Articles | Volume 10, issue 5
https://doi.org/10.5194/amt-10-1859-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-1859-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Lee
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Hyangsun Han
Unit of Arctic Sea-Ice prediction, Korea Polar Research Institute,
Incheon, 21990, South Korea
Jungho Im
CORRESPONDING AUTHOR
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Eunna Jang
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Myong-In Lee
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
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- Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting W. Woo & W. Wong 10.3390/atmos8030048
41 citations as recorded by crossref.
- Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches S. Sim et al. 10.3390/rs10040631
- A Novel Approach for the Detection of Developing Thunderstorm Cells R. Müller et al. 10.3390/rs11040443
- Rain Detection using Himawari-8 Imagery; Case Study Singkawang West Kalimantan C. Dharma & N. Trilaksono 10.1088/1755-1315/750/1/012011
- Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions Y. Kang et al. 10.1016/j.rse.2023.113814
- A simplified method for the detection of convection using high-resolution imagery from GOES-16 Y. Lee et al. 10.5194/amt-14-3755-2021
- Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas D. Cho et al. 10.1029/2019EA000740
- Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat C. Zhang et al. 10.1080/01431161.2019.1594438
- Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models R. Dávila-Ortiz et al. 10.3390/rs16040675
- Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. 10.3390/rs10010137
- Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data M. Kim et al. 10.3390/rs11101195
- Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations X. ZHUGE & X. ZOU 10.2151/jmsj.2018-041
- Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks Y. Kim et al. 10.5194/tc-14-1083-2020
- Estimation of Maximum Hail Diameters from FY-4A Satellite Data with a Machine Learning Method Q. Wu et al. 10.3390/rs14010073
- Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea E. Jang et al. 10.3390/rs11030271
- Evaluation of lightning warning technique with multi-source data for Vung Tau coastal area H. Hoang et al. 10.15625/1859-3097/18413
- Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification T. Liu & A. Abd-Elrahman 10.1016/j.isprsjprs.2018.03.006
- Rapidly Evolving Cirrus Clouds Modulated by Convectively Generated Gravity Waves A. Prasad et al. 10.1029/2019JD030538
- Multi-Scale Bushfire Detection From Multi-Modal Streams of Remote Sensing Data T. Phan et al. 10.1109/ACCESS.2020.3046649
- An Assessment of Convective Initiation Nowcasting Algorithm within 0-60 Minutes using Himawari-8 Satellite I. Perdana & D. Septiadi 10.1088/1755-1315/893/1/012049
- Prediction of extreme rain in Kototabang using Himawari-8 satellite based on differences in cloud brightness temperature N. Jumianti et al. 10.1016/j.rsase.2023.101102
- Convective Initiation Nowcasting Over China From Fengyun-4A Measurements Based on TV-L1 Optical Flow and BP_Adaboost Neural Network Algorithms F. Sun et al. 10.1109/JSTARS.2019.2952976
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al. 10.1109/TGRS.2023.3298976
- Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data 10.1175/MWR-D-21-0096.1
- Improving Radar Reflectivity Reconstruction with Himawari-9 and UNet++ for Off-Shore Weather Monitoring B. Wan & C. Gao 10.3390/rs16010056
- Spatio-temporal fire detection based on brightness temperature change in Himawari-8 images C. Zhang et al. 10.1080/01431161.2022.2135414
- Detection of Tropical Overshooting Cloud Tops Using Himawari-8 Imagery M. Kim et al. 10.3390/rs9070685
- Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data C. Yoo et al. 10.1016/j.isprsjprs.2018.01.018
- A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data K. Zhou et al. 10.1175/JTECH-D-19-0146.1
- On the utilization of RDCA method for detecting and predicting the occurrence of heavy rainfall in Indonesia W. Harjupa et al. 10.1016/j.rsase.2021.100681
- On a Flood-Producing Coastal Mesoscale Convective Storm Associated with the Kor’easterlies: Multi-Data Analyses Using Remotely-Sensed and In-Situ Observations and Storm-Scale Model Simulations S. Park & S. Park 10.3390/rs12091532
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al. 10.1016/j.rse.2023.113762
- A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning A. Shishov 10.3103/S1068373924040071
- A Novel Multitemporal Image-Fusion Algorithm: Method and Application to GOCI and Himawari Images for Inland Water Remote Sensing Y. Guo et al. 10.1109/TGRS.2019.2960322
- Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery T. Fu et al. 10.1117/1.JRS.12.025010
- Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data H. Han & H. Kim 10.1016/j.rse.2018.02.058
- Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data E. Jang et al. 10.3390/rs9080821
- A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data D. Han et al. 10.3390/rs11121454
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- Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data M. Kim et al. 10.1016/j.rse.2020.111782
- Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique D. Cho et al. 10.1080/15481603.2020.1766768
- Quantitative Applications of Weather Satellite Data for Nowcasting: Progress and Challenges J. Li et al. 10.1007/s13351-024-3138-6
1 citations as recorded by crossref.
Latest update: 17 Jul 2024
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...
