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| Created: | Oct 02, 2019 at 2:17 a.m. | |
| Last updated: | Dec 03, 2020 at 7:19 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 1687 |
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Abstract
Precipitation estimation at a global scale is essential for global water cycle simulation and water resources management. The precipitation estimation from gauge-based, satellite retrieval and reanalysis datasets have heterogeneous uncertainties for different areas at global land. Here, the 13 monthly precipitation datasets and the 11 daily precipitation datasets are analyzed to examine the relative uncertainty of individual data based on the developed generalized three-cornered hat (TCH) method. The generalized TCH method can be used to evaluate the uncertainty of multiple (>3) precipitation products in an iterative optimization process. A weighting scheme is designed to merge the individual precipitation datasets to generate a new weighted precipitation using the inverse error variance-covariance matrix of TCH estimated uncertainty. The weighted precipitation is then validated using gauged data with the minimal uncertainty among all the individual products. The merged results indicate the superiority of the weighted precipitation with substantially reduced random errors over individual datasets and a state-of-the-art multi-satellite merged product, i.e. the Integrated Multi-satellitE Retrievals for Global precipitation measurement (IMERG) at validated areas. The weighted dataset can largely reproduce the interannual and seasonal variations of regional precipitation. The TCH-based merging results outperform two other mean-based merging methods at both monthly and daily scales. Overall, the merging scheme based on the generalized TCH method is effective to produce a new precipitation dataset integrating information from multiple products for hydrometeorological applications.
Subject Keywords
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Temporal
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Related Resources
| This resource is referenced by | Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets |
| The content of this resource is derived from | Xu Lei et al. (2020) Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets. doi://10.1029/2019WR026444 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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