Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events

Autor: Moron, Vincent, Acharya, Nachiketa, Hassan, Quamrul Sm
Přispěvatelé: Aix-Marseille Université - Faculté des Arts, Lettres, Langues et Sciences Humaines (AMU ALLSH), Aix Marseille Université (AMU), University of Colorado [Boulder], National Oceanic and Atmospheric Administration (NOAA), Bangladesh Meteorological Department
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Climatology
International Journal of Climatology, In press, 43 (2), pp.850-873. ⟨10.1002/joc.7835⟩
ISSN: 1097-0088
0899-8418
Popis: International audience; We explore the characteristics of 96190 wet events (WE) defined as consecutive 3-hourly rainfall >= 1 mm/3h from a network of 34 stations across Bangladesh. Nearly 60% (5%) of wet events last = 15) hours. The WEs are dynamically clustered into four "canonical" storm types (ST), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. ~ 60% of WEs are associated with ST#1, that is short and small WEs and very low rainfall amounts (usually < 10 mm), ~15% of WEs are associated with either (ST#2) short/small WEs but with intense rainfall, probably mostly related to scattered thunderstorms, or (ST#3) longer/larger WEs but with less intense rainfall. The last ST (ST#4) is rare (~ 6%), related to very long durations and large wet areas, and includes the wettest WEs. It is especially frequent over southeastern Bangladesh. ST#2 to #4 contribute almost equally to the local-scale total amount of rainfall (27-29% each in mean) while ST#1, despite its individual low rainfall amount, still includes ~ 15% of it. ST#2 (ST#4) is related to the highest probability of occurrence of 3-hourly (daily) extremes. ST#4 occurrence is the most impacted by synoptic Indian lows/depressions as well as the main modes of intraseasonal variation, while ST#1 and #2 are also significantly impacted by intraseasonal modes but in reverse manner than ST#4.
Databáze: OpenAIRE
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