Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
<p>Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-10-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/5895/2023/gmd-16-5895-2023.pdf |
| _version_ | 1797654553257050112 |
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| author | D. Han J. Im Y. Shin Y. Shin J. Lee |
| author_facet | D. Han J. Im Y. Shin Y. Shin J. Lee |
| author_sort | D. Han |
| collection | DOAJ |
| description | <p>Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 <span class="inline-formula">h</span>), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 <span class="inline-formula">h</span>. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (<span class="inline-formula">≤</span> 5 <span class="inline-formula">mm h<sup>−1</sup></span>), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 <span class="inline-formula">h</span>) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.</p> |
| first_indexed | 2024-03-11T17:01:10Z |
| format | Article |
| id | doaj.art-49bf3d95553b454788aca47938c3c59b |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-03-11T17:01:10Z |
| publishDate | 2023-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-49bf3d95553b454788aca47938c3c59b2023-10-20T07:28:16ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-10-01165895591410.5194/gmd-16-5895-2023Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learningD. Han0J. Im1Y. Shin2Y. Shin3J. Lee4Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South KoreaDepartment of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South KoreaNational Institute of Meteorological Sciences, Korea Meteorological Administration, Jeju-do, 63568, South KoreaMarket Intelligence Team, CJ CheilJedang Corporation, Seoul, 04560, South KoreaDepartment of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea<p>Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 <span class="inline-formula">h</span>), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 <span class="inline-formula">h</span>. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (<span class="inline-formula">≤</span> 5 <span class="inline-formula">mm h<sup>−1</sup></span>), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 <span class="inline-formula">h</span>) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.</p>https://gmd.copernicus.org/articles/16/5895/2023/gmd-16-5895-2023.pdf |
| spellingShingle | D. Han J. Im Y. Shin Y. Shin J. Lee Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning Geoscientific Model Development |
| title | Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| title_full | Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| title_fullStr | Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| title_full_unstemmed | Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| title_short | Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| title_sort | key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning |
| url | https://gmd.copernicus.org/articles/16/5895/2023/gmd-16-5895-2023.pdf |
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