Editorial Type:
Article
Article Type:
Research Article

The First Operational Version of Taiwan Central Weather Bureau’s One-Tier Global Atmosphere–Ocean Coupled Forecast System for Seasonal Prediction

Hann-Ming Henry Juang bEnvironmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland
dDepartment of Atmospheric Sciences, National Central University, Taoyuan, Taiwan

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Tzu-Yu Wu dDepartment of Atmospheric Sciences, National Central University, Taoyuan, Taiwan

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Pang-Yen Brian Liu aMeteorology Research and Development Center, Central Weather Bureau, Taipei, Taiwan

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Hsin-Yi Lin dDepartment of Atmospheric Sciences, National Central University, Taoyuan, Taiwan

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Ching-Teng Lee aMeteorology Research and Development Center, Central Weather Bureau, Taipei, Taiwan

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Mien-Tze Kueh fResearch Center for Environmental Changes, Academia Sinica, Taipei, Taiwan

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Jia-Fong Fan dDepartment of Atmospheric Sciences, National Central University, Taoyuan, Taiwan

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Jen-Her River Chen cMeteorological Information Center, Central Weather Bureau, Taipei, Taiwan

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Mong-Ming Lu eDepartment of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

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Pay-Liam Lin dDepartment of Atmospheric Sciences, National Central University, Taoyuan, Taiwan

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Online Publication:
15 May 2024
Print Publication:
01 May 2024
DOI:
https://doi.org/10.1175/WAF-D-22-0128.1
Page(s):
745–763
Restricted access

Abstract

The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hann-Ming Henry Juang, henry.juang@g.ncu.edu.tw

Abstract

The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hann-Ming Henry Juang, henry.juang@g.ncu.edu.tw
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