Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China
Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation est...
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| Veröffentlicht in: | Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (24), p.4703 |
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| description | Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias. |
| doi_str_mv | 10.3390/rs16244703 |
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This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. 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| subjects | Accuracy Algorithms Carbon cycle Carbon sources Climate change Correlation coefficient Correlation coefficients Datasets Drought Emergency preparedness Error analysis Estimates Hydrologic data Hydrology least squares merging Precipitation precipitation estimates Rain Rainfall intensity Satellites Temporal variations triple collocation |
| title | Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China |
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