TY - JOUR
T1 - Recommendations for improved tropical cyclone formation and position probabilistic Forecast products
AU - Dunion, Jason P.
AU - Davis, Chris
AU - Titley, Helen
AU - Greatrex, Helen
AU - Yamaguchi, Munehiko
AU - Methven, John
AU - Ashrit, Raghavendra
AU - Wang, Zhuo
AU - Yu, Hui
AU - Fontan, Anne Claire
AU - Brammer, Alan
AU - Kucas, Matthew
AU - Ford, Matthew
AU - Papin, Philippe
AU - Prates, Fernando
AU - Mooney, Carla
AU - Kruczkiewicz, Andrew
AU - Chakraborty, Paromita
AU - Burton, Andrew
AU - DeMaria, Mark
AU - Torn, Ryan
AU - Vigh, Jonathan L.
N1 - The authors thank the participants of the June 2021 WMO Tropical Cyclone-Probabilistic Forecast Products Workshop for their valuable insights and contributions to this manuscript. We also thank the participants of the 2018 IWTC-9 workshop in Honolulu, HI for their insightful recommendations regarding probabilistic forecasting of tropical cyclones, which motivated the formation of the TC-PFP project. TC-PFP was developed in collaboration with the WMO Research Board (through the WWRP), its Commission for Observation, Infrastructure, and Information Systems (InfCom) and its Commission for Weather, Climate, Water and Related Environmental Services and Applications (SerCom) (through the TC RSMCs). TC-PFP is the first-ever pilot project under the Global Data Processing and Forecast System (GDPFS) of WMO and we wish to thank the WMO Standing Committee on Data Processing for Applied Earth System Modelling and Prediction (SC-ESMP) for their support. This material is based, in part, upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. We also wish to thank two anonymous TCRR reviewers for their insightful suggestions and recommendations.
PY - 2023/12
Y1 - 2023/12
N2 - Prediction of the potentially devastating impact of landfalling tropical cyclones (TCs) relies substantially on numerical prediction systems. Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios, it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings. RSMCs, TCWCs, and other forecast centers value probabilistic guidance for TCs, but the International Workshop on Tropical Cyclones (IWTC-9) found that the “pull-through” of probabilistic information to operational warnings using those forecasts is slow. IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products (TC-PFP) project, which is also endorsed as a WMO Seamless GDPFS Pilot Project. The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts. TC-PFP is being implemented in 3 phases: Phase 1 (TC formation and position); Phase 2 (TC intensity and structure); and Phase 3 (TC related rainfall and storm surge). This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position. There is considerable variability in the nature and interpretation of forecast products based on ensemble information, making it challenging to transfer knowledge of best practices across forecast centers. Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices. Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts. Finally, forecast centers need timely access to ensemble information that has consistent, user-friendly ensemble information. Greater consistency across forecast centers in data accessibility, probabilistic forecast products, and warnings and their communication to users will produce more reliable information and support improved outcomes.
AB - Prediction of the potentially devastating impact of landfalling tropical cyclones (TCs) relies substantially on numerical prediction systems. Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios, it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings. RSMCs, TCWCs, and other forecast centers value probabilistic guidance for TCs, but the International Workshop on Tropical Cyclones (IWTC-9) found that the “pull-through” of probabilistic information to operational warnings using those forecasts is slow. IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products (TC-PFP) project, which is also endorsed as a WMO Seamless GDPFS Pilot Project. The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts. TC-PFP is being implemented in 3 phases: Phase 1 (TC formation and position); Phase 2 (TC intensity and structure); and Phase 3 (TC related rainfall and storm surge). This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position. There is considerable variability in the nature and interpretation of forecast products based on ensemble information, making it challenging to transfer knowledge of best practices across forecast centers. Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices. Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts. Finally, forecast centers need timely access to ensemble information that has consistent, user-friendly ensemble information. Greater consistency across forecast centers in data accessibility, probabilistic forecast products, and warnings and their communication to users will produce more reliable information and support improved outcomes.
KW - Formation
KW - Position
KW - Probabilistic
KW - Tropical cyclone
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U2 - 10.1016/j.tcrr.2023.11.003
DO - 10.1016/j.tcrr.2023.11.003
M3 - Article
AN - SCOPUS:85182353867
SN - 2225-6032
VL - 12
SP - 241
EP - 258
JO - Tropical Cyclone Research and Review
JF - Tropical Cyclone Research and Review
IS - 4
ER -