Continuous Radar QPE Bias Field Reconstruction Through the Implicit Neural Representation Framework: QPE-BiasINR

Biases in high-spatiotemporal-resolution radar quantitative precipitation estimation (QPE) can substantially compromise the reliability of meteorological and hydrological disaster warning. Existing QPE correction methods commonly assume predefined spatial smoothness, whereas localized biases can vary sharply over short spatial distances. We therefore propose an implicit neural representation (INR) framework for continuous radar QPE bias-field reconstruction (QPE-BiasINR). QPE-BiasINR introduces QPE-background modulation, which conditions bias estimation on the local precipitation context to account for precipitation-dependent spatial variations in radar QPE errors. Moreover, a multiscale spatial consistency (MSC) constraint is incorporated into the loss function to explicitly strengthen spatial consistency. QPE-BiasINR was evaluated using hourly multi-radar mosaic QPE and gauge observations within the coverage of four radars in the Daqing River Basin,China, during June–September 2023. Test set performance was benchmarked against the uncorrected radar QPE, mean-field bias correction, and Kriging. QPE-BiasINR achieved the highest correlation coefficient (0.84) and the lowest root mean square error (3.29 mm) and mean absolute error (1.68 mm). Ablation experiments on QPE-BiasINR demonstrated that QPE-background modulation improved accuracy, while the MSC constraint reduced localized overcorrection. These results indicate that QPE-BiasINR can improve high-resolution radar QPE, with potential value for hydrological modeling and operational flood-risk monitoring.