Abstract
Evolution of precipitation structures are simulated and compared with radar observations for the November Madden‐Julian Oscillation (MJO) event during the DYNAmics of the MJO (DYNAMO) field campaign. Three ground‐based, ship‐borne, and spaceborne precipitation radars and three cloud‐resolving models (CRMs) driven by observed large‐scale forcing are used to study precipitation structures at different locations over the central equatorial Indian Ocean. Convective strength is represented by 0‐dBZ echo‐top heights, and convective organization by contiguous 17‐dBZ areas. The multi‐radar and multi‐model framework allows for more stringent model validations. The emphasis is on testing models' ability to simulate subtle differences observed at different radar sites when the MJO event passed through. The results show that CRMs forced by site‐specific large‐scale forcing can reproduce not only common features in cloud populations but also subtle variations observed by different radars. The comparisons also revealed common deficiencies in CRM simulations where they underestimate radar echo‐top heights for the strongest convection within large, organized precipitation features. Cross validations with multiple radars and models also enable quantitative comparisons in CRM sensitivity studies using different large‐scale forcing, microphysical schemes and parameters, resolutions, and domain sizes. In terms of radar echo‐top height temporal variations, many model sensitivity tests have better correlations than radar/model comparisons, indicating robustness in model performance on this aspect. It is further shown that well‐validated model simulations could be used to constrain uncertainties in observed echo‐top heights when the low‐resolution surveillance scanning strategy is used.
Key Points
Multiradar observations allow stringent multimodel comparisons and validation for precipitation structures during an MJO event in the Indian Ocean
Cloud‐resolving models constrained by site‐specific‐observed large‐scale forcing can reproduce temporal and spatial variations of radar echo‐top heights
The multiradar, multimodel framework also benefits quantitative sensitivity studies to highlight common model strength and weakness
