Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Himawari-8
2.3. Field Reference Data
2.4. LDAPS
2.5. HYCOM SST
3. Methodology
3.1. Extraction of Ocean Fog Reference Data
3.2. Modeling
3.3. Evaluation
4. Results and Discussion
4.1. Quantitative Assessment
4.2. Variable Contribution Analysis
4.3. Evaluation of Spatial Distribution
4.4. Evaluation of Spatiotemporal Distribution
5. Conclusions
Author Contributions
Funding
Data Availability Statement
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- Himawari-8: ftp.ptree.jaxa.jpThe access account is required following the completion of the registration.
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Conflicts of Interest
References
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Source | Variable Name | Description |
---|---|---|
Himawari-8 | SWR_6to9h | Accumulated shortwave radiation from −9 to −6 h |
SWR_6to12h | Accumulated shortwave radiation from −12 to −6 h | |
SWR_6to24h | Accumulated shortwave radiation from −24 to −6 h | |
SWR_preday | Accumulated shortwave radiation in previous day | |
cooling_H | Cooling hours without shortwave radiation (hours) | |
LDAPS | Ta | Air temperature (°C) |
RH | Relative humidity (%) | |
U | u-vector wind (m/s) | |
V | v-vector wind (m/s) | |
WS | Wind speed (m/s) | |
P | Pressure (Pa) | |
VIS | Visibility (m) | |
HYCOM | SST | Sea surface temperature (°C) |
LDAPS & HYCOM | TD | Temperature difference between sea surface and air (°C) |
Purpose | Year | Data Type | Number of Ocean Fog Cases |
---|---|---|---|
Training | 2019 | Analysis | 3001 |
2021 | Analysis | 1987 | |
2022 | Analysis | 912 | |
Test | 2020 | Analysis | 2187 |
Forecast +1 h | 2170 | ||
Forecast +2 h | 2300 | ||
Forecast +3 h | 2008 | ||
Forecast +4 h | 624 | ||
Forecast +5 h | 767 | ||
Forecast +6 h | 1111 |
Reference | |||
Ocean fog | Non-fog | ||
Predicted | Ocean fog | TT | FT |
Non-fog | TF | FF |
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Sim, S.; Im, J.; Jung, S.; Han, D. Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML. Remote Sens. 2024, 16, 2348. https://doi.org/10.3390/rs16132348
Sim S, Im J, Jung S, Han D. Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML. Remote Sensing. 2024; 16(13):2348. https://doi.org/10.3390/rs16132348
Chicago/Turabian StyleSim, Seongmun, Jungho Im, Sihun Jung, and Daehyeon Han. 2024. "Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML" Remote Sensing 16, no. 13: 2348. https://doi.org/10.3390/rs16132348
APA StyleSim, S., Im, J., Jung, S., & Han, D. (2024). Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML. Remote Sensing, 16(13), 2348. https://doi.org/10.3390/rs16132348