SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

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 co...
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Autor*in:	Xiang-Yu Huang [verfasserIn] Dale Barker [verfasserIn] Stuart Webster [verfasserIn] Anurag Dipankar [verfasserIn] Adrian Lock [verfasserIn] Marion Mittermaier [verfasserIn] Xiangming Sun [verfasserIn] Rachel North [verfasserIn] Rob Darvell [verfasserIn] Douglas Boyd [verfasserIn] Jeff Lo [verfasserIn] Jianyu Liu [verfasserIn] Bruce Macpherson [verfasserIn] Peter Heng [verfasserIn] Adam Maycock [verfasserIn] Laura Pitcher [verfasserIn] Robert Tubbs [verfasserIn] Martin McMillan [verfasserIn] Sijin Zhang [verfasserIn] Susanna Hagelin [verfasserIn] Aurore Porson [verfasserIn] Guiting Song [verfasserIn] Becky Beckett [verfasserIn] Wee Kiong Cheong [verfasserIn] Allison Semple [verfasserIn] Chris Gordon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics

Übergeordnetes Werk:	In: ASEAN Journal on Science and Technology for Development - Universiti Brunei Darussalam, 2018, 36(2019), 3, Seite 81–90-81–90
Übergeordnetes Werk:	volume:36 ; year:2019 ; number:3 ; pages:81–90-81–90

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.29037/ajstd.581

Katalog-ID:	DOAJ061382868

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allfields	10.29037/ajstd.581 doi (DE-627)DOAJ061382868 (DE-599)DOAJ5b0b1675ee7f4c0597f1e8b6eeeb727c DE-627 ger DE-627 rakwb eng T1-995 Q1-390 Xiang-Yu Huang verfasserin aut SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General) Dale Barker verfasserin aut Stuart Webster verfasserin aut Anurag Dipankar verfasserin aut Adrian Lock verfasserin aut Marion Mittermaier verfasserin aut Xiangming Sun verfasserin aut Rachel North verfasserin aut Rob Darvell verfasserin aut Douglas Boyd verfasserin aut Jeff Lo verfasserin aut Jianyu Liu verfasserin aut Bruce Macpherson verfasserin aut Peter Heng verfasserin aut Adam Maycock verfasserin aut Laura Pitcher verfasserin aut Robert Tubbs verfasserin aut Martin McMillan verfasserin aut Sijin Zhang verfasserin aut Susanna Hagelin verfasserin aut Aurore Porson verfasserin aut Guiting Song verfasserin aut Becky Beckett verfasserin aut Wee Kiong Cheong verfasserin aut Allison Semple verfasserin aut Chris Gordon verfasserin aut In ASEAN Journal on Science and Technology for Development Universiti Brunei Darussalam, 2018 36(2019), 3, Seite 81–90-81–90 (DE-627)558389791 (DE-600)2409270-8 22249028 nnns volume:36 year:2019 number:3 pages:81–90-81–90 https://doi.org/10.29037/ajstd.581 kostenfrei https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c kostenfrei https://ajstd.org/index.php/ajstd/article/view/581 kostenfrei https://doaj.org/toc/0217-5460 Journal toc kostenfrei https://doaj.org/toc/2224-9028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2019 3 81–90-81–90
spelling	10.29037/ajstd.581 doi (DE-627)DOAJ061382868 (DE-599)DOAJ5b0b1675ee7f4c0597f1e8b6eeeb727c DE-627 ger DE-627 rakwb eng T1-995 Q1-390 Xiang-Yu Huang verfasserin aut SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General) Dale Barker verfasserin aut Stuart Webster verfasserin aut Anurag Dipankar verfasserin aut Adrian Lock verfasserin aut Marion Mittermaier verfasserin aut Xiangming Sun verfasserin aut Rachel North verfasserin aut Rob Darvell verfasserin aut Douglas Boyd verfasserin aut Jeff Lo verfasserin aut Jianyu Liu verfasserin aut Bruce Macpherson verfasserin aut Peter Heng verfasserin aut Adam Maycock verfasserin aut Laura Pitcher verfasserin aut Robert Tubbs verfasserin aut Martin McMillan verfasserin aut Sijin Zhang verfasserin aut Susanna Hagelin verfasserin aut Aurore Porson verfasserin aut Guiting Song verfasserin aut Becky Beckett verfasserin aut Wee Kiong Cheong verfasserin aut Allison Semple verfasserin aut Chris Gordon verfasserin aut In ASEAN Journal on Science and Technology for Development Universiti Brunei Darussalam, 2018 36(2019), 3, Seite 81–90-81–90 (DE-627)558389791 (DE-600)2409270-8 22249028 nnns volume:36 year:2019 number:3 pages:81–90-81–90 https://doi.org/10.29037/ajstd.581 kostenfrei https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c kostenfrei https://ajstd.org/index.php/ajstd/article/view/581 kostenfrei https://doaj.org/toc/0217-5460 Journal toc kostenfrei https://doaj.org/toc/2224-9028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2019 3 81–90-81–90
allfields_unstemmed	10.29037/ajstd.581 doi (DE-627)DOAJ061382868 (DE-599)DOAJ5b0b1675ee7f4c0597f1e8b6eeeb727c DE-627 ger DE-627 rakwb eng T1-995 Q1-390 Xiang-Yu Huang verfasserin aut SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General) Dale Barker verfasserin aut Stuart Webster verfasserin aut Anurag Dipankar verfasserin aut Adrian Lock verfasserin aut Marion Mittermaier verfasserin aut Xiangming Sun verfasserin aut Rachel North verfasserin aut Rob Darvell verfasserin aut Douglas Boyd verfasserin aut Jeff Lo verfasserin aut Jianyu Liu verfasserin aut Bruce Macpherson verfasserin aut Peter Heng verfasserin aut Adam Maycock verfasserin aut Laura Pitcher verfasserin aut Robert Tubbs verfasserin aut Martin McMillan verfasserin aut Sijin Zhang verfasserin aut Susanna Hagelin verfasserin aut Aurore Porson verfasserin aut Guiting Song verfasserin aut Becky Beckett verfasserin aut Wee Kiong Cheong verfasserin aut Allison Semple verfasserin aut Chris Gordon verfasserin aut In ASEAN Journal on Science and Technology for Development Universiti Brunei Darussalam, 2018 36(2019), 3, Seite 81–90-81–90 (DE-627)558389791 (DE-600)2409270-8 22249028 nnns volume:36 year:2019 number:3 pages:81–90-81–90 https://doi.org/10.29037/ajstd.581 kostenfrei https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c kostenfrei https://ajstd.org/index.php/ajstd/article/view/581 kostenfrei https://doaj.org/toc/0217-5460 Journal toc kostenfrei https://doaj.org/toc/2224-9028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2019 3 81–90-81–90
allfieldsGer	10.29037/ajstd.581 doi (DE-627)DOAJ061382868 (DE-599)DOAJ5b0b1675ee7f4c0597f1e8b6eeeb727c DE-627 ger DE-627 rakwb eng T1-995 Q1-390 Xiang-Yu Huang verfasserin aut SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General) Dale Barker verfasserin aut Stuart Webster verfasserin aut Anurag Dipankar verfasserin aut Adrian Lock verfasserin aut Marion Mittermaier verfasserin aut Xiangming Sun verfasserin aut Rachel North verfasserin aut Rob Darvell verfasserin aut Douglas Boyd verfasserin aut Jeff Lo verfasserin aut Jianyu Liu verfasserin aut Bruce Macpherson verfasserin aut Peter Heng verfasserin aut Adam Maycock verfasserin aut Laura Pitcher verfasserin aut Robert Tubbs verfasserin aut Martin McMillan verfasserin aut Sijin Zhang verfasserin aut Susanna Hagelin verfasserin aut Aurore Porson verfasserin aut Guiting Song verfasserin aut Becky Beckett verfasserin aut Wee Kiong Cheong verfasserin aut Allison Semple verfasserin aut Chris Gordon verfasserin aut In ASEAN Journal on Science and Technology for Development Universiti Brunei Darussalam, 2018 36(2019), 3, Seite 81–90-81–90 (DE-627)558389791 (DE-600)2409270-8 22249028 nnns volume:36 year:2019 number:3 pages:81–90-81–90 https://doi.org/10.29037/ajstd.581 kostenfrei https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c kostenfrei https://ajstd.org/index.php/ajstd/article/view/581 kostenfrei https://doaj.org/toc/0217-5460 Journal toc kostenfrei https://doaj.org/toc/2224-9028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2019 3 81–90-81–90
allfieldsSound	10.29037/ajstd.581 doi (DE-627)DOAJ061382868 (DE-599)DOAJ5b0b1675ee7f4c0597f1e8b6eeeb727c DE-627 ger DE-627 rakwb eng T1-995 Q1-390 Xiang-Yu Huang verfasserin aut SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General) Dale Barker verfasserin aut Stuart Webster verfasserin aut Anurag Dipankar verfasserin aut Adrian Lock verfasserin aut Marion Mittermaier verfasserin aut Xiangming Sun verfasserin aut Rachel North verfasserin aut Rob Darvell verfasserin aut Douglas Boyd verfasserin aut Jeff Lo verfasserin aut Jianyu Liu verfasserin aut Bruce Macpherson verfasserin aut Peter Heng verfasserin aut Adam Maycock verfasserin aut Laura Pitcher verfasserin aut Robert Tubbs verfasserin aut Martin McMillan verfasserin aut Sijin Zhang verfasserin aut Susanna Hagelin verfasserin aut Aurore Porson verfasserin aut Guiting Song verfasserin aut Becky Beckett verfasserin aut Wee Kiong Cheong verfasserin aut Allison Semple verfasserin aut Chris Gordon verfasserin aut In ASEAN Journal on Science and Technology for Development Universiti Brunei Darussalam, 2018 36(2019), 3, Seite 81–90-81–90 (DE-627)558389791 (DE-600)2409270-8 22249028 nnns volume:36 year:2019 number:3 pages:81–90-81–90 https://doi.org/10.29037/ajstd.581 kostenfrei https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c kostenfrei https://ajstd.org/index.php/ajstd/article/view/581 kostenfrei https://doaj.org/toc/0217-5460 Journal toc kostenfrei https://doaj.org/toc/2224-9028 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 36 2019 3 81–90-81–90
language	English
source	In ASEAN Journal on Science and Technology for Development 36(2019), 3, Seite 81–90-81–90 volume:36 year:2019 number:3 pages:81–90-81–90
sourceStr	In ASEAN Journal on Science and Technology for Development 36(2019), 3, Seite 81–90-81–90 volume:36 year:2019 number:3 pages:81–90-81–90
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	convective-scale weather modelling atmospheric data assimilation ensemble forecasts numerical weather prediction systems deep tropics Technology (General) Science (General)
isfreeaccess_bool	true
container_title	ASEAN Journal on Science and Technology for Development
authorswithroles_txt_mv	Xiang-Yu Huang @@aut@@ Dale Barker @@aut@@ Stuart Webster @@aut@@ Anurag Dipankar @@aut@@ Adrian Lock @@aut@@ Marion Mittermaier @@aut@@ Xiangming Sun @@aut@@ Rachel North @@aut@@ Rob Darvell @@aut@@ Douglas Boyd @@aut@@ Jeff Lo @@aut@@ Jianyu Liu @@aut@@ Bruce Macpherson @@aut@@ Peter Heng @@aut@@ Adam Maycock @@aut@@ Laura Pitcher @@aut@@ Robert Tubbs @@aut@@ Martin McMillan @@aut@@ Sijin Zhang @@aut@@ Susanna Hagelin @@aut@@ Aurore Porson @@aut@@ Guiting Song @@aut@@ Becky Beckett @@aut@@ Wee Kiong Cheong @@aut@@ Allison Semple @@aut@@ Chris Gordon @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	558389791
id	DOAJ061382868
language_de	englisch
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callnumber-first	T - Technology
author	Xiang-Yu Huang
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title	SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore
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author_browse	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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title_auth	SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore
url	https://doi.org/10.29037/ajstd.581 https://doaj.org/article/5b0b1675ee7f4c0597f1e8b6eeeb727c https://ajstd.org/index.php/ajstd/article/view/581 https://doaj.org/toc/0217-5460 https://doaj.org/toc/2224-9028
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