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Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

Authors
  • Park, Seonyoung
  • Seo, Eunkyo
  • Kang, Daehyun
  • Im, Jungho
  • Lee, Myong-In
Publication Date
Nov 15, 2018
Source
MDPI
Keywords
Language
English
License
Green
External links

Abstract

Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

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