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All Journal Jurnal Teknosains BULETIN FISIKA
Retnadi Heru Jatmiko
Fakultas Geografi, Universitas Gadjah Mada
Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Pengaruh koreksi atenuasi radar cuaca terhadap perhitungan estimasi curah hujan di Jawa Timur Ahmad Kosasih; Hartono Hartono; Retnadi Heru Jatmiko
Jurnal Teknosains Vol 10, No 2 (2021): June
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.53452

Abstract

Rainfall estimation using band C weather radar creates uncertainty in the results of its estimation accuracy. The cause is meteorological and non-meteorological disturbances that affect the reflectivity raw data (dBz), one of which is attenuation due to rain, especially with heavy and very heavy intensity. This study aims to evaluate the attenuation correction ability of the reflectivity raw data generated by the weather radar against the calculation of rainfall estimates at the Juanda Sidoarjo Meteorological Station, as well as the best attenuation correction coefficient to be applied in the processing of rainfall estimates by weather radar. The method used to perform attenuation correction is Z-based attenuation correction (ZATC). The calculation of attenuation correction using the ZATC method uses several α and β coefficients while the Z-R relation (Z = 200R1.6) is used to calculate the estimated rainfall before and after attenuation correction. The results showed that the attenuation correction of the C band weather radar reflectivity raw data was able to provide an increase in the accuracy of rainfall estimation where in the estimation of rainfall from a weather radar without the attenuation correction stage of the raw data, an accuracy value of 70.8% was obtained, while applying the attenuation correction using several The α and β coefficients obtained an increase in the accuracy of rainfall estimation between 72.5% to 86.9%. The best α and β coefficients for attenuation correction of weather radar reflectivity (dBz) can be applied in obtaining a more accurate rainfall estimate, namely the α and β coefficients according to Krämer and Verworn which are able to provide an increase in the accuracy of rainfall estimation by 16.1%.
Perbaikan Estimasi Hujan Multisatelit Berbasis Google Earth Engine dengan Data Penakar Hujan di Sulawesi Selatan Prayoga Ismail; Retnadi Heru Jatmiko; Nur Mohammad Farda; Muhammad Arif Munandar
BULETIN FISIKA Vol 26 No 1 (2025): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

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Abstract

Precipitation, particularly rainfall, is vital in understanding weather and climate. In Indonesia, the uneven distribution of in situ rainfall observations poses a challenge to accurately measuring surface rainfall. Remote sensing systems and cloud computing technologies, such as Google Earth Engine (GEE), offer potential solutions. This study evaluates the spatial distribution and performance of four multi-satellite rainfall estimates available in GEE, namely CHIRPS, GSMAP, GPM-IMERG, and PERSIANN-CDR, before and after calibration using BMKG rain gauge data in South Sulawesi during the 2018–2023 period. The original multi-satellite data revealed significant discrepancies, with an annual RMSE of 1534 mm/year, a yearly RSQ value of 0.3, and an annual RBIAS of 27% compared to observational data. Among the datasets, O_CHPS demonstrated the best spatial similarity visually. Calibration using the Geographical Differential Analysis (GDA) method effectively enhanced the accuracy, reducing the annual RMSE to 807 mm/year, increasing the yearly RSQ to 0.5, and lowering the annual bias to 1.6%. Improvements were also noted in monthly and daily rainfall estimates. After calibration, C_PRSN exhibited the most favorable spatial distribution and performance, achieving a 26% reduction in annual RMSE, a 105% increase in annual RSQ, and a 101% decrease in annual bias compared to its initial data. Furthermore, sensitivity to elevation and rainfall intensity was enhanced, with improved detection indicators, particularly for heavy to extreme rainfall events. This included a 43% increase in POD, a 262% increase in CSI, and a 42% reduction in FAR.
Co-Authors Ahmad Kosasih Hartono Hartono Muhammad Arif Munandar Nur Mohammad Farda Prayoga Ismail