2023
DOI: 10.3389/fclim.2022.1031226
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Sub-seasonal to seasonal drivers of dry extreme rainfall events over Northeast Thailand

Abstract: The interannual El Niño-Southern Oscillation (ENSO) and the Boreal Summer Intraseasonal Oscillation (BSISO) strongly modulate sub-seasonal to seasonal rainfall variability, leading to dry extreme rainfall events (DEREs) over Northeast (NE) Thailand. In this study, the ability of climate models to simulate the ENSO-BSISO-induced DEREs and associated synoptic features are evaluated using self-organizing maps. Observed DEREs occur most frequently during ENSO Neutral and La Niña conditions, when enhanced convectio… Show more

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Cited by 4 publications
(3 citation statements)
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“…(2016) and Abatan et al. (2023). However, such analysis is beyond the scope of the present study, but it is be a focus of our future studies.…”
Section: Resultsmentioning
confidence: 95%
See 1 more Smart Citation
“…(2016) and Abatan et al. (2023). However, such analysis is beyond the scope of the present study, but it is be a focus of our future studies.…”
Section: Resultsmentioning
confidence: 95%
“…This makes it difficult to isolate the atmospheric conditions which induce extremes over the region. Such analysis would require application of Self-Organizing Maps to first group areas that experience extreme events at the same time and analyze the atmospheric feature responsible for each group as done in Abiodun et al (2016) and Abatan et al (2023). However, such analysis is beyond the scope of the present study, but it is be a focus of our future studies.…”
Section: Table 2 Reference Data Applied In the Present Studymentioning
confidence: 99%
“…Several studies have demostrated the use of SOM in grouping climate variables (e.g. Abiodun et al, 2020;Abatan et al, 2023). In this study, the SOM was used to classify the projected spatial patterns of four extreme rainfall indices, the rainfall total (RTOT), the number of wet days (WDAYS), the extreme rainfall threshold (R97.5p), and the rainfall frequency (R97.5pFREQ) at four GWLs into 12 groups (i.e., a 4 × 3 nodes).…”
Section: Testing the Robustness Of Projected Changes In Spatial Patternsmentioning
confidence: 99%