SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore. (31st March 2020)

• Record Type: Journal Article
• Authors: Heng, B. C. Peter; Tubbs, Robert; Huang, Xiang‐Yu; Macpherson, Bruce; Barker, Dale M.; Boyd, Douglas F. A.; Kelly, Graeme; North, Rachel; Stewart, Laura; Webster, Stuart; Wlasak, Marek
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 146:Number 729(2020)
• Page Start: 1923
• Page End: 1938
• Publication Date: 2020-03-31
• Subjects: background error covariances -- convective‐scale -- data assimilation -- numerical weather prediction -- observations -- Singapore; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3774
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: SINGV‐DA is a convective‐scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV‐DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV‐DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system. Abstract : The assimilation of satellite radiances, which outnumber all other observations ingested by SINGV‐DA, had the largest positive impact on precipitation forecasts. SINGV‐DA forecasts were also sensitive to background error covariances, horizontal resolution and lateral boundary conditions.