Successful Precipitation Downscaling Through an Innovative Transformer-Based Model
In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept invol...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4292 |
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| author | Fan Yang Qiaolin Ye Kai Wang Le Sun |
| author_facet | Fan Yang Qiaolin Ye Kai Wang Le Sun |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. |
| format | Article |
| id | doaj-art-403ec25f067d4ee48929e7b6770bb9cf |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-403ec25f067d4ee48929e7b6770bb9cf2025-08-20T02:04:56ZengMDPI AGRemote Sensing2072-42922024-11-011622429210.3390/rs16224292Successful Precipitation Downscaling Through an Innovative Transformer-Based ModelFan Yang0Qiaolin Ye1Kai Wang2Le Sun3School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaNanjing NARl Information & Communication Technology Co., Ltd., Nanjing 211815, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaIn this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.https://www.mdpi.com/2072-4292/16/22/4292Transformer-basedsimulating high-resolution modelextreme precipitation weatherdownscaled spatiotemporal precipitation |
| spellingShingle | Fan Yang Qiaolin Ye Kai Wang Le Sun Successful Precipitation Downscaling Through an Innovative Transformer-Based Model Remote Sensing Transformer-based simulating high-resolution model extreme precipitation weather downscaled spatiotemporal precipitation |
| title | Successful Precipitation Downscaling Through an Innovative Transformer-Based Model |
| title_full | Successful Precipitation Downscaling Through an Innovative Transformer-Based Model |
| title_fullStr | Successful Precipitation Downscaling Through an Innovative Transformer-Based Model |
| title_full_unstemmed | Successful Precipitation Downscaling Through an Innovative Transformer-Based Model |
| title_short | Successful Precipitation Downscaling Through an Innovative Transformer-Based Model |
| title_sort | successful precipitation downscaling through an innovative transformer based model |
| topic | Transformer-based simulating high-resolution model extreme precipitation weather downscaled spatiotemporal precipitation |
| url | https://www.mdpi.com/2072-4292/16/22/4292 |
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