The benefits of ensemble prediction for forecasting an extreme event: The Queensland Floods of February 2019
| العنوان: | The benefits of ensemble prediction for forecasting an extreme event: The Queensland Floods of February 2019 |
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| المؤلفون: | Warren Tennant, Sean Milton, José M. Rodríguez, Tim Cowan, Stuart Webster, Sally L. Lavender, Matt Hawcroft, Dan Copsey |
| المصدر: | Monthly Weather Review. |
| بيانات النشر: | American Meteorological Society, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Atmospheric Science, 010504 meteorology & atmospheric sciences, Ensemble forecasting, Event (relativity), 0208 environmental biotechnology, 02 engineering and technology, Numerical weather prediction, 01 natural sciences, 020801 environmental engineering, Ensemble prediction, Climatology, Wind chill, Environmental science, Precipitation, Duration (project management), Lead time, 0105 earth and related environmental sciences |
| الوصف: | From late January to early February 2019, a quasi-stationary monsoon depression situated over northeast Australia caused devastating floods. During the first week of February, when the event had its greatest impact in northwest Queensland, record-breaking precipitation accumulations were observed in several locations, accompanied by strong winds, substantial cold maximum temperature anomalies and related wind chill. In spite of the extreme nature of the event, the monthly rainfall outlook for February issued by Australia’s Bureau of Meteorology on 31st January provided no indication of the event. In this study, we evaluate the dynamics of the event and assess how predictable it was across a suite of ensemble model forecasts using the UK Met Office numerical weather prediction (NWP) system, focussing on a one week lead time. In doing so, we demonstrate the skill of the NWP system in predicting the possibility of such an extreme event occurring. We further evaluate the benefits derived from running the ensemble prediction system at higher resolution than used operationally at the Met Office and with a fully coupled dynamical ocean. We show that the primary forecast errors are generated locally, with key sources of these errors including atmosphere-ocean coupling and a known bias associated with the behaviour of the convection scheme around the coast. We note that a relatively low resolution ensemble approach requires limited computing resource, yet has the capacity in this event to provide useful information to decision makers with over aweek’s notice, beyond the duration of many operational deterministic forecasts. |
| تدمد: | 1520-0493 0027-0644 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::ff600a642eb4fb32e65217b8dd18cee1 https://doi.org/10.1175/mwr-d-20-0330.1 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........ff600a642eb4fb32e65217b8dd18cee1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15200493 00270644 |
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