상세정보
Export to Refworks부가기능
Dynamics-informed Machine Learning Approaches for El Nino Prediction and Its Teleconnection to Antarctica
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Dynamics-informed Machine Learning Approaches for El Nino Prediction and Its Teleconnection to Antarctica. |
| 개인저자 | Wang, Xinyang. |
| 단체저자명 | New York University. Mathematics. |
| 발행사항 | [S.l.]: New York University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 144 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781392565964 |
| 학위논문주기 | Thesis (Ph.D.)--New York University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Includes supplementary digital materials. Advisor: Giannakis, Dimitrios. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be sold to any third party vendors.This item must not be added to any third party search indexes.This item must not be added to any third party search indexes. |
| 요약 | Forecasting the El Nino-Southern Oscillation (ENSO) has been a subject of vigorous research due to the important role of the phenomenon in climate dynamics and its worldwide socioeconomic impacts. Over the past decades, numerous models for ENSO prediction have been developed, among which statistical models approximating ENSO evolution by linear dynamics have received significant attention owing to their simplicity and comparable forecast skill to first-principles models at short lead times. Yet, due to highly nonlinear and chaotic dynamics (particularly during ENSO initiation), such linear models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, we employ a recently introduced data analysis technique called nonlinear Laplacian spectral analysis (NLSA) and a new nonparametric prediction approach called kernel analog forecasting (KAF). These approaches use nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets, and have rigorous connections with the spectral theory of dynamical systems. In particular, using the multiscale modes recovered by NLSA, KAF yields statistically optimal predictions of future ENSO states as conditional expectations without assumptions on the underlying dynamics. In Chapter 3, KAF is applied to ENSO prediction using modelled and observed sea surface temperature (SST) data in the Indo-Pacific region. In the observation case, KAF successfully predicts the Nino~3.4 index up to a 13-month lead, which corresponds to an increase of 6 months over a benchmark linear inverse model (LIM), while significantly improving upon the ENSO predictability ``spring barrier''. When applied to 1300-yr model data, the enhanced predictability afforded by KAF holds over potentially much longer leads, extending to 24 months versus 11 months in the benchmark LIM. Additionally, in Chapter 4, a strong connection between ENSO and Antarctic interannual variability has been found by the multiscale modes recovered from NLSA, providing a new approach to study the complex climate system around Antarctica which is still not well simulated by general circulation models. Motivated by this, we investigate regional forecasts of Antarctic sea ice via KAF in Chapter 5. Although Antarctic sea ice variability is much less predictable than ENSO, KAF shows improved performance over the benchmark persistence forecast. |
| 일반주제명 | Applied mathematics. Atmospheric sciences. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
