Improving weather radar rainfall estimates by merging with commercial microwave link data: a fully reproducible, large-scale method intercomparison

Accurate rainfall estimation is essential for hydrometeorological applications, but capturing the fine spatiotemporal variability of rainfall remains challenging. In this study, we assess the impact of merging commercial microwave link (CML) data with weather radar for quantitative precipitation estimation (QPE) using two openly available datasets with contrasting observational densities. We compare multiple merging methods, including kriging with external drift (KED), and derive a block kriging interpolation method to account for the line-average nature of CMLs. The results show that merging CML data improves radar QPE, with reductions in mean absolute error (MAE) of up to 41 % for multiplicative merging and up to 38 % for KED, aligning well with similar studies using rain gauges. Performance depends on rainfall intensity, distance, and network density: in sparse networks, multiplicative methods yield the lowest RMSE and MAE, indicating that radar errors are dominated by scaling errors, though KED and additive methods maintain slightly higher spatial correlation (PCC) over distance. In dense networks, all methods perform comparably. Overall, additive methods perform best at capturing extremes. Despite accounting for line-averaging, block kriging performed similarly to point-based kriging. All merging methods reduced RMSE and MAE compared to unadjusted radar fields. The merging framework and intercomparison study are openly available, enabling reproducibility and further exploration by the scientific community.