2019
DOI: 10.1088/1755-1315/303/1/012054
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Performance of Principal Component Analysis to Classify Precipitation Type from Raindrop Size Distribution Data at Kototabang, Indonesia

Abstract: This study examines the use of principal component analysis (PCA) to classify the RDSD data at Kototabang, Indonesia. In addition to PCA with 6 attributes (hereinafter called PCA6) that had been developed by a previous researcher, this study also examines PCA with 7 attributes (PCA7) by adding radar reflectivity factor. The PCA is applied to the RDSD that had been classified by a wind profiler into Stratiform (S), deep convective (DC), shallow convective (SHC) and mixed stratiform/convective (MSC). The number … Show more

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Cited by 2 publications
(2 citation statements)
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“…(2018) and Marzuki et al . (2019). The data are restricted to the total number of drops exceeding 50 in a 30‐s sample, rain rate above 0.1 mm·hr −1 and μ values ranging between −4 and 15.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…(2018) and Marzuki et al . (2019). The data are restricted to the total number of drops exceeding 50 in a 30‐s sample, rain rate above 0.1 mm·hr −1 and μ values ranging between −4 and 15.…”
Section: Resultsmentioning
confidence: 99%
“…The application of PCA on DSD requires M (number of quantities describing DSD) arrays of length N (number of DSD observations). To understand the DSD variability, the present study selected the DSD parameters, D m , N w and 𝜎 m and the rain integral parameters LWC, N t (total number of drops) and rain rate following Dolan et al (2018) and Marzuki et al (2019). The data are restricted to the total number of drops exceeding 50 in a 30-s sample, rain rate above 0.1 mm⋅hr −1 and 𝜇 values ranging between −4 and 15.…”
Section: Empirical Orthogonal Function (Eof) Analysismentioning
confidence: 99%