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

Abstract. 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 38% on average for KED, aligning well with similar studies using rain gauges. However, the performance of merging methods varies with rainfall intensity, distance to observations, and network density. In terms of Pearson correlation coefficient (PCC), additive methods outperform KED in data-dense networks and during extreme rainfall events, while in data-sparse regions, KED provides more consistent adjustments, particularly at medium ranges (up to 15 km). At greater distances, additive methods again perform better by preserving radar variability. For RMSE and MAE, however, KED consistently outperforms additive methods across all settings. All merging methods reduced bias 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.