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SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore
Author(s) -
Heng B. C. Peter,
Tubbs Robert,
Huang XiangYu,
Macpherson Bruce,
Barker Dale M.,
Boyd Douglas F. A.,
Kelly Graeme,
North Rachel,
Stewart Laura,
Webster Stuart,
Wlasak Marek
Publication year - 2020
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3774
Subject(s) - nowcasting , data assimilation , numerical weather prediction , meteorology , quantitative precipitation forecast , environmental science , weather research and forecasting model , north american mesoscale model , climatology , precipitation , spurious relationship , global forecast system , weather forecasting , bootstrapping (finance) , scale (ratio) , radiance , convection , geography , remote sensing , econometrics , mathematics , geology , statistics , cartography
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.