Accessibility navigation


Weather patterns in Southeast Asia: relationship with tropical variability and heavy precipitation

Howard, E., Thomas, S., Frame, T. H. A., Gonzalez, P. L. M. ORCID: https://orcid.org/0000-0003-0154-0087, Methven, J. ORCID: https://orcid.org/0000-0002-7636-6872, Martinez-Alvarado, O. ORCID: https://orcid.org/0000-0002-5285-0379 and Woolnough, S. J. ORCID: https://orcid.org/0000-0003-0500-8514 (2022) Weather patterns in Southeast Asia: relationship with tropical variability and heavy precipitation. Quarterly Journal of the Royal Meteorological Society, 148 (743). pp. 747-769. ISSN 1477-870X

[img] Text - Accepted Version
· Restricted to Repository staff only until 24 December 2022.

4MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/qj.4227

Abstract/Summary

Two sets of weather patterns describing variability in 850 hPa winds in Southeast Asia are presented and compared. Patterns are calculated using EOF/k-means clustering with and without imposing a separation between planetaryscale and regional-scale circulation features. The former are labelled as tiered patterns while the latter are referred to as flat. The ability of the patterns to distinguish between known modes of tropical circulation variability is examined. This includes climate modes such as the seasonal monsoons, the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as sub-seasonal modes including cold surges, phases of the MJO and BSISO, tropical cyclones, Borneo Vortices and equatorial waves. All these modes are well captured by the weather patterns except for the equatorial waves and the IOD. The tiered patterns are shown to better describe large-scale modes of variability, while the flat patterns better describe the synoptic variability. Both sets of weather patterns are then used to study the likelihood of heavy precipitation depending on synoptic circulation by considering the regime-conditioned probability of high-percentile precipitation using the satellite-derived Global Precipitation Measurement (GPM) dataset. It is shown that the pattern centroids explain up to 10% of the seasonally anomalous precipitation over land, and that a perfect weather pattern forecast would outperform a perfect MJO forecast. These weather patterns show promising potential in extending the useful forecast range for the risk of heavy precipitation, dependent on their forecastability.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:101724
Publisher:Royal Meteorological Society

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation