JOURNAL ARTICLE
Atmospheric water vapour transport in ACCESS‐S2 and the potential for enhancing skill of subseasonal forecasts of precipitation.
Published In: Quarterly Journal of the Royal Meteorological Society, 2024, v. 150, n. 758. P. 68 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Reid, Kimberley J.; Hudson, Debra; King, Andrew D.; Lane, Todd P.; Marshall, Andrew G. 3 of 3
Abstract
Extended warning of above‐average and extreme precipitation is valuable to a wide range of stakeholders. However, the sporadic nature of precipitation makes it difficult to forecast skilfully beyond one week. Subseasonal forecasting is a growing area of science that aims to predict average weather conditions multiple weeks in advance using dynamical models. Building on recent work in this area, we test the hypothesis that using large‐scale horizontal moisture transport as a predictor for precipitation may increase the forecast skill of the above‐median and high‐precipitation weeks on subseasonal time‐scales. We analysed retrospective forecast (hindcast) sets from the Australian Bureau of Meteorology's latest operational subseasonal‐to‐seasonal forecasting model, ACCESS‐S2, to compare the forecast skill of precipitation using integrated water vapour transport (IVT) as a proxy, compared to using precipitation forecasts directly. We show that ACCESS‐S2 precipitation generally produces more skilful forecasts, except over some regions where IVT could be a useful additional diagnostic for warning of heavy precipitation events. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Quarterly Journal of the Royal Meteorological Society. 2024/01, Vol. 150, Issue 758, p68
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2024
- ISSN:0035-9009
- DOI:10.1002/qj.4585
- Accession Number:175256348
- Copyright Statement:Copyright of Quarterly Journal of the Royal Meteorological Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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