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.

Aircraft play an important role in providing in‐situ upper air observations for use in numerical weather prediction (NWP) models. Aircraft observations of wind, temperature and humidity have been routinely assimilated into NWP models for decades. More recently, the Met Office has installed a network of six receivers to collect Mode‐Select Enhanced Surveillance (Mode‐S EHS) broadcasts and derive wind and temperature information. The paper focuses on the assimilation of high‐frequency (sub‐hourly) Mode‐S winds into the Met Office's hourly cycling high‐resolution NWP model, which employs four‐dimensional‐variational data assimilation (4D‐Var). An observing system experiment (OSE) experiment was carried out to assess the impact of additionally assimilating Mode‐S wind data on the model forecasts in both summer and winter. It is shown that assimilating Mode‐S winds has a positive benefit on the forecast skill in the first 6 hr wind profiles, with an improvement of the mean bias and root mean square (RMS) errors of up to 0.5 m·s⁻¹ in the first hour and 0.3 m·s⁻¹ in the sixth hour. The assimilation of Mode‐S winds is also seen to be beneficial to the near‐surface‐focused UK NWP composite index, and to the hourly precipitation accumulations in fractions skill score (FSS) for forecasts up to 9 hr ahead.

A high‐resolution demonstration Numerical Weather Prediction (NWP) nowcasting system was developed and run in real time at the Met Office for the prediction of precipitation on a domain covering southern England and Wales. The hourly cycling nowcasting system, known as NDP (Nowcasting Demonstration Project), combined a 3 km resolution 4D‐Variational data assimilation (4D‐Var) and a 1.5 km resolution version of the Met Office Unified Model (UM) to provide hourly NWP analyses and forecasts for a period of 0–6 h. Central to the NDP was the rapid updating cycling and its timely delivery of analyses and forecasts using the latest conventional and sub‐hourly novel observations. In this article the benefits of using 4D‐Var assimilation compared to First Guess at Appropriate Time (FGAT) 3D‐Variational assimilation (3D‐Var) on forecasts of precipitation are considered by comparing model forecasts with radar‐derived hourly surface accumulations for the period of June 2012, using an objective, scale‐dependent verification scheme. It is shown that 4D‐Var assimilation has a positive impact on precipitation forecasting skills compared to the corresponding 3D‐Var assimilation for the whole nowcast period [T + 0, T + 6]. The 4D‐Var assimilation system produced forecasts with greater spatial accuracy and longer lead times with acceptable skill. By comparing 3D‐Var FGAT using sub‐hourly observations with 3D‐Var FGAT using only the observations closest to the analysis time, the sub‐hourly observations are seen to be beneficial in the hourly cycling convective‐scale system in both 3D‐Var FGAT and 4D‐Var. The forecast clearly benefitted from the use of extra, higher time frequency observations in both 3D‐Var and 4D‐Var.