Articles | Volume 10, issue 5
https://doi.org/10.5194/amt-10-1859-2017
https://doi.org/10.5194/amt-10-1859-2017
Research article
 | 
24 May 2017
Research article |  | 24 May 2017

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Sanggyun Lee, Hyangsun Han, Jungho Im, Eunna Jang, and Myong-In Lee

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Cited articles

Amorati, R., Alberoni, P. P., Levizzani, V., and Nanni, S.: IR-based satellite and radar rainfall estimates of convective storms over northern Italy, Meteorol. Appl., 7, 1–18, https://doi.org/10.1017/S1350482700001328, 2000.
Banacos, P. C. and Schultz, D. M.: The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives, Weather Forecast., 20, 351–366, https://doi.org/10.1175/WAF858.1, 2005.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9; Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016.
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Craven, J. P., Jewell, R. E., and Brooks, H. E.: Comparison between Observed Convective Cloud-Base Heights and Lifting Condensation Level for Two Different Lifted Parcels, Weather Forecast., 17, 885–890, https://doi.org/10.1175/1520-0434(2002)017<0885:CBOCCB>2.0.CO;2, 2002.
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Short summary
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 AHI data obtained over the Korean Peninsula. We used a total of 12 interest fields including time trends to develop the models. We identified contributing variables for CI detection. DT showed a higher hit rate, while RF produced a higher critical success index. The mean lead times by the four models were in the range of 20–40 min.