The Generalized Likelihood Uncertainty Estimation methodology (GLUE) is investigated for radar rainfall calibration and uncertainty assessment. The method is used to calibrate radar data collected by a Local Area Weather Radar (LAWR).

In contrast to other LAWR data calibrations, the method combines calibration with uncertainty estimation. Instead of searching for a single set of calibration parameters, the method uses the observations to construct distributions of the calibration parameters. These parameter sets provide valuable knowledge of parameter sensitivity and the uncertainty.

Two approaches are analyzed; the static calibration approach, where the LAWR is calibrated once for a long period and the dynamic approach, where the estimate is continuously adjusted based on ground observations.

The analysis illustrates that the static calibration performs insufficiently, whereas the dynamic adjustment improves the performance significantly.

It is found that even if the dynamic adjustment method is used the uncertainty of rainfall estimates can still be significant.