Radar reflectivity assimilation using hourly cycling 4D‐Var in the Met Office Unified Model

• Published in: Quarterly journal of the Royal Meteorological Society 2021-04, Vol.147 (736), p.1516-1538
• Main Authors: Hawkness‐Smith, L. D., Simonin, D.
• Format: Article
• Language: English
• Subjects: assimilation; Atmospheric precipitations; Convective precipitation; convective‐scale; Microphysics; Nowcasting; NWP; Precipitation; Quality control; Radar; Radar data; Radar reflectivity; Rain; Rainfall forecasting; Reflectance; reflectivity; Stability; Weather forecasting
• ISSN: 0035-9009; 1477-870X
• DOI: 10.1002/qj.3977

A new method has been developed to directly assimilate volume scans of radar reflectivity data with 4D‐Var in the Met Office Unified Model. The method has been demonstrated in the convective‐scale hourly‐cycling UKV forecast model. Reflectivity observations from 18 C‐band radars in the British Isles are assimilated. This article describes the method of observation processing and quality control, the observation operator, and assimilation method. The assimilation method uses a minimum threshold rainwater content in the forward operator to give sensitivity to reflectivity observations where there is no rain in the background. Furthermore, the use of the Huber norm in the observation penalty function allows the use of observations with large innovations in the assimilation. A change was made to the precipitation efficiency in the microphysics scheme of the linear perturbation forecast model to ensure stability of the scheme. A case‐study is presented which demonstrates how the inclusion of reflectivity observations enhances convergence through analysis increments to the wind field, leading to improvements to the location of convective precipitation features in the forecast. Two‐month trials for summer and winter seasons demonstrate significant improvements to rain forecasts in the nowcasting range. A scheme for the direct assimilation of radar reflectivity observations in 4D‐Var has been developed for the Met Office convective‐scale model. Information from reflectivity observations can improve mesoscale dynamics, for example enhancing convergence in the wind increments as shown in the image. This leads to improved precipitation forecasts in the nowcasting range.