What was needed?
The ultimate aim of this project was to develop a localised and computationally simple radar adjustment methodology to improve radar accuracy for the real-time rainfall-based services provided by Meniscus across the UK, such as rainfall-sensitive cycling route optimisation.
How did we do it?
The re-calibration strategy was informed by a preliminary radar QPE (quantitative precipitation estimates) assessment which entailed comparison against ground rain gauge records for selected winter and summer storm events. This assessment revealed a tendency of radar QPEs to underestimate high rainfall rates, such as those observed during convective events. Moreover, the discrepancies between radar and rain gauge records were found to be non-linear and to vary across events, rendering a simple 1st statistical order model, such as mean field bias correction, insufficient for adjusting radar records.
In light of this and considering application requirements and operational constraints, different radar re-calibration strategies were tested, including dynamic and static Z-R (radar reflectivity to rain rate) function re-calibration. Both re-calibration strategies were shown to improve the quality of radar QPEs, with the dynamic adjustment generally leading to best results. However, dynamic re-calibration is conditional on good quality rain gauge records being available in near real-time. The two strategies can be used inter-changeably, depending on operational conditions.