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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 5 | Copyright
Atmos. Meas. Tech., 10, 1859-1874, 2017
https://doi.org/10.5194/amt-10-1859-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 Lee1, Hyangsun Han2, Jungho Im1, Eunna Jang1, and Myong-In Lee1 Sanggyun Lee et al.
  • 1School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
  • 2Unit of Arctic Sea-Ice prediction, Korea Polar Research Institute, Incheon, 21990, South Korea

Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2µm Tb was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6–11.2µm)–(11.2–12.4µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20–40min, which implies that convective clouds can be detected 30min in advance, before precipitation intensity exceeds 35dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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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.
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest...
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