Advance: Amplifying Data Validation and Analysis Through Time-Varying Quadruple Collocation for Enhanced Precipitation Error Estimation and Integration
SSRN, ISSN: 1556-5068
2023
- 91Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Understanding the uncertainties associated with precipitation estimates is crucial for ensuring the accuracy and reliability of data analysis and applications, including data fusion. In recent years, there has been significant progress in estimating uncertainties within existing datasets. A notable development is the Quadruple Collocation (QC) approach, which does not rely on in-situ observations as ground truth for determining source uncertainties and also recognizes error cross-correlation between datasets. Despite these considerable advancements in identifying data errors, collocation approaches still rely on the assumption that errors remain static over time. To address this limitation, this study proposes the time-varying quadruple collocation (TV-QC) approach to enhance the estimation of precipitation uncertainties and facilitate more effective data fusion. In pursuit of this objective, we begin by comparing the performance of time-variant errors (TVE) derived from TV-QC with time-invariant errors (TIE) derived from the conventional QC, and we then assess them against gauge-based errors to identify their unique characteristics. Subsequently, we utilize the errors obtained from TV-QC for data merging. Our findings highlight that TVEs not only differ significantly from their time-invariant counterparts but also display notably stronger correlations with ground-based errors. Furthermore, our analysis reveals differences between the performances of the individual products. GPM consistently exhibits superior accuracy, while the PERSIANN and ERA5 datasets consistently exhibit weaker and less consistent performance across diverse seasons and climate zones. Additionally, utilizing TV-QC for data fusion leads to the development of a new precipitation product for the Philippines, “PHIL-ADVANCE”, that surpasses existing gridded products. Overall, the integration of time-varying collocation and the QC technique enhances our understanding of precipitation uncertainties and facilitates improved data fusion.
Bibliographic Details
Elsevier BV
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