PreciPatch: A Dictionary-based Precipitation Downscaling Method
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for lo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/6/1030 |
| _version_ | 1818941280491667456 |
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| author | Mengchao Xu Qian Liu Dexuan Sha Manzhu Yu Daniel Q. Duffy William M Putman Mark Carroll Tsengdar Lee Chaowei Yang |
| author_facet | Mengchao Xu Qian Liu Dexuan Sha Manzhu Yu Daniel Q. Duffy William M Putman Mark Carroll Tsengdar Lee Chaowei Yang |
| author_sort | Mengchao Xu |
| collection | DOAJ |
| description | Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018). |
| first_indexed | 2024-12-20T06:53:02Z |
| format | Article |
| id | doaj.art-e916dd2d23c04e2ca21634ab7f560a89 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T06:53:02Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e916dd2d23c04e2ca21634ab7f560a892022-12-21T19:49:28ZengMDPI AGRemote Sensing2072-42922020-03-01126103010.3390/rs12061030rs12061030PreciPatch: A Dictionary-based Precipitation Downscaling MethodMengchao Xu0Qian Liu1Dexuan Sha2Manzhu Yu3Daniel Q. Duffy4William M Putman5Mark Carroll6Tsengdar Lee7Chaowei Yang8NSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USANSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USANSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USANSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USANASA Center for Climate Simulation, Greenbelt, MD 20771, USANASA Center for Climate Simulation, Greenbelt, MD 20771, USANASA Center for Climate Simulation, Greenbelt, MD 20771, USANASA Headquarters, Washington, DC 20546, USANSF Spatiotemporal Innovation Center, George Mason Univ., Fairfax, VA 22030, USAClimate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).https://www.mdpi.com/2072-4292/12/6/1030geoainatural disasterfloodingglobal water cyclespatiotemporal data analytics |
| spellingShingle | Mengchao Xu Qian Liu Dexuan Sha Manzhu Yu Daniel Q. Duffy William M Putman Mark Carroll Tsengdar Lee Chaowei Yang PreciPatch: A Dictionary-based Precipitation Downscaling Method Remote Sensing geoai natural disaster flooding global water cycle spatiotemporal data analytics |
| title | PreciPatch: A Dictionary-based Precipitation Downscaling Method |
| title_full | PreciPatch: A Dictionary-based Precipitation Downscaling Method |
| title_fullStr | PreciPatch: A Dictionary-based Precipitation Downscaling Method |
| title_full_unstemmed | PreciPatch: A Dictionary-based Precipitation Downscaling Method |
| title_short | PreciPatch: A Dictionary-based Precipitation Downscaling Method |
| title_sort | precipatch a dictionary based precipitation downscaling method |
| topic | geoai natural disaster flooding global water cycle spatiotemporal data analytics |
| url | https://www.mdpi.com/2072-4292/12/6/1030 |
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