The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications
Abstract The growing demand for renewable energy underscores the importance of accurate dust forecasting in regions with abundant wind and solar resources. However, leading real‐time global numerical weather prediction (NWP) models often lack dust modules due to computational constraints. Current “N...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-01-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004525 |
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| author | Xi Chen Mei Chong Shian‐Jiann Lin Zhi Liang Paul Ginoux Yuan Liang Bihui Zhang Qian Song Shengkai Wang Jiawei Li Yimin Liu |
| author_facet | Xi Chen Mei Chong Shian‐Jiann Lin Zhi Liang Paul Ginoux Yuan Liang Bihui Zhang Qian Song Shengkai Wang Jiawei Li Yimin Liu |
| author_sort | Xi Chen |
| collection | DOAJ |
| description | Abstract The growing demand for renewable energy underscores the importance of accurate dust forecasting in regions with abundant wind and solar resources. However, leading real‐time global numerical weather prediction (NWP) models often lack dust modules due to computational constraints. Current “Near‐Real‐Time” dust forecasting services can only run after the completion of NWP, failing to meet the timeliness requirements for reporting power generation plans to the grids. This work proposes a global dust‐weather integrated (iDust) model development paradigm, efficiently incorporating dust modules into the dynamical core. Using about one‐eighth additional computing power, iDust extends global 12.5 km resolution NWP with dust prediction capabilities. iDust's forecasting abilities are evaluated against ECMWF CAMS forecast and NASA MERRA2 reanalysis, including verifications over China from March to May 2023 and three extreme dust events. Results show that iDust outperforms its counterparts in dust storm forecasting intensity and timing. Using iDust, global 12.5‐km 10‐day hourly dust storm forecast simulations initiated at 00UTC can produce results by 06UTC, enabling timely forecasting of severe dust storms with concentrations exceeding 1,000 μg/m3. This novel capability of iDust can meet the urgent forecasting needs of the renewable energy industry for extreme dust conditions, supporting the green energy transition. |
| format | Article |
| id | doaj-art-c528d10537a74bd28ec3840013dfa643 |
| institution | Kabale University |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-c528d10537a74bd28ec3840013dfa6432025-01-28T13:21:09ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-01-01171n/an/a10.1029/2024MS004525The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy ApplicationsXi Chen0Mei Chong1Shian‐Jiann Lin2Zhi Liang3Paul Ginoux4Yuan Liang5Bihui Zhang6Qian Song7Shengkai Wang8Jiawei Li9Yimin Liu10National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing ChinaTianJi Weather Science and Technology Company Beijing ChinaTianJi Weather Science and Technology Company Beijing ChinaGeophysical Fluid Dynamics Laboratory NOAA/OAR Princeton NJ USATianJi Weather Science and Technology Company Beijing ChinaNational Meteorological Centre Beijing ChinaTianJi Weather Science and Technology Company Beijing ChinaXiamen University Xiamen ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing ChinaNational Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics, Chinese Academy of Sciences Beijing ChinaAbstract The growing demand for renewable energy underscores the importance of accurate dust forecasting in regions with abundant wind and solar resources. However, leading real‐time global numerical weather prediction (NWP) models often lack dust modules due to computational constraints. Current “Near‐Real‐Time” dust forecasting services can only run after the completion of NWP, failing to meet the timeliness requirements for reporting power generation plans to the grids. This work proposes a global dust‐weather integrated (iDust) model development paradigm, efficiently incorporating dust modules into the dynamical core. Using about one‐eighth additional computing power, iDust extends global 12.5 km resolution NWP with dust prediction capabilities. iDust's forecasting abilities are evaluated against ECMWF CAMS forecast and NASA MERRA2 reanalysis, including verifications over China from March to May 2023 and three extreme dust events. Results show that iDust outperforms its counterparts in dust storm forecasting intensity and timing. Using iDust, global 12.5‐km 10‐day hourly dust storm forecast simulations initiated at 00UTC can produce results by 06UTC, enabling timely forecasting of severe dust storms with concentrations exceeding 1,000 μg/m3. This novel capability of iDust can meet the urgent forecasting needs of the renewable energy industry for extreme dust conditions, supporting the green energy transition.https://doi.org/10.1029/2024MS004525NWPextreme dust stormsolar energyhigh‐resolution dust forecastphysics‐dynamics integrationreal‐time dust forecast |
| spellingShingle | Xi Chen Mei Chong Shian‐Jiann Lin Zhi Liang Paul Ginoux Yuan Liang Bihui Zhang Qian Song Shengkai Wang Jiawei Li Yimin Liu The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications Journal of Advances in Modeling Earth Systems NWP extreme dust storm solar energy high‐resolution dust forecast physics‐dynamics integration real‐time dust forecast |
| title | The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications |
| title_full | The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications |
| title_fullStr | The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications |
| title_full_unstemmed | The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications |
| title_short | The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications |
| title_sort | efficient integration of dust and numerical weather prediction for renewable energy applications |
| topic | NWP extreme dust storm solar energy high‐resolution dust forecast physics‐dynamics integration real‐time dust forecast |
| url | https://doi.org/10.1029/2024MS004525 |
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