Statistical uncertainty analysis-based precipitation merging (SUPER)
framework and its data product
Overview
Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. We present a new uncertainty analysis-based precipitation merging framework (SUPER) for improved global precipitation estimation.
- Additional information is referred to: Dong, J., Crow, W. T., Chen, X., Tangdamrongsub, N., Gao, M., Sun, S., Qiu, J., Wei, L., Gao, H., & Duan, Z. (2022). Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation Remote Sensing of Environment, 283, 113299.
- https://doi.org/10.1016/j.rse.2022.113299
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The merging data of SUPER and code are released under the CC BY-NC 4.0 license and thus may not be used for commercial purposes. Please contact us if you are affiliated with a commercial entity and want to trial SUPER. If you do not have a commercial affiliation and you intend to use the product for non-commercial purposes, please send us a request using the following form. You will receive a link to SUPER data once your request has been approved. By using the dataset in any publication, you agree to cite the corresponding paper.