Dawn Harrison

For a one-month period in summer 2020, a prototype Vaisala broadband differential absorption lidar (BB-DIAL) was deployed at a Met Office research site. It was compared with in-situ observations of humidity (93 radiosonde ascents and 27 of uncrewed aerial vehicle flights) and the Met Office 1.5 km resolution numerical weather prediction (NWP) model: UK Variable resolution model (UKV). The BB-DIAL was able to collect data up to the cloud base, in all-weather situations including rain, when it was possible to reach 3 km. The average maximum height was 1300 m, with 75% of the data reaching 1000 m and 35% extending to 1500 m. Compared with radiosondes, the standard deviation for the water vapour is between 5% and 10%. The comparison with the UKV is very encouraging, with a correlation of 0.90. The error against the radiosonde is smaller than against the UKV, which is encouraging for assimilation the BB-DIAL data in UKV. Some data quality issues, such as an increase in error and variable bias in the region of overlap between the far field and close field, spurious oscillations and an unrealistic dry layer above fog are identified. Despite these issues, the overall results from this assessment are promising in terms of potential benefit, instrument reliability and capturing significant humidity changes in the boundary layer.

Quality is key to ensuring that the potential offered by weather radar networks is realized. To achieve optimum quality, a comprehensive radar data quality management system, designed to monitor the end-to-end radar data processing chain and evaluate product quality, is being developed at the UK Met Office. Three contrasting elements of this system are described: monitoring of key radar hardware performance indicators; generation of long-term integrations of radar products; and monitoring of radar reflectivity factor using synthesized observations from numerical weather prediction model fields. Examples of each component are presented and ways in which the different types of monitoring information have been used to both identify issues with the radar product data quality and help formulate solutions are given.