How much improvement can precipitation data fusion achieve with a Multiscale Kalman Smoother-based framework?

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Abstract

[1] With advancements in measuring techniques and modeling approaches, more and more precipitation data products, with different spatial resolutions and accuracies, become available. Therefore, there is an increasing need to produce a fused precipitation product that can take advantage of the strengths of each individual precipitation data product. This study systematically and quantitatively evaluates the improvements of the fused precipitation data as a result of using the Mulitscale Kalman Smoother-based (i.e., MKS-based) framework. Impacts of two types of errors, i.e., white noise and bias that are associated with individual precipitation products, are investigated through hypothetical experiments. Two measures, correlation and root-mean-square error, are used to evaluate the improvements of the fused precipitation data. Our study shows that the MKS-based framework can significantly recover the loss of precipitation's spatial patterns and magnitudes that are associated with the white noise and bias when the erroneous data at different spatial scales are fused together. Although the erroneous data at a finer resolution are generally more effective in improving the spatial patterns and magnitudes of the erroneous data at a coarser resolution, data at a coarser resolution can also provide valuable information in improving the quality of the data at a finer resolution when they are fused. This study provides insights on the values of the MKS-based framework and a guideline for determining a potentially optimal spatial scale over which improvements in both the spatial patterns and the magnitudes can be maximized based on given data with different spatial resolutions.

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