning or end of the year ranging from 80-110 mm, decreasing after the 9th day of March, then increasing again in May to mid-June. The second pattern has a slightly lower rainfall intensity than the first one. The third pattern shows rainfall that increases beyond 100 mm at the end of each year, decreasing after February and reaching its lowest point in August. Towards the end of the year, there is an increase exponentially. CHIRPS rainfall predictions tend to overestimate in the southwest coastal area of Mamminasata, while underestimations are observed in the northern urban areas and eastern mountains. The accuracy of satellite rainfall estimates varies significantly across the Mamminasata region. In general, the performance of CHIRPS rainfall estimates is better in lowland areas than in mountainous areas.

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Publication Details

Journal
Edelweiss Applied Science and Technology
Published
2024-12-31
DOI
https://doi.org/10.55214/25768484.v8i6.4035
Citations
1
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.38

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article

Cluster analysis of rainfall patterns in Mamminasata: Validation of climate hazards group infrared precipitation with station dataset using observational comparisons

Nurtiti Sunusi, Sumanta Pasari, Giarno Giarno, Muflihah Muflihah
1 citations
Edelweiss Applied Science and Technology
Precipitation Measurement and Analysis
0.38
article

Cluster analysis of rainfall patterns in Mamminasata: Validation of climate hazards group infrared precipitation with station dataset using observational comparisons

Nurtiti Sunusi, Sumanta Pasari, Giarno Giarno, Muflihah Muflihah
article en
1 citations

Abstract

This study aims to apply the cluster method to objectively classify rainfall patterns in the Mamminasata monsoon region based on observational data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. Validation was carried out using dichotomous and numerical comparisons with in situ observational data to strengthen the validity of CHIRPS data. The results showed that observational and CHIRPS data showed three different rainfall patterns. The first pattern shows rainfall at the beginning or end of the year ranging from 80-110 mm, decreasing after the 9th day of March, then increasing again in May to mid-June. The second pattern has a slightly lower rainfall intensity than the first one. The third pattern shows rainfall that increases beyond 100 mm at the end of each year, decreasing after February and reaching its lowest point in August. Towards the end of the year, there is an increase exponentially. CHIRPS rainfall predictions tend to overestimate in the southwest coastal area of Mamminasata, while underestimations are observed in the northern urban areas and eastern mountains. The accuracy of satellite rainfall estimates varies significantly across the Mamminasata region. In general, the performance of CHIRPS rainfall estimates is better in lowland areas than in mountainous areas.

Edelweiss Applied Science and TechnologyVol. 8(6)
Meteorological, Climatological, And Geophysical Agency (ID), Birla Institute of Technology and Science, Pilani (IN), Hasanuddin University (ID)
Universitas Hasanuddin
Climate action
Openalex Percentile: Top 37%
Precipitation Measurement and Analysis
0.38
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Cluster analysis of rainfall patterns in Mamminasata: Validation of climate hazards group infrared precipitation with station dataset using observational comparisons — Nurtiti Sunusi, Sumanta Pasari, et al. · Edelweiss Applied Science and Technology (2024) | TGRS Research Map | TGRS