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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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doi:10.5194/amt-2016-308
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
01 Nov 2016
Review status
A revision of this discussion paper is under review for the journal Atmospheric Measurement Techniques (AMT).
Detection of deterministic and probabilistic convective initiation using Himawari-8 Advanced Himawari Imager data
Sanggyun Lee1, Hyangsun Han2, Jungho Im1,3, Eunna Jang1, and Myong-In Lee1 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
3Environmental Resource Engineering, State University of New York, College of Environmental Science and Forestry, 13210, Syracuse, NY, USA
Abstract. Detection of Convective Initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socioeconomic damages. 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 2015 over the Korean Peninsula. We used a total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends to develop CI detection models. While the interest field of 11.2 μm TB was considered the most crucial to detect CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e., (8.6–11.2 μm) − 11.2–12.4 μm), was determined as 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 POD, while the LR model produced higher OA and CSI and lower FAR than the other models. The CI detection of the mean lead times by the four models were in the range of 32–35 min, which implies that convective clouds can be detected in 30 min advance before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data is possible.

Citation: Lee, S., Han, H., Im, J., Jang, E., and Lee, M.-I.: Detection of deterministic and probabilistic convective initiation using Himawari-8 Advanced Himawari Imager data, Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-308, in review, 2016.
Sanggyun Lee et al.
Sanggyun Lee et al.
Sanggyun Lee et al.

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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 LR produced a lower false alarm rate. The mean lead times by the four models were in the range of 32–35 min.
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest...
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