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

Journal metrics

  • IF value: 3.089 IF 3.089
  • IF 5-year<br/> value: 3.700 IF 5-year
    3.700
  • CiteScore<br/> value: 3.59 CiteScore
    3.59
  • SNIP value: 1.273 SNIP 1.273
  • SJR value: 2.026 SJR 2.026
  • IPP value: 3.082 IPP 3.082
  • h5-index value: 45 h5-index 45
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
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 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 11.2 µm Tb was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6–11.2 µm)–(11.2–12.4 µm), 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 20–40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

Citation: Lee, S., Han, H., Im, J., Jang, E., and Lee, M.-I.: Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data, Atmos. Meas. Tech., 10, 1859-1874, https://doi.org/10.5194/amt-10-1859-2017, 2017.
Publications Copernicus
Download
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.
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest...
Share