Assessing the impact of a NWP warm‐start system on model spin‐up over tropical Africa. (10th February 2023)

• Record Type: Journal Article
• Authors: Warner, James L.; Petch, Jon; Short, Chris J.; Bain, Caroline
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 149:Number 751(2023)
• Page Start: 621
• Page End: 636
• Publication Date: 2023-02-10
• Subjects: convection‐permitting -- DA -- numerical weather prediction -- NWP -- spin‐up -- tropical Africa; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.4429
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: Convection‐permitting (CP) models have provided a step change in the ability to forecast impactful convective storms, which pose risks such as flash flooding and lightning. Despite CP models being routinely run over tropical Africa, they typically lack data assimilation and are initialised directly from a global analysis. The CP model therefore takes time to generate convective structures and consequently precipitation, rendering the early part of the forecast unusable. A 'warm‐starting' method developed within the Met Office Unified Model is tested over a large tropical Africa domain, leading to substantial improvements in rainfall predictions during both the spin‐up period but also later in the forecast. The spin‐up period is found to vary considerably, both geographically and with model initialisation time, due to the differing states of the atmosphere through the day. We quantify this impact through a spin‐up diagnostic, which shows that over central Africa the spin‐up period can be as large as 24 hr. In these regions, the warm starting substantially reduces spin‐up time. Additionally, the large tropical Africa domain allows us to examine the impact of the prevailing meteorology on both the spin‐up period and the warm‐start method. We find large‐scale positive vorticity filaments over West Africa, likely tied to African Easterly Waves, increase rainfall rates across all models. The substantial reduction in the spin‐up of rainfall and increased skill at longer lead times in the warm‐start demonstrate the importance of representing convective features in the analysis for a CP model. This is especially important in large tropical domains where the initial conditions are likely to have a lasting impact on the forecast. Improvements in model performance across the forecast will enhance the ability of forecasters to provide advice on high‐impact weather events, supporting increased resilience against weather extremes in Africa. Abstract : A method that involves blending both regional and global model data together is applied to forecasts over tropical Africa. Rainfall forecasts are shown to improve substantially, particularly during the first few hours of the forecast but also later into the forecast.