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

Abstract

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 planetary-scale 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 Boreal summer intraseasonal oscillation (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.

Forecasting extreme precipitation in the central Mediterranean: Changes in predictors' strength with prediction lead time

“…The methodology can be implemented to other regions in the Mediterranean and the world, taking into consideration relevant localized and large‐scale drivers, and analysing their connection with EPEs and their predictability. This is further supported by other studies around the globe (with various climatic conditions) that use similar concepts and present positive results (Howard et al, 2022; Mastrantonas et al, 2022; Neal et al, 2020; D. Richardson, Neal, et al, 2020). The fact that all datasets and scripts used in this work are freely available online (check data availability section) can contribute to such future efforts.…”

“…Ongoing research investigates the option of using predictors other than direct model precipitation output to inform about precipitation state and EPEs. Such studies, for example, make use of relative humidity (Reggiani & Weerts, 2008), atmospheric rivers (in the United States of America: Lavers, Waliser, et al, 2016 and Europe: Lavers et al, 2018; Lavers, Pappenberger, et al, 2016), or large‐scale atmospheric variability (over the United Kingdom: D. Richardson, Fowler, et al, 2020; D. Richardson, Neal, et al, 2020, India: Neal et al, 2020, Europe: Gvoždíková & Müller, 2021; Krouma et al, 2022, or southeast Asia: Howard et al, 2022). Connections between sea surface temperature and continental precipitation have also been studied by Rieger et al (2021).…”

Weather patterns in Southeast Asia: Enhancing high‐impact weather subseasonal forecast skill

“…No new pattern definitions were implemented for the GS5 hindcasts. Instead, for each hindcast date and ensemble member, an assignment of the atmospheric state to a pattern in the flat, tier-1 and tier-2 categories is done by finding the minimum Euclidean distance between the 850-hPa 0000 UTC zonal and meridional simulated fields and the patterns or "centroids" obtained from the clustering analysis of ERA5 data, as calculated in H21 (Howard et al, 2021). The full expression of the Euclidean distance to be minimised is…”

“…In a companion article, Howard et al . (2021). (referred to as H21 hereafter) introduced a novel technique for the identification of WPs over Southeast Asia based on clustering lower tropospheric horizontal wind data.…”

The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather

“…The weather pattern approach explored here assumes that the predictability of the large‐scale circulation is better than the predictability of rainfall at a specific location within the medium range. This is an approach explored in previous studies for other parts of the world (Richardson, Neal, et al, 2020; Richardson, Fowler, et al, 2020 for the United Kingdom; Mastrantonas, Herrera‐Lormendez, et al, 2021; Mastrantonas, Magnusson, et al, 2021 for the Mediterranean and Howard et al, 2021 for Southeast Asia). This predictability assumption allows a high confidence forecast for a particular weather pattern to be used to infer rainfall, based on the climatological characteristics of that particular circulation type.…”