SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore
| Autor: | Xiang-Yu Huang, Dale Barker, Stuart Webster, Anurag Dipankar, Adrian Lock, Marion Mittermaier, Xiangming Sun, Rachel North, Rob Darvell, Douglas Boyd, Jeff Lo, Jianyu Liu, Bruce Macpherson, Peter Heng, Adam Maycock, Laura Pitcher, Robert Tubbs, Martin McMillan, Sijin Zhang, Susanna Hagelin, Aurore Porson, Guiting Song, Becky Beckett, Wee Kiong Cheong, Allison Semple, Chris Gordon |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ASEAN Journal on Science and Technology for Development, Vol 36, Iss 3, Pp 81–90-81–90 (2019) |
| Druh dokumentu: | article |
| ISSN: | 0217-5460 2224-9028 |
| DOI: | 10.29037/ajstd.581 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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