Performance of Principal Component Analysis to Classify Precipitation Type from Raindrop Size Distribution Data at Kototabang, Indonesia

International Conference On Tropical Meteorology And Atmospheric Sciences

生态环境科学;原子能学

Marzuki^1 ; Hashiguchi, H.^2 ; Vonnisa, M.^1 ; Harmadi^1 ; Shimomai, T.^3 ; Yoseva, M.^1 ; Saufina, E.^1

Department of Physics, Andalas University, Padang, Indonesia^1

Research Institute for Sustainable Humanosphere (RISH), Kyoto University, Japan^2

Interdisciplinary Faculty of Science and Engineering, Shimane University, Japan^3

关键词: deep convective;    Indonesia;    Radar reflectivity factor;    Rain type;    raindrop;    Raindrop size distribution;    Wind profiler;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/303/1/012054/pdf
DOI  :  10.1088/1755-1315/303/1/012054

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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 of unclassified data is much smaller than that reported by previous study in which it is about 33-47% with PCA6 and 29-44% with PCA7. While the PCA classifies the same group for different rain type from wind profiler, especially for Group I (moderate Do and large N W) and II (small Do and N W), some differences are observed. Each rain type classified by wind profiler has different dominant group in which Group II is dominant for S, Group I and V (large Do and low N w) are dominant for DC, Group I, IV (small Do and large N w) and VI (small Do and very large N w) are dominant for SHC and Group I is also dominant for MSC type.