Please use this identifier to cite or link to this item: https://doi.org/10.29037/ajstd.581
Title: SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore
Authors: Huang, Xiang-Yu
Barker, Dale
Webster, Stuart
Dipankar, Anurag
Lock, Adrian 
Mittermaier, Marion
Sun, Xiangming 
North, Rachel
Darvell, Rob
Boyd, Douglas
Lo, Jeff
Liu, Jianyu
Macpherson, Bruce
Heng, Peter
Maycock, Adam
Pitcher, Laura
Tubbs, Robert
McMillan, Martin
Zhang, Sijin
Hagelin, Susanna
Porson, Aurore
Song, Guiting
Beckett, Becky
Cheong, Wee Kiong
Semple, Allison
Gordon, Chris
Issue Date: 2019
Publisher: UGM Press
Citation: Huang, Xiang-Yu, Barker, Dale, Webster, Stuart, Dipankar, Anurag, Lock, Adrian, Mittermaier, Marion, Sun, Xiangming, North, Rachel, Darvell, Rob, Boyd, Douglas, Lo, Jeff, Liu, Jianyu, Macpherson, Bruce, Heng, Peter, Maycock, Adam, Pitcher, Laura, Tubbs, Robert, McMillan, Martin, Zhang, Sijin, Hagelin, Susanna, Porson, Aurore, Song, Guiting, Beckett, Becky, Cheong, Wee Kiong, Semple, Allison, Gordon, Chris (2019). SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore. ASEAN Journal on Science and Technology for Development 36 (3). ScholarBank@NUS Repository. https://doi.org/10.29037/ajstd.581
Abstract: Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.
Source Title: ASEAN Journal on Science and Technology for Development
URI: https://scholarbank.nus.edu.sg/handle/10635/227962
ISSN: 02175460
22249028
DOI: 10.29037/ajstd.581
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