| 26 Mar 2026
Machine Learning vs. Conventional Methods for X-Band Radar Rainfall Estimation in Cyprus
Abstract. Polarimetric X-band radars offer high-resolution precipitation observations that are often challenged by attenuation, calibration errors, and absence of routine correction procedures, which limit reliable quantitative precipitation estimation (QPE). This study proposes a two-stage machine learning framework for estimating near-surface rainfall from the Cyprus national X-band radar network. In the first stage (Stage 1), feedforward neural networks correct raw ground radar reflectivity using volume-matched Ku-band measurements from the Global Precipitation Measurement (GPM) Mission dual-frequency precipitation radar (DPR). In the second stage (Stage 2), the corrected reflectivity is used as input to regression models, including support vector regression (SVR) and neural networks, to estimate rainfall rates using tipping-bucket rain gauge data. Results show that the Stage 1 networks substantially improve ground radar reflectivity, while Stage 2 SVR models outperform traditional Z–R relationships in predicting rainfall, despite residual underestimation and moderate accuracy. The study highlights the potential of machine learning methods for X-band radar QPE in environments with limited calibration and emphasizes the benefit of combining multiple radar datasets to improve spatial consistency. These findings provide practical insights for enhancing rainfall estimation in Cyprus and other regions with similar radar network constraints.
Machine Learning vs. Conventional Methods for X-Band Radar Rainfall Estimation in Cyprus
The manuscript presents a two-step machine learning framework for estimating precipitation from X-band radar observations in Cyprus. In the first step, a neural network is used to correct ground-based reflectivities using DPR data, and in the second step, the corrected reflectivities are used to estimate precipitation rates.
While the general problem of inferring precipitation rates from ground-based X-band radar measurements is relevant for any region where they are deployed, the current study exhibits fundamental shortcomings in both methodology and results. Most importantly, the reported performance metrics raise serious concerns about the validity of the proposed approach and its implementation. The coefficients of determination (r^2) reported for one of the PFO radar from 0.078 to -67.66. Negative values of r2^2 indicate that the model performs substantially worse than a trivial baseline indicating either implementation or data errors. This strongly suggests that the model, in its current form, does not capture a meaningful relationship between radar observations and precipitation. In addition, the precipitation fields shown in Figures 6 and 10 appear to be dominated by artifacts rather than coherent precipitation structures. Taken together, these results do not provide evidence that the proposed method produces reliable precipitation estimates. Given these issues, I cannot recommend publication of the manuscript in its current form.
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