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Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data Nurhamidah, Nurhamidah; Andari, Rafika; Junaidi, Ahmad; Daoed, Darwizal
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1578

Abstract

The availability of hydrological data is one of the challenges associated with developing water infrastructure in different areas. This led to the TRMM (Tropical Precipitation Measurement Mission) design by NASA, which involves using satellite weather monitoring technology to monitor and analyze tropical precipitation in different parts of the world. Therefore, this validation study was conducted to compare TRMM precipitation data with observed precipitation to determine its application as an alternate source of hydrological data. The Kuranji watershed was selected as the study site due to the availability of suitable data. Moreover, the validation analyses applied include the Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Coefficient Correlation (R), and Relative Error (RE). These used two calculation forms: one for the uncorrected data and another for the corrected data. The results showed that the best-adjusted data validation from the Gunung Nago station in 2016 was recorded to be RMSE = 62,298, NSE = 0.044, R = 0.902, and RE = 11,328. The closeness of the R-value to one implies that the corrected TRMM data outperforms the uncorrected ones. Therefore, it was generally concluded that the TRMM data matches the observed precipitation data and can be used for hydrological study in the Kuranji watershed
Kajian Literatur Penggunaan Data Satelit GPM-IMERG pada Aplikasi Hidrologi Andari, Rafika; Nurhamidah
Jurnal Serambi Engineering Vol. 11 No. 1 (2026): Januari 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Global Precipitation Measurement (GPM) – Integrated Multi-satellite Retrievals for GPM (IMERG) satellite data is one of the main sources of global rainfall estimates with high spatial and temporal resolution. This study aims to identify spatial, temporal, and methodological trends in research related to the use of GPM-IMERG between 2014 and 2024, as well as to assess the effectiveness of various bias correction methods in improving data accuracy and to examine the main hydrological applications utilising these data. A systematic review of over twenty scientific publications reveals that the use of GPM-IMERG has grown considerably since 2017, particularly in humid tropical regions such as Southeast Asia, South Asia, and Latin America. Methodologically, there has been a shift towards integrating GPM-IMERG with physically based hydrological models (e.g. SWAT, HEC-HMS and VIC) and machine learning algorithms (e.g. Random Forest and XGBoost) to improve the prediction of rainfall and river discharge. Analysis also shows that Quantile Mapping (QM) and Distribution Mapping (DM) provide the best correction performance, increasing NSE values by 20–35% in mountainous areas. Linear Scaling (LS) remains effective in tropical lowlands. The most prevalent hydrological applications are discharge modelling, flood analysis and drought monitoring. Key research gaps include the absence of long-term studies (>10 years), limitations in topographically complex regions and the lack of multi-sensor integration.