Mathematical Strategies for Climate and Long Range Weather Forecasting in Hierarchy of Models
暫譯: 氣候與長期天氣預測的數學策略:模型層級中的應用
Majda, Andrew
- 出版商: Springer
- 出版日期: 2019-05-15
- 售價: $4,070
- 貴賓價: 9.5 折 $3,867
- 語言: 英文
- 頁數: 300
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3319223267
- ISBN-13: 9783319223261
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商品描述
This book gives a research exposition of interdisciplinary topics at the cutting edge of the applied mathematics of climate change and long range weather forecasting through a hierarchy of models with contemporary applications to grand challenges such as intraseasonal weather prediction. The developments include recent physics constrained low-order models, new analog prediction models, and equation free methods to capture intermittency and low frequency variabilities in massive datasets through Nonlinear Laplacian Spectral Analysis (NLSA) which combines delayed embeddings, causal constraints, and machine learning. Applications to grand challenges such as tropical intraseasonal variability of the Madden-Julian Oscillation (MJO) and the Monsoon as well as sea ice re-emergence in the Arctic on yearly time scales. A highlight is the exposition and pedagogical development of recent intermediate stochastic skeleton models to capture the main features of the MJO through PDE ideas, stochastics, and physical reasoning and compared with observational data. The mathematical theory of model error and the use of information theory combined with linear statistical response theory in a calibration stage are applied to improve long range forecasting and multi-scale data assimilation with concrete examples.
商品描述(中文翻譯)
本書對於氣候變遷應用數學的跨學科主題進行了研究性闡述,探討了透過一系列模型來應對當前挑戰的最新應用,例如季內天氣預測。這些發展包括最近的物理約束低階模型、新的類比預測模型,以及無方程方法,透過非線性拉普拉斯譜分析(Nonlinear Laplacian Spectral Analysis, NLSA)來捕捉大量數據集中的間歇性和低頻變異性,該方法結合了延遲嵌入、因果約束和機器學習。應用於重大挑戰,例如馬登-朱利安振盪(Madden-Julian Oscillation, MJO)和季風的熱帶季內變異性,以及北極海冰的再出現,這些都在年度時間尺度上進行了研究。本書的一個亮點是對最近的中介隨機骨架模型的闡述和教學發展,這些模型通過偏微分方程(PDE)思想、隨機性和物理推理來捕捉MJO的主要特徵,並與觀測數據進行比較。模型誤差的數學理論以及信息理論與線性統計響應理論的結合在校準階段中被應用,以改善長期預測和多尺度數據同化,並提供具體範例。
作者簡介
作者簡介(中文翻譯)
安德魯·J·馬伊達(Andrew J. Majda)是紐約大學庫朗數學研究所的藝術與科學摩爾斯教授。