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Hayim Muhammad Agiel
Program Studi Sains Atmosfer dan Keplanetan, Institut Teknologi Sumatera, Lampung Selatan 35365, Lampung, Indonesia
Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Science and Applicative Technology

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Evaluasi Satellite Precipitation Product (GSMaP, CHIRPS, dan IMERG) di Kabupaten Lampung Selatan Alvin Pratama; Hayim Muhammad Agiel; Ade Ayu Oktaviana
Journal of Science and Applicative Technology Vol 6 No 1 (2022): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v6i1.702

Abstract

The availability of rainfall data is an important requirement in various activities. The limitations of observational data make the satellit precipitation product (SPPs) as an alternative. However, the data must be verified before being used. Verification methods are done by using matric statistic methods such as correlation, error and relative bias. Meanwhile, to see the ability of SPPs in detecting rainfall events, it uses a contingency table method. The purpose of this research is to evaluate the ability of SPPs against observation data. Evaluation of SPPs rainfall data is carried out based on a time scale, namely monthly, 10 daily, and daily. This research uses the data from 2018–2020. On a monthly and 10 daily scale, the CHIRPS data shows excellent linearity and rain detection ability. On a daily scale, IMERG shows better linearity than GSMaP and CHIRPS in every season, with moderate to strong correlation coefficients. However, these data tend to be underestimated with a very large bias. In terms of detecting daily rain, GSMaP tends to be better than CHIRPS and IMERG on every season based on the CSI index. However, in the dry season and transition II, the ability tends to be lower. In general, for the amount of rainfall intensity, the three SPPs data still has a fairly large error against the observation data eventhough the ability to detect rainfall is good.
Co-Authors Ade Ayu Oktaviana Alvin Pratama