A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates
Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coar...
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doaj-d86e5e7458eb46679c16e28409be0f6d2021-07-23T14:04:12ZengMDPI AGRemote Sensing2072-42922021-07-01132693269310.3390/rs13142693A New HASM-Based Downscaling Method for High-Resolution Precipitation EstimatesNa Zhao0Yimeng Jiao1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaObtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R<sup>2</sup> values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R<sup>2</sup>, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.https://www.mdpi.com/2072-4292/13/14/2693satellite precipitation estimatesdownscalingIMERG |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Na Zhao Yimeng Jiao |
spellingShingle |
Na Zhao Yimeng Jiao A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates Remote Sensing satellite precipitation estimates downscaling IMERG |
author_facet |
Na Zhao Yimeng Jiao |
author_sort |
Na Zhao |
title |
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates |
title_short |
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates |
title_full |
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates |
title_fullStr |
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates |
title_full_unstemmed |
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates |
title_sort |
new hasm-based downscaling method for high-resolution precipitation estimates |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-07-01 |
description |
Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R<sup>2</sup> values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R<sup>2</sup>, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales. |
topic |
satellite precipitation estimates downscaling IMERG |
url |
https://www.mdpi.com/2072-4292/13/14/2693 |
work_keys_str_mv |
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