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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%.