Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees
Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the enti...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/10/1489 |
| _version_ | 1797574807800250368 |
|---|---|
| author | Shaonan Zhu Xiangyuan Wang Donglai Jiao Yiding Zhang Jiaxin Liu |
| author_facet | Shaonan Zhu Xiangyuan Wang Donglai Jiao Yiding Zhang Jiaxin Liu |
| author_sort | Shaonan Zhu |
| collection | DOAJ |
| description | Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model’s capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions. |
| first_indexed | 2024-03-10T21:27:23Z |
| format | Article |
| id | doaj.art-53512ebd18d246e197df632fe434cb1b |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T21:27:23Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-53512ebd18d246e197df632fe434cb1b2023-11-19T15:35:46ZengMDPI AGAtmosphere2073-44332023-09-011410148910.3390/atmos14101489Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random TreesShaonan Zhu0Xiangyuan Wang1Donglai Jiao2Yiding Zhang3Jiaxin Liu4School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunication, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunication, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunication, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunication, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunication, Nanjing 210023, ChinaObtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model’s capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions.https://www.mdpi.com/2073-4433/14/10/1489spatial downscalingprecipitationvegetation regionsGPM |
| spellingShingle | Shaonan Zhu Xiangyuan Wang Donglai Jiao Yiding Zhang Jiaxin Liu Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees Atmosphere spatial downscaling precipitation vegetation regions GPM |
| title | Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees |
| title_full | Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees |
| title_fullStr | Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees |
| title_full_unstemmed | Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees |
| title_short | Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees |
| title_sort | spatial downscaling of gpm satellite precipitation data using extreme random trees |
| topic | spatial downscaling precipitation vegetation regions GPM |
| url | https://www.mdpi.com/2073-4433/14/10/1489 |
| work_keys_str_mv | AT shaonanzhu spatialdownscalingofgpmsatelliteprecipitationdatausingextremerandomtrees AT xiangyuanwang spatialdownscalingofgpmsatelliteprecipitationdatausingextremerandomtrees AT donglaijiao spatialdownscalingofgpmsatelliteprecipitationdatausingextremerandomtrees AT yidingzhang spatialdownscalingofgpmsatelliteprecipitationdatausingextremerandomtrees AT jiaxinliu spatialdownscalingofgpmsatelliteprecipitationdatausingextremerandomtrees |