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Sea-Surface Temperature Based Statis...

Sengupta, Agniv .

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Sea-Surface Temperature Based Statistical Prediction of the South Asian Summer Monsoon Rainfall Distribution.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sea-Surface Temperature Based Statistical Prediction of the South Asian Summer Monsoon Rainfall Distribution./
作者:	Sengupta, Agniv .
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	186 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Atmospheric sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27667694
ISBN:	9798662446694

Sea-Surface Temperature Based Statistical Prediction of the South Asian Summer Monsoon Rainfall Distribution.
Sengupta, Agniv .

Sea-Surface Temperature Based Statistical Prediction of the South Asian Summer Monsoon Rainfall Distribution. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 186 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2020.

This item is not available from ProQuest Dissertations & Theses.

The South Asian summer monsoon brings copious amounts of rainfall accounting for over 70% of the annual rainfall over India. Summer monsoon predictions have drawn considerable public/policy attention lately as South Asia has become a resource-stressed and densely populated region. This environmental backdrop and the livelihood concerns of a billion-plus people generate the demand for more accurate monsoon predictions. The prediction skill, however, has remained marginal and stagnant for several decades despite advances in the representation of physical processes, numerical model resolution, and data assimilation techniques, leading to the following key question: what is the potential predictability of summer monsoon rainfall at lead times of one month to a season?This dissertation examines the role of influential climate system components with large thermal inertia and reliable long-term observational records, like sea-surface temperature (SST) in forecasting the seasonal distribution of South Asian monsoon rainfall. First, an evolution-centric SST analysis is conducted in the global oceans using the extended-Empirical Orthogonal Function technique to uncover the recurrent modes of spatiotemporal variability and their potential inter-basin linkages. A statistical forecast model is next developed using these extracted modes of SST variability as predictors. Assessment of the forecasting system's long-term performance from reconstruction and hindcasting over an independent verification period demonstrates high forecast skill over core monsoon regions-the Indo-Gangetic Plain and southern peninsular India, indicating prospects for improved seasonal predictions. The influence of SSTs on the northeast winter monsoon is subsequently investigated, especially, its evolution, interannual variability and the El Nino-Southern Oscillation (ENSO) influence. Key findings from this study include evidence of increased rainfall over southeastern peninsular India and Sri Lanka (generated by an off-equatorial anticyclonic circulation centered over the Bay of Bengal) during El Nino winters.This dissertation provides the first quantitative assessment of the potential predictability of summer monsoon rainfall anomalies-the maximum predictable summer rainfall signal (amount, distribution) over South Asia from prior SST information-at various seasonal leads, and notably, at SST-mode resolution. The improved skill of the SST-based statistical forecast establishes the bar-an evaluative benchmark-for the dynamical prediction of summer monsoon rainfall.

ISBN: 9798662446694Subjects--Topical Terms:

3168354
Atmospheric sciences.
Subjects--Index Terms:

Data mining

Sea-Surface Temperature Based Statistical Prediction of the South Asian Summer Monsoon Rainfall Distribution.
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500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Nigam, Sumant.
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520 $a The South Asian summer monsoon brings copious amounts of rainfall accounting for over 70% of the annual rainfall over India. Summer monsoon predictions have drawn considerable public/policy attention lately as South Asia has become a resource-stressed and densely populated region. This environmental backdrop and the livelihood concerns of a billion-plus people generate the demand for more accurate monsoon predictions. The prediction skill, however, has remained marginal and stagnant for several decades despite advances in the representation of physical processes, numerical model resolution, and data assimilation techniques, leading to the following key question: what is the potential predictability of summer monsoon rainfall at lead times of one month to a season?This dissertation examines the role of influential climate system components with large thermal inertia and reliable long-term observational records, like sea-surface temperature (SST) in forecasting the seasonal distribution of South Asian monsoon rainfall. First, an evolution-centric SST analysis is conducted in the global oceans using the extended-Empirical Orthogonal Function technique to uncover the recurrent modes of spatiotemporal variability and their potential inter-basin linkages. A statistical forecast model is next developed using these extracted modes of SST variability as predictors. Assessment of the forecasting system's long-term performance from reconstruction and hindcasting over an independent verification period demonstrates high forecast skill over core monsoon regions-the Indo-Gangetic Plain and southern peninsular India, indicating prospects for improved seasonal predictions. The influence of SSTs on the northeast winter monsoon is subsequently investigated, especially, its evolution, interannual variability and the El Nino-Southern Oscillation (ENSO) influence. Key findings from this study include evidence of increased rainfall over southeastern peninsular India and Sri Lanka (generated by an off-equatorial anticyclonic circulation centered over the Bay of Bengal) during El Nino winters.This dissertation provides the first quantitative assessment of the potential predictability of summer monsoon rainfall anomalies-the maximum predictable summer rainfall signal (amount, distribution) over South Asia from prior SST information-at various seasonal leads, and notably, at SST-mode resolution. The improved skill of the SST-based statistical forecast establishes the bar-an evaluative benchmark-for the dynamical prediction of summer monsoon rainfall.
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