A major source of errors in radar-derived quantitative precipitation estimates is the inhomogeneous nature of the vertical reflectivity profile (VPR). Operational radars generally scan in azimuth at constant elevation (PPI mode) and provide limited VPR information, so predetermined VPR shapes with limited degrees of freedom are needed to correct for the VPR in real time. Typical stratiform VPRs have a sharp peak below the 0° isotherm, known as the “bright band,” caused by the presence of large melting snowflakes, but this feature is not present in convective cores where the melting ice is in the form of graupel or compact ice. Inappropriate correction assuming a brightband VPR can lead to underestimation of rain rates, with particular impacts in intense convective storms. This paper proposes the use of high values of linear depolarization ratio (LDR) measurements to confirm the presence of large melting snowflakes and lower values for melting graupel or high-density ice as a prerequisite to selecting a suitable profile shape for VPR correction. Using a climatologically representative dataset of short-range, high-resolution C-band vertical profiles, the peak value of the LDR in the melting layer is shown to have robust skill in identifying VPRs without bright band, with the “best” performance at a threshold of −20 dB. Further work is proposed to apply this result to improving corrections for VPR at longer range, where the limited effect of beam broadening on LDR peaks could provide advantages over other available methods.

The Met Office in the UK has developed a completely new probabilistic post-processing system called IMPROVER to operate on outputs from its operational Numerical Weather Prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders whilst better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous post-processing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on pre-defined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes/no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in Spring 2022.

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