Correction of Systematic Calibration Drift in Weather Radar Observations to Improve Precipitation Uncertainty Modelling

Accurate precipitation estimates depend critically on the calibration fidelity of ground-based Doppler Weather Radar (DWR) systems. While these radars provide high-resolution observations essential for hydrological modelling and forecasting, their measurements often suffer from bias due to radar constant drift. Conventional calibration approaches, such as using metallic spheres, are operationally demanding and poorly maintained. As a result, biases in reflectivity can propagate, thereby degrading quantitative precipitation estimation (QPE) and introducing uncertainty into downstream applications.

This study develops a correction strategy that utilizes the well-calibrated reflectivity measurements from satellite radar (SR) to account for the systematic underestimation in ground radar (GR) measurements. A machine-learning approach based on the XGBoost algorithm is used to model the bias between GR and SR reflectivity along with key radar-geometric parameters, including range, elevation angle, and azimuth, to capture the spatial heterogeneity. The proposed framework is evaluated using eight years (2017-2024) of collocated observations from the C-band DWR at the Thumba Equatorial Rocket Launching Station (TERLS), Thiruvananthapuram, India. The proposed correction framework significantly enhances consistency between GR and SR observations. The correlation coefficient increases from 0.23 to 0.88 with a marked reduction in mean bias, mean absolute error and root mean squared error. The results demonstrate the potential of space-ground radar synergy to mitigate calibration-driven uncertainties and strengthen the reliability of near-real-time precipitation products. This framework offers a scalable pathway for enhancing operational QPE and for supporting climate-scale radar reflectivity reanalysis where long-term consistency is essential.