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Hybrid dynamical-statistical forecasts of the risk of rainfall in South East Asia dependent on equatorial waves

Ferrett, S. ORCID:, Methven, J. ORCID:, Woolnough, S. J. ORCID:, Yang, G.-Y. ORCID:, Holloway, C. E. ORCID: and Wolf, G. (2023) Hybrid dynamical-statistical forecasts of the risk of rainfall in South East Asia dependent on equatorial waves. Monthly Weather Review, 151 (8). pp. 2139-2152. ISSN 1520-0493

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To link to this item DOI: 10.1175/MWR-D-22-0300.1


Equatorial waves are a major driver of widespread convection in South East Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between two and four times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast (MOGREPS-G). Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward moving mixed Rossby-gravity waves are substantially more skilful than for the eastward moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical-statistical forecasts of rainfall probability using a Bayesian approach. The Brier Skill Score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high impact weather using this method as forecasts of large-scale waves improve.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:111329
Publisher:American Meteorological Society


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